Different families of D* Lite variants.
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Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
\n'}],latestNews:[{slug:"intechopen-partners-with-ehs-for-digital-advertising-representation-20210416",title:"IntechOpen Partners with EHS for Digital Advertising Representation"},{slug:"intechopen-signs-new-contract-with-cepiec-china-for-distribution-of-open-access-books-20210319",title:"IntechOpen Signs New Contract with CEPIEC, China for Distribution of Open Access Books"},{slug:"150-million-downloads-and-counting-20210316",title:"150 Million Downloads and Counting"},{slug:"intechopen-secures-indefinite-content-preservation-with-clockss-20210309",title:"IntechOpen Secures Indefinite Content Preservation with CLOCKSS"},{slug:"intechopen-expands-to-all-global-amazon-channels-with-full-catalog-of-books-20210308",title:"IntechOpen Expands to All Global Amazon Channels with Full Catalog of Books"},{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"}]},book:{item:{type:"book",id:"3596",leadTitle:null,fullTitle:"Multiprocessor Scheduling, Theory and Applications",title:"Multiprocessor Scheduling",subtitle:"Theory and Applications",reviewType:"peer-reviewed",abstract:"A major goal of the book is to continue a good tradition - to bring together reputable researchers from different countries in order to provide a comprehensive coverage of advanced and modern topics in scheduling not yet reflected by other books. The virtual consortium of the authors has been created by using electronic exchanges; it comprises 50 authors from 18 different countries who have submitted 23 contributions to this collective product. In this sense, the volume can be added to a bookshelf with similar collective publications in scheduling, started by Coffman (1976) and successfully continued by Chretienne et al. (1995), Gutin and Punnen (2002), and Leung (2004).\r\nThis volume contains four major parts that cover the following directions: the state of the art in theory and algorithms for classical and non-standard scheduling problems; new exact optimization algorithms, approximation algorithms with performance guarantees, heuristics and metaheuristics; novel models and approaches to scheduling; and, last but least, several real-life applications and case studies.",isbn:null,printIsbn:"978-3-902613-02-8",pdfIsbn:"978-953-51-5819-6",doi:"10.5772/52",price:139,priceEur:155,priceUsd:179,slug:"multiprocessor_scheduling_theory_and_applications",numberOfPages:438,isOpenForSubmission:!1,isInWos:1,hash:null,bookSignature:"Eugene Levner",publishedDate:"December 1st 2007",coverURL:"https://cdn.intechopen.com/books/images_new/3596.jpg",numberOfDownloads:84264,numberOfWosCitations:59,numberOfCrossrefCitations:40,numberOfDimensionsCitations:91,hasAltmetrics:0,numberOfTotalCitations:190,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:null,dateEndSecondStepPublish:null,dateEndThirdStepPublish:null,dateEndFourthStepPublish:null,dateEndFifthStepPublish:null,currentStepOfPublishingProcess:1,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,editors:[{id:"25544",title:"Prof.",name:"Eugene",middleName:null,surname:"Levner",slug:"eugene-levner",fullName:"Eugene Levner",profilePictureURL:"https://mts.intechopen.com/storage/users/25544/images/system/25544.jpg",biography:"Eugene Levner received the B.S.+M.S. degree in computational mathematics from Lomonosov State University, Moscow, USSR, and the Ph.D. degree in computer & systems sciences from the Central Economic-Mathematical Institute of the Soviet Academy of Sciences, Moscow, in 1968 and 1973, respectively. From 1972 to 1990, he worked for the Soviet Academy of Sciences, Moscow, He joined the Department of Computer Science, Holon Institute of Technology, Holon, Israel, in 1994, and is currently a Full Professor of Computer Science.\n\nIn 1991-2009, he was a Visiting Scholar at the Eindhoven Technological University, the Netherlands (1991), Hebrew University of Jerusalem, Israel (1991-1992), Japan Advanced Institute of Science and Technology, Ishikawa, Japan (1997), INRIA, Metz, France (1998), Groningen University, the Netherlands (1999), Osaka University, Japan (2001), National Polytechnic Institute, Mexico (2004), University of La Laguna, Tenerife, Spain (2006-2007), Indian Institute of Technology, Kanpur (2007), and the Rutgers University, USA (2009).\n\nHe has authored/coauthored several books and more than 100 papers in refereed journals and chapters in books. 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Nowadays, as the rapid advances of computational power together with development of state-of-the-art motion planning (MP) algorithms, autonomous robots can now robustly plan optimal path in narrow configuration space or wide dynamic complex environment with high accuracy and low latency. These recent MP developments have a large impact in medical surgery, animation, expedition and many other disciplines. For instance, RRT [1] algorithm was applied for multi-arm surgical robot in [2]. Expedition robot GDRS XUV was implemented field D* any-angle path planner [3] that enables the robot to optimally move in harsh environment. D* [4] is implemented for Mars Rover prototypes and tactical mobile robots in [5]. Bug algorithms were implemented in multi-robot cooperation scenarios [6].
In general, the problem statement of MP can be generalised as follows: Given the initial defined world space and the robot’s configuration space, the MP algorithm must generate a series of consecutive collision-free configurations of the robot that connects start configuration and goal configuration. This series configuration must satisfy any inherent motion or non-motion constraints of the robot.
To cope with a wide range of environment characteristics, MP can be divided into two categories: gross MP and fine MP [7]. The gross MP concerns with the scenarios when world space is much wider than obstacles’ size and positional error of the robot, whereas the fine MP solves the planning problems in narrow space that requires high accuracy.
This manuscript presents the development of gross MP algorithm family, in particular search-based planning and replanning paradigm. The foundation concepts of MP, configuration space representations, and the position of mentioned paradigm in MP big picture is presented in Section 2. Section 3 describes historical basis of search-based algorithm family. Section 4 demonstrates the properties and pitfall of D* Lite, which is one of the most crucial algorithms to plan path in dynamic environment. After that, the variants of D* Lite, which improve D* Lite’s optimality and performance, are presented. To confirm the improvements, we provide experimental results of recent path planning algorithms and their comparisons in terms of performance and optimality in Section 5. Section 6 will discuss about the future development of MP and provide conclusion.
This section will provide an overview of the basic elements that every MP problem must involve. These elements are configuration space of robot and obstacles, environment representation, MP method and search method. The mentioned factors must be analysed consecutively in order to apply suitable MP algorithm family for each scenario.
There still does not exist unified MP algorithm that can robustly solve MP problems in any scenarios such as time optimality, path optimality, moving target, non-holonomic motion, etc. However, with the active recent development of MP, a variety of MP algorithm families are invented to deal with the mentioned scenarios. We will provide detail MP algorithm family classifications based on problem type and therefore demonstrate the location of search-based paradigm in MP.
Figure 1 describes the family tree of MP algorithms based on problem-type classification.
Classification of MP algorithm families based on problem type; the deepest leaves of algorithm tree are representatives for their families.
As can be seen, MP with non-holonomic (velocity and kinodynamic) constraints, which is handled by sampling-based paradigm, is still an open research area due to the hardness of transforming high DOF robot and surroundings into configuration space. This configuration space problem has been proved to be NP hard, and computing configuration space operation has exponential lower bound [7]. Until recently, the mainstream of non-holonomic MP research is developed based on random rapidly exploring random tree planner (RRT). For example, heuristics property of A* [8] has been applied to RRT for faster trajectory convergence [9]. Fast Marching Square method was developed for non-holonomic car-like robot based on RRT that produces smoother trajectory than RRT [10].
Unlike sampling-based paradigm, search-based paradigm, which represents for path planning algorithms, has a long history of evolution, from basic graph searching to dynamic motion planner with constraints. In this paradigm, robot is treated as point or scalar robot that is able to move in any direction at any time interval. Hence, the configuration obstacle space has the same dimension with the environment, and the generated trajectory is just a path in operating environment. Search-based paradigm is divided into time-invariant and time-variant environment categories. A* is the representative for time-invariant algorithm family; its cost function is incorporated with heuristic property for faster optimal path planning. When dealing with time-variant problem, although we can ensure the optimality and correctness of path solution, we cannot just rerun A* from the point that the robot detects changes in environment due to high latency. To efficiently path replanning in dynamic environment, incremental property is combined with heuristic property to develop D* Lite algorithm; this algorithm is the basis for future development of search-based replanning. Many variants of D* Lite for different MP problems are presented in Table 1.
Problem scenarios | Algorithms |
---|---|
Moving target | MTD* Lite [11] |
Fast/suboptimal | Anytime D* [12], truncated D* Lite [13], anytime Truncated D* [14] |
Any-angle movement | Field D* [3], incremental Phi* [15] |
Performance improvement | D* Lite with Reset [16] |
Different families of D* Lite variants.
The development of search-based algorithm family is described detail in Section 3 and Section 4.
The general MP problem can be formulated as the following six terms:
General MP is viewed as a search for path (a series of configuration) in state space W that connects start configuration
This section will describe the transformation of world space to state space. This is the first step to formulate a MP algorithm; it creates an operation environment for MP algorithm and a way to represent physical world information as data structure in computer.
The world space (physical space) is where the robot and obstacles exist; it is a map of the practical world. However, we cannot apply directly MP algorithm to this space due to the hardness of representing orientation dimension and other parameters such as motion constraints on computer. Therefore, a C-space is needed, which incorporates all independent parameters that completely define the position of all points on the robot and specifies global constraints of the robot as Cartesian space. Figure 2 [17] shows a mapping between an effector of 2DOF robot arm and a set of possible two angle parameters that constitutes C-space of the effector.
Configuration space of 2DOF robot arm that represents a set of collision-free angles in white and specific object collided in colours (a) Workspace, (b) C-space.
After computing the C-space, all MP problems are basically reduced to finding a series of configuration that connects start configuration and goal configuration. In other word, the problem is reduced to finding a path for a point robot from start to goal. The number of parameters that defines robot position is the dimension of C-space. The method to compute C-space is mentioned in [7].
For simplicity, to follow the scope of this chapter, we will treat C-space of point robot the same as world space; the reason is that search-based paradigm deals with holonomic MP problem in which the size of robot is neglected compared to operating environment.
After transforming world space to C-space, we still cannot apply search-based algorithms to C-space. The problem is that search-based algorithms like A* or D* Lite work on graph-like structures; hence, applying search-based algorithms on continuous C-space is intractable. However, other MP algorithm families such as sampling based can apply directly to C-space. Unfortunately, the path optimality and performance of sampling-based algorithms are currently worse than state-of-the-art search-based algorithms.
There are two main approaches to discretize C-space into graph-like structure:
Cell decomposition
Roadmap
In cell decomposition approach, we divide C-space into eight-connected square grid environment with arbitrary resolution. Then we colour all cells that intersect with obstacle configuration with black, and other free cells are white. Figure 3 illustrates this approach.
Cell decomposition approach (a) Original Objects, (b) Encoded Objects into cells.
This approximation has limited assumptions on obstacle configuration. Therefore, the approach is used widely in practice. However, there is no concept of path optimality, because we can infinitely divide C-space into smaller squares. It is a trade-off between optimality and computation. Cell decomposition in high dimensions is also expensive; it has exponential growth in PSPACE.
In roadmaps approach, the idea is avoiding scanning the entire C-space by computing an undirected graph with “road” edges that are guaranteed to be collision-free. The main methods of this approach are visibility graph [17] and Voronoi diagrams. The examples of the two methods are demonstrated in Figure 4.
Path topologies of visibility graph and Voronoi diagram methods in roadmap approach (a) Visibility Graph method, (b) Voronoi method.
As can be seen, this approach generates fewer vertices than cell decomposition approach. Visibility graph method tends to generate with vertices that are the vertices of obstacles; this property leads to finding shortest path. However, the visibility graph’s roadmaps are close to obstacles; collision is inevitable due to some movement error. Voronoi diagram solves the problem by generating roadmaps that keep robot as far away as possible from obstacles.
Despite this approach constructs efficiently graph representation for search-based algorithm; it is difficult to compute in higher dimension or non-polygonal environment. The approach also can be unstable in dynamic scenarios; small changes in obstacles can lead to large changes in graph.
In the following sections, we use cell decomposition approach for search-based algorithms due to its clarity to describe the operation of search-based algorithms and its feasibility to apply in practice.
This section demonstrates one of the most well-known algorithms in graph search family: A*. The A* algorithm’s properties are also examined and utilised to use in different cases.
There are three main properties of A* [8] that are inherited from historical graph search algorithms:
where
where
for any successor
Figure 5 illustrates each property of A* when they are applied to search for goal:
Operation demonstration of properties of A* and A* itself (a) Map, (b) Uniform cost search, (c) Greedy Best-First Search and (d) A*.
The total expanded cells in each algorithm constitutes for their performance (e.g. how many cells are processed before path is found). As can be seen, Dijkstra’s algorithm has the worst performance due to lack of guidance to expand search; it just expands uniformly to all directions. Greedy best-first search has the best computation; however, it does not guarantee the shortest path like Dijkstra’s algorithm, because its search is trap in local minima shown in the picture. A* has both computation and optimality advantages over these old algorithms by combining uniform cost search rule to guarantee path optimality and heuristic rule of greedy best-first search to guide search process towards goal. Both rules can be combined and formulated as priority function:
Intuitively, one could think
Pseudo code of A* algorithm.
The pseudo code for A* is shown in Figure 6.
In practice, the performance issue is more critical; time for robot to “think” before making decision is limited. Therefore, a path planner, which has these properties, is essential:
Quickly producing a suboptimal solution and then gradually improving its solution as time allowed by reusing its previous search effort as much as possible
Having control over the suboptimal bound and hence indicating a bound of processing time of each search iteration
We introduce the algorithm that is well-suited for this scenario: ARA* [18].
Basically, ARA* is developed from A*; it inherits all intrinsic properties of A*. The idea to quickly plan suboptimal path is derived from inflated heuristics function [18] by a factor
where
The pseudo code for ARA* is shown in Figure 7.
Pseudo code of ARA* algorithm.
To understand the behaviours of ARA*, we must keep in mind that ARA* violates admissible property—
Hence, the computed path is no longer optimal. Moreover, each search iteration is no longer guaranteed to expand searching each cell at most once like A* due to decreasing
(Figure 7, line 13) that already are expanded once and processes these cells in the next search iteration.
In general, ARA* executes consecutive search iterations with decreasing suboptimal bound; each search does not recalculate consistent cells from previous search. Therefore, the path improvement process is efficient. Theoretical properties of ARA* is described in [18].
In real-world application, there is often a scenario that the robot initially does not know a priori information about its surroundings. We cannot encode the world space information each time the robot runs, because it is expensive, tedious, and infeasible due to rapid changes in practice. To maintain collision-free path, one can naively rerun A* to replan the shortest path from the point that the robot detects changes. However, this naïve approach will waste computation by reprocessing cells that are irrelevant to compute a new path and hence increase idle time between each search. This section will demonstrate search-based algorithms to solve mentioned problem in time-variant environment.
In goal-directed navigation task, with cell decomposition approximation, the robot always observes a limited range of eight connected grids. The robot is able to move in eight directions with cost one, and it assumes that unknown cells are traversable. The robot follows the initial calculated path to goal and encounters blockage cells; it must be able to process only cells that are relevant to compute the new path. The challenge is to find these relevant cells. Figure 8 illustrates this idea.
MP simulation on grid environment (a) Initial path, (b) Reset A* and (c) D* Lite.
Note that grey cells (in Figure 8) are expanded cells to compute initial path or new path when robot detects blockage cell in purple at position yellow cell. Darker grey cells are processed multiple times. As can be seen, total expanded cells in replanning process of D* Lite is 61, whereas expanded cells of rerunning A* are 75.
D* Lite [19] is developed directly from Lifelong Planning A* (LPA*) [20] for applying on mobile robot, which is a combination of Dynamic SWSF-FP [21] and A* [8]. Therefore, D* Lite possesses these properties:
Reverse search: Unlike A*, D* Lite expands its search from goal;
Heuristics: D* Lite inherits this property from A* with admissible rule. Thus, D* Lite maintains path optimality by expanding heuristically towards start cell.
Incremental: D* Lite inherits incremental search property from Dynamic SWSF-FP; it reuses information from previous search to repair path in a series of similar searches, which is much efficient than calculating path from scratch.
The pseudo code for D* Lite is shown in Figure 9.
Pseudo code of D* Lite algorithm.
In general, the pseudo code of D* Lite maintains three invariants:
Invariant 1:
Invariant 2: OPEN list contains exactly only local inconsistent cells
Invariant 3: Priority value of cells in OPEN list is equal to its
At the first run, D* Lite is exactly like A*. It guarantees to expand cells at most twice in each search routine due to the concept of one-step look-ahead estimated goal distance r
Despite being an effective replanner for dynamic environment, D* Lite does have a big pitfall for certain circumstances. In fact, D* Lite is designed to be implemented in mobile robot with range sensors, in which the environment changes are perceived near the robot (the starting cell). In other word, the changes occurred at the perimeter of expansion. Therefore, D* Lite just propagates inconsistencies in a small area near the search front; the replanning process is efficient. However, the problem arises when we combine other sensors (e.g. UAV, satellite, etc.) to detect environment changes in further area near the goal. Intuitively, we can imagine a valley where
This behaviour leads us to a problem statement: The location of environment changes with respect to goal position makes an enormous difference to efficiency of D* Lite. This problem is also addressed by the author of D* Lite in [12] as open question. In other papers, mathematical approach is used to study this pitfall in [16]. Unfortunately, the problem is still not solved thoroughly; however, there are approaches to partly overcome this pitfall in certain situation that will be presented in the following section.
This section describes variants of D* Lite that partially solves the mentioned pitfall of D* Lite. Hence, these state-of-the-art algorithms improve the computation factor of D* Lite.
As one of the prominent properties of D* Lite, it maintains the optimality of solution paths. However, in real-world application, optimal paths are difficult to calculate due to the complexity and uncertainty of environment within available time; the paths are also quickly to become out of date because of dynamic surroundings. Moreover, the state space, which encodes global motion constraints of the robot, tends to be high dimensions. With these difficulties, D* Lite becomes unreliable when implementing in real robot.
Anytime Dynamic A* (AD*) [12], which is a combination of D* Lite and ARA* algorithm, is a trade-off between computation and path optimality. It sacrifices the shortest path to quickly generate suboptimal solution to cope with imperfect information and dynamic environment. AD* inherits these properties from its parent algorithms:
In general, AD* executes a series of similar searches with decreasing suboptimal bounds to generate a series of paths with improved bounds. As environment changes are detected, the locally inconsistent cells are placed in OPEN list with uninflated heuristic keys, and AD* processes these cells to correct the outdated path. The pseudo code for AD* is shown in Figure 10.
Pseudo code of AD* algorithm.
At first, we set the suboptimal bound
When changes are perceived, the suboptimal bound of solution is no longer guaranteed, especially the under-consistent cells, due to the fact that there could have shorter paths exist. Therefore, these under-consistent cells are placed in OPEN list with uninflated heuristic to ensure these cells propagate their inconsistences to neighbours first when ComputeOrImprovePath() function is called (Figure 10, lines 5 and 45–50). However, because of this effect, many under-consistent cells quickly rise to the top of OPEN list; they usually do not contribute to calculate new path in practice (e.g. objects cause under-consistent changes like human movement, etc.). Hence, AD* tends to slow down when the path is near optimal. Theorems of AD* are described in detail in [12].
When “significant cell changes are detected” (Figure 10, line 53), there is a high chance that the problem of D* Lite occurs and the search tree may be corrupted heavily; the replanning process now is expensive; we can increase suboptimal bound
Unlike AD*, D* Lite with Reset (D*LR) [16] partially solves the problem D* Lite while still maintaining path optimality. The idea of D*LR is simple; it decides flushing previous search data and starts searching from scratch when the replanning process is expensive.
D*LR is a variant of D* Lite; it inherits all the properties of D* Lite. The main contribution of D*LR is that it proposes two criteria to decide whether to incrementally replan path or calculate a fresh path using A* at the position the robot detects changes. Let total traversed cell is
If the ratio is greater than
where
As can be seen, these criteria use only path information between consecutive detection incidents in order to estimate the amount of computation of replanning process comparing with planning over from scratch. The reason is that it is hard to predict propagation behaviour of OPEN list, because the state space is only partially known. Moreover, these criteria only work in high cluttered and complex environment, where environment changes usually block initial path and the new path is likely to be much longer than initial path. The pseudo code of D*LR is presented in Figure 11.
Pseudo code of D*LR. Other functions such as Key(), UpdateVertex() and ComputePath() are the same with D* Lite and thus are not presented.
The proposed criteria of D*LR are not robust due to extensively replying on environmental assumptions. However, the algorithm can be improved if criteria that can robustly estimate computation of replanning process in any kind of environment are applied. If criteria are robust, D*LR performance of each iteration is bounded by the complexity of A*:
Although cell decomposition approximation is widely used to discretize C-space for search-based algorithms due to its robustness (no prior environmental assumptions), this approximation intrinsically prevents search-based algorithm to produce optimal path. The search-based algorithms just allow to transition between cell centres, thus restricting robot traverse directions to increment of
There were many approaches to cope with this problem. For instance, post-processing method that finds the furthest point P along the solution path for which a straight line path from P to robot position is collision-free and replaces the original path to P with this straight line. However, this method sometimes does not work and increases the path cost. Another approach is fast marching method [22]; this method incorporates interpolation step in planning step to produce low-cost interpolated path. Nonetheless, this method assumes that transition cost between grid cells is constant and does not have heuristic property like A*; hence it is not applicable to outdoor environment, which requires fast path generating and non-uniform cost grid.
To incorporate incremental heuristic property of D* Lite, the authors of Field D* [3] embed linear interpolation method to the replanning process to generate “any-angle” optimal path that overcomes grid limitation in dynamic environment. The root cause of restriction of path optimality is the rule to transition between cell centres; the idea of Field D* to solve this problem is to remap state space graph vertices to the corner of each cell (see Figure 12). The nodes
Remapping state space graph vertices from cell centres to cell corners (a) Center Vertices, (b) Corner Vertices and (c) Optimal Path intersected ⟶
In this case, the edge, which resides on the boundary of two cells, has the edge cost equal to the minimum of cost of the two cells. Field D* use linear interpolation to compute approximately the cost of any point
where
Linear interpolation process to compute path cost of s using edge ⟶
where
The interpretation from formulas (4) into ComputeCost() function is described in detail in [3]. This optimization approach can be plugged in any dynamic planner by replacing standard cost function between cell centres by function ComputeCost(). In addition, due to remapping graph vertices into cell corners, we also need to change finding cell centre neighbours to a pair of corner nodes as illustrated in Figure 13. Once the path costs of necessary nodes are computed, the path is generated by starting from the initial node and iteratively finds, using linear interpolation, the optimal node on the neighbor cell boundary to move next. The pseudo code of Field D* and modifications in red colour are shown in Figure 14. Note that the differences between D* Lite and Field D* are highlighted in red. The function Key(), ComputePath() are the same as D* Lite and thus is not presented. This pseudo code is a basic version of Field D*; optimised versions are presented in [3].
Pseudo code of Field D*.
Field D* inherits all properties of D* Lite; it combines linear interpolation method to compute path from any point inside cell, not just corners or cell edges. This feature is crucial for robot to get back on track if the actuator execution is faulty. Moreover, Field D* is not subjected to direction restriction; hence, it produces much shorter and smoother path.
In this section, using our path planning framework, we demonstrate the evaluation comparison between algorithms in search-based family in terms of performance and path optimality.
To visualise the evolution in computation of search-based algorithms, we compare the replanning computation of D* Lite, Anytime Dynamic A* and D* Lite with Reset. The purpose of the comparison is to demonstrate the performance improvements of D* Lite variants in order to apply on robot that operates in complex and dynamic environment. However, since the planning time depends on the implementation and machine configuration, we therefore choose the amount of cell expansion in each replanning iteration of search-based algorithm to be standard performance measurement of the mentioned algorithms. This method is independent on machine specifics and actual implementation and therefore firmly accurately shows the enhancement of this evaluation. The path solution ratio between AD* with different
The experiments are conducted on our 2D simulation engine. The state space is a 2D grid cell with uniform resolution [23]. The conceptual robot in this simulation has two-cell-unit range and its own known grid map to detect environment changes (unblocked cell to blocked cell and vice versa) as it moves along the initial path (see Figure 15).
Simulated environment on our framework.
We evaluate the performance and path solution of search-based algorithms in two scenarios: partially known and unknown 2D grid environment with uniform resolution. The total expanded cells are averaged based on total replanning processes on each simulation instance, with 95% confident. The path solution of each algorithm is counted as the total cells that the robot has traversed from corner to corner of the map. We decrease the suboptimal bound of AD* for 0.1 per step the robot travels until the suboptimal bound reaches 1.0 (optimal path).
Figure 16 shows the speedup result throughout the evolution from Replanning A* to AD* with different
Comparison between search-based algorithms on partially known environment with increasing map scale in terms of computation and path solution.
As can be seen, AD* has the highest performance that has least total expanded cells in replanning process; the higher the suboptimal bound, the better the performance. The reason is AD* is inflated its heuristic function to make it greedier in expanding cells towards goal. It is interesting that path solution of AD* is not much longer than optimal path. As the scale of map is increasing, the path between map corner is longer to travel, and thus, the robot is given enough time to improve its solution (path ratio with
D*LR slightly improves the performance of D* Lite; it is because D*LR relies on computation differences between Replanning A* and D* Lite. In fact, the pitfall of D* Lite rarely happens in scenarios that the robot detects changes near its position. Replanning A* does not have incremental property and thus uses the highest computation.
The data confirms the fact that AD*, in average throughout the increasing map scale, improves 125% and 194% performance compared to D* Lite with
Figure 17 describes the evaluation case on unknown environment. The environment is initially generated with random obstacles that occupy 15% of the map. The robot does not know the initial conditions; it will replan its path whenever it detects obstacles that do not exist in its map.
Comparison between search-based algorithms on unknown environment with increasing map scale in terms of computation and path solution.
For unknown environment scenario, D*LR performs significantly better than D* Lite as increasing map scale. The reason is that if the replanned path is much longer than the initial path, which is the common case in unknown environment, the replanning process of D* Lite is also expensive. AD* still has the least computation compared to old search-based algorithm; it reduces drastically the computation of D* Lite with 845% better performance, in the case
In practice, motion planning algorithms can be implemented on top of navigation layer such as simultaneous localization and mapping (SLAM) for autonomous robot. While navigation layer enables the robot to perceive surrounding information and its position relative to the surroundings, motion planning layer gives the robot abilities to plan a path in surrounding environment and make decision to avoid obstacles. Because of that fact, navigation and motion planning are always paired up to enable autonomous robot to operate in dynamic and complex environment.
This chapter is a guide to comprehend the foundation of motion planning, in particular, search-based path planning algorithms. In this chapter, we present the steps to develop and formulate a motion planning problem. We also describe the evolution branches of motion planning and then focus on the development of search-based algorithm family. Each algorithm in search-based family is invented to cope with increasing demands in performance or solution quality, for the robot to operate in more complex scenarios. To reinforce the revolution statement of state-of-the-art search-based algorithms, we provide a computation and optimality comparison between search-based algorithms on partially known and unknown environment. Based on the data, we conclude that Anytime Dynamic A* is the most suitable algorithm that enables the robot to operate in cluttered and fast changing scenario.
Until recently, the mainstream of motion planning development is to enhance the performance of search-based algorithm and their solution optimality by modifying cell decomposition method. There are signals that the trajectory planning paradigm is starting to be active research field after being frozen for a decade. We expect that the future development of trajectory planning will robustly incorporate motion constraints with higher optimality and better computation. The ultimate goal of motion planning field is giving robot spatial decision planning converging to human ability.
Every time academics talk about the evolution of human societies and the advance of humanity, language is always mentioned, followed by different pieces of technology that allowed us to change the world. Few times, medicine is mentioned, and within the same area of knowledge, pharmacology is even more frequently omitted. But without the development of pharmacology as a science founded in systematic research, the capacities of medical sciences and therapeutics would be very limited. Knowledge in pharmacology allows us to understand that there exist chemical substances with very specific structures and properties which, in controlled doses, can interact with the normal physiology of our organism in order to produce effects that improve our health, known as therapeutic effects; but if the doses are insufficient or excessive, the effects will be useless or harmful (toxic), respectively [1]. These substances responsible for the actions of medicines are named as active compounds.
\nMost of the active compounds used in medicine were consumed together with the organism which contained them, most frequently plants. As chemistry advanced, scientists succeed in isolating these compounds and described their chemical structure. In consequence, laboratories started to synthesize these substances and others with a similar structure that should be tested in research laboratories before using them to treat diseases in humans [2].
\nNowadays, pharmacological research has grown beyond treatments for infectious agents, covering diseases related to the alteration of the normal functioning of the central nervous system (CNS). There are medications to treat disorders such as depression, anxiety, chronic pain, attention deficit and hyperactivity disorder, epilepsy, and Parkinson’s disease, and new drugs are desperately sought to stop Alzheimer’s disease. On the other hand, one of the most important current health problems is related to the addictive behaviors triggered by the consumption of certain substances and the side effects of these addictions: respiratory and cardiovascular diseases in the case of tobacco, metabolic diseases in the case of alcoholism and addictive consumption of refined sugars, infectious diseases in the case of injected drugs, and many others that are not mentioned here. Without losing sight of the fact that addiction is itself a disease of the nervous system with devastating effects
Behavioral pharmacology, also known as psychopharmacology, has developed as an interdisciplinary science that comprises fields such as neuroethology, neurochemistry, pharmacology and neuropharmacology, psychophysiology, neurophysiology, experimental analysis of behavior, and several other fields related to neurosciences [13]. Behavioral pharmacology is founded on systematic research with precise methods for assessing and interpreting the effects of chemical, hormones, and drugs on the behavior in humans and experimental animals in order to establish its potential as therapeutic agents or pharmacologic tools to explore how the brain functions and the underlying neurobiological mechanism of cognition, emotions, and behavior. Behavioral pharmacology must thus be an integral component of many neuroscience research programs [14].
\nIn this sense, the development of behavioral pharmacology comprises the development of areas as pharmacology and psychology, experimental analysis of behavior, and recently neuroscience. For a historical review, see [14, 15, 16]. However, research in behavioral pharmacology can be summarized in: (1) the development of procedures to screen pharmacological agents for potential clinical effectiveness. (2) Perfecting behavioral techniques to explore the mechanisms of action of behaviorally active drugs and using these chemicals and drugs as tools for the analysis of complex behaviors (i.e., when drugs reinforce behavior and when drugs serve as discriminative stimuli) [16] (see Table 1). Therefore, drugs are not only a subject of study, because of its behavioral effects but are also a piece of technology that helps to elucidate how behaviors are controlled by living organisms.
\nYear | \nDescription | \nReference | \n
---|---|---|
1936 | \nSelye H. described the impact of several types of adverse stimuli on animal health, in the form of a syndrome characterized by three phases: alarm, adaptation, and exhaustion, which can lead to death if stimuli are maintained. This syndrome was later named as the stress response which has been intensively studied and strongly associated with the impairment of brain function in animals or the development of mental disorders in humans | \n[17] | \n
1972 | \nThe first study to administrate Delta-9-tetrahydrocannabinol in humans to test the effects on sleep patterns is carried out. The results show a decrease in sleep onset latency. To date, there are controversial results about the positive effects the cannabis on sleep quality | \n[18] | \n
1977 | \nThe forced swim test is proposed as a behavioral tool to explore the effects of antidepressant drugs in rats and mice that are exposed to a stressful inescapable condition that triggers despair behavior (immobility) | \n[19] | \n
1986 | \nElevated plus maze is developed as a tool to measure anxiety-like behaviors of the rat and test substances with potential anxiolytic effects | \n[20] | \n
1988 | \nModafinil was prescribed for the first time for the treatment of narcolepsy and idiopathic hypersomnia in patients | \n[21] | \n
2005 | \nThis study explored the behavioral and neuronal response to stress in ovariectomized rats (OVX). These rats were more sensitive to stress, which was associated with a low concentration of steroid hormones. This effect was prevented by restitution with 17-β estradiol | \n[22] | \n
2006 | \nAnxiety-like behavior is dependent on the post-ovariectomy time frame. At 12-week post-ovariectomy there is more anxiety-like behavior than a 3-week post-ovariectomy | \n[23] | \n
2016 | \nThe first systemic review and meta-analysis that discuss the effects of the orexin agonist Suvorexant for the treatment of insomnia. Suvorexant improved some sleep parameters, but some adverse effects were reported | \n[24] | \n
2019 | \nIn this study, it was identified that at 3-week post-ovariectomy appears anxiety-like behavior, but from 6-week post-ovariectomy in addition to anxiety-like behavior, also increases depression-like behavior in rats, supporting an experimental model of surgical post-menopause | \n[25] | \n
Emblematic research in behavioral pharmacology.
Behavior is a biological property of organisms, which remarks on the significance of the study of drug-behavior interactions [15]. Maybe, a great example of the impact of behavior beyond psychology is the research by ethologists K. Lorenz, N. Tinbergen, and K. von Frisch, which focused on the analysis of behavior in several species including fish, insects, and birds, and the importance of which made them worthy of the Nobel price of medicine in 1973 “
The first step in all behavioral sciences has been to define what is behavior; it could seem an easy task, but historically many different definitions of behavior have been used by scientists over the time, and even the knowing of a unique definition is elusive and may be useless for every different area such as psychology, ethology, and experimental analysis of behavior, among others; for review see [26, 27]. As mentioned before, one of the directions of behavioral pharmacology was the development of procedures to screen the effects of pharmacological agents on specific behaviors under controlled environments. This approach allows scientists to work with operational definitions of specific behaviors, for example, exploration can be measured by scoring ambulation, rearing or nose approaching to an object; sexual behavior can be measured by conditioned place preference, number of mounts, latency and number of ejaculations. All these behaviors are normally studied under controlled environments that are designed specifically to the required behavioral display and every feature of the environment; the experimental subjects or chemical agents with probed effects on humans have been studied in this environment with the purpose of establishing these manipulations as models of a specific behavior (see Table 2) as spatial learning and memory, or models of specific pathologies behaviorally expressed as is the case of anxiety [28], depression [29], obsessive compulsive disorder [30], Parkinson [31], epilepsy [32] or addictive behaviors [33], and sleep deprivation [34], among others.
\nResearch area | \nDescription | \n
---|---|
Hormone restitution therapy | \nThis review discussed, 25 years ago, the importance of steroid hormones in the regulation of behavior and some psychiatry disorders; particularly depression associated with premenstrual syndrome and the transition to menopause. Also, it discusses some research about the role of hormone restitution therapy in ameliorating depression symptoms [35] | \n
Sexual dimorphism | \nThis review discusses preclinical and clinical research that show how hormones are involved in the sex differences in some psychiatric disorders like anxiety, and their interactions between fear, stress, and gonadal hormones [36] | \n
Behavioral animal models | \nThis research reviews the relevance of non-mammalian models in behavioral pharmacology with application in the development of biological psychiatry [37] | \n
Behavioral model of menopause | \nThis review highlights the importance of animal models of menopause in the understanding of neurobiological changes associated with the long-term absence of ovarian hormones. To then elucidate novel perspectives and interventions to improve the life quality in the menopausal women under a translational context [38] | \n
Sleep and insomnia | \nThis review describes the efficacy of new drugs in the treatment of insomnia such as melatonin, Remelteon, Tasimelteon, and Suvorexant, among others [39] | \n
Hormones and behavior | \nThis review discusses the influence of hormones on brain function and behavior, and integrate information to explain how the brain and the body communicate reciprocally via hormones and other mediators, and in ways that influence brain and body health but which can also accelerate diseases processes when the mediators of allostasis are dysregulated [40] | \n
Addiction | \nA review of the most popular behavioral models for the study of addictions such as conditioned place preference and self-administration and new models to study behavioral addictions as gambling and exercise addiction [33] | \n
Sleep disorders | \nThis review describes the Pitolisant (Wakix®), first-in-class antagonist/inverse agonist of the H3 receptor for the treatment of narcolepsy with or without cataplexy [41] | \n
Current topics in behavioral pharmacology.
Animals are used as proxies for human phenomena throughout the literature, and the exact definition of what constitutes a “model” can be confusing. In behavioral pharmacology, a field that intersects between psychology, neuroscience, and pharmacology [42], different uses are attributed to different epistemic operations and, as a consequence, to different definitions of validity [43, 44]. One of the most basic definitions is that by Paul Willner, which defined screening tests as those uses of animal behavior that are capable of discriminating between different drug effects (i.e., possess high predictive validity); behavioral bioassays as those uses of animal behavior that are capable of shedding light on the neural basis of normal behavior (i.e., possess high face validity); and simulations as those uses of animal behavior that can inform on the etiology, pathophysiology, and treatment of human (mental) disorders (i.e., possess high construct validity). Further developments of this framework [45] advance the theory of validity, therefore improving the capability of researchers to evaluate animal models.
\nScreening tests show good predictive validity in that they are able to detect the effects of drugs, which are already known to have clinical efficacy; as a result, they are likely to be able to predict the effect of new drugs, which show similar biochemical or behavioral effects in the test [42, 43]. Examples include most uses of the tail suspension test and forced swim tests, which are commonly referred to as models of depression but actually do not simulate the etiological and pathophysiological aspects of human depression. When used without any further manipulations of the animal (i.e., lesions, genetic manipulations, or other stressors which are thought to be causally related to depression), these tests are good at discriminating drugs which act as serotonin reuptake inhibitors and reasonably good at predicting antidepressant efficacy. Since screening tests rely mostly on predictive validity, current approaches to modeling in behavioral pharmacology view them as limited. Moreover, producing models which show good construct validity in at least some domains (i.e., epidemiology, symptomatology and natural history, genetics, biochemistry, etiology, histological alterations, or endpoints) has been proposed as a way to indirectly increase predictive validity [46], as drugs which improve performance in a test that simulates at least some aspects of the target disorder.
\nBehavioral bioassays are tests that use nonhuman animals to try to understand the histological, electrophysiological, biochemical, and genetic bases of neurobehavioral functions [42, 43]. Usually, bioassays are used to understand normal functioning, instead of pathological alterations in these psychological processes. They rely on face validity—that is, how much performance in the test “resembles” the target human function. Of course, taken “as is,” face validity runs a great risk of anthropomorphism, and the resemblance should not be sought at the topography level, but at the functional level [47]. For example, the elevated plus-maze, when used as a test
Finally, simulations are tests, which use nonhuman animals to try to understand a human disorder from the point of view of etiology and pathophysiology [42, 43]. Most approaches to psychopathology currently frame disorders in a diathesis-stress theory [45], which assumes that vulnerabilities (general or specific; genetic, developmental, or temperamental) increase the probability of developing a specific disorder when the individual passes through general or specific stressors. In analogy, to develop a simulation of a mental disorder in a nonhuman animal, the vulnerabilities and stressors should be modeled, transforming an “initial organism” into a “vulnerable organism” and this latter into a “pathological organism,” in which behavioral endpoints are assessed and biomarkers evaluated [44, 45]. From all senses of “behavioral model,” the simulation is the one that better approaches the idea of modeling a disease [42, 44], but is also the more time-consuming. Moreover, to increase the construct validity of a simulation, aspects such as etiology and pathophysiology should be taken into consideration, but sometimes these aspects are unknown and are precisely what is under investigation [42]. Thus, high construct validity needs to be balanced against practical constraints, and therefore no behavioral simulations with optimal characteristics exist [52]. In the next pages some examples of these “behavioral models” are described in order to introduce the present book.
\nUnder the framework discussed above for behavioral models, interesting approaches have appeared using non-rodent species. While mice and rats are still the most widely used model organisms in behavioral pharmacology [53], zebra fish (
Currently, very few true simulations exist in zebra fish, and most behavioral tests that are used to study psychiatric disorders in this species are actually screening tests or behavioral bioassays. This is a consequence of an extensive focus of the research in the field in the last 20 years on developing behavioral tests. This step, of course, was necessary to galvanize research in the field. Notable exceptions exist, but—as is the case with most initial work on using model organisms to study disorders and investigational treatments—these are still limited. However, past research has identified and allowed to control factors that affect zebra fish behavioral tests. Now it is clear how chemical properties of the water, illumination, number of fish per tank and routes of administration modify pharmacological effects. For example, administration by immersion is useful for chronic treatments but lacks a precise control of the doses absorbed [56], on the other hand, intraperitoneal administrations ensure the absolute control of doses but are not useful for chronic treatments due to the stress that produce [57]. Oral administration through drugs incorporated in the food is useful for chronic treatments and controlling the doses is easier than immersion [58], however chemical properties of the drug determine their ability to hold into the food until swallowed and oral metabolism must be considered. With the standardization of the proper protocols these factors can be controlled, and its effects limited so, behavioral pharmacology research with zebra fish is still a suitable and growing field.
\nThe zebra fish light/dark test [59] and the novel tank test [60] are widely used to test the effects of different drugs on anxiety-like behavior in this species. These tests rely on natural preferences observed in the wild, and display excellent remission validity—that is, they are sensitive to drugs which affect anxiety in clinical settings, and not sensitive to drugs which do not affect anxiety [61]. As a result, these tests were used as screening tests to investigate new drugs, including drugs derived from natural products and plants, for example, refs. [62, 63]. These tests have also been used to study the neural mechanisms of anxiety-like behavior [64, 65, 66, 67, 68]. Thus, these tests can be used both as screening tests and as behavioral bioassays.
\nThe behavior of adult zebra fish is more complex than the behavior of larvae, but its throughput is smaller. Throughput can be increased by testing larval behavior in microplates [69]. Light levels and stimuli can be delivered simultaneously to many larvae at once, increasing throughput and reproducibility. For example, the photo-motor response (a stereotypic series of motor behaviors that are elicited by high-intensity light) is sensitive to a wide range of psychoactive drugs and able to predict mechanisms of action of drugs, which were previously not investigated in rodents [70]. A battery of assays has been proposed in larval zebra fish that is highly sensitive to antipsychotics and able to identify haloperidol-like compounds [71]. While suffering from the low face and construct validity these assays show very good predictive validity, and therefore are suitable as screening tests.
\nExamples of simulations can be found in the field of neurological disorders [72]. An interesting example is the generation of mutants with differences in genes known to be associated with diseases. In humans, mutations in the SCN1A gene, which encodes a voltage-gated sodium channel, causes Dravet syndrome, characterized by severe intellectual disability, impaired social development, and drug-resistant seizures. The scn1Lab mutant zebra fish displays spontaneous seizure-like electroencephalogram activity, convulsive-like motor patterns, and hyperactivity [73]. These mutants have been used to investigate drugs, which could be used to treat Dravet syndrome in human patients; drugs that affect the serotonergic system have been found to ameliorate the symptoms in the mutants [74], and suggest interesting avenues for human patients.
\nNow, we will review the role of behavioral pharmacology on a subject extensively explored in human trials: sleep.
\nPharmacological treatment of sleep disorders is still partially known and not well understood. Currently, extensively pharmacological research is focused in two sleep disorders: insomnia and narcolepsy. Insomnia is defined as the individual’s inability to fall asleep, manifested by a long latency to sleep onset and frequent nighttime awakenings experienced three times per week or more, for at least 1 month [75]. Insomnia causes emotional disturbances, impairs cognition, and reduced quality of life [76, 77]. Most epidemiologic studies have found that about one-third of adults (30–36%) report at least one symptom of insomnia, like difficulty initiating sleep or maintaining sleep [78]. Currently, benzodiazepines or Z-drugs (zopiclone, zolpidem, or zaleplon) are the first options to treat insomnia. These drugs act as positive allosteric modulators at the GABAA binding site, potentiating GABAergic inhibitory effects [79]. However, short-term or long-term treatment with these drugs has undesirable effects such as cognitive or memory impairment, the rapid development of tolerance, rebound insomnia upon discontinuation, car accidents or falls, and a substantial risk of abuse and dependence [39, 80, 81], which make necessary research on new potential therapeutic agents.
\nAccording to the new evidence-based clinical practice guidelines for the treatment of insomnia [75], new pharmacology agents for insomnia management are implemented (Table 3).
\nDrugs | \nSite of action | \nTherapeutic effect | \n
---|---|---|
Antidepressant (trazodone, mirtazapine, olanzapine, and quetiapine) | \nAgonists of the serotonin receptor 5-HT2A and 5-HT2C\n | \nModerate improvement in subjective sleep Little improvement in sleep efficiency [82] | \n
Antiparkinsonian ropinirole | \nAgonist of the dopamine receptor D2 | \nImprovement in efficiency of sleep and total time slept [83] | \n
Suvorexant | \nAntagonist of the orexin receptor | \nImprovement of sleep onset and subjective total slept time compared to placebo [84] | \n
Ramelteon | \nDual agonist of both MT1 and MT2 melatonin receptors | \nImprovement in latency to persistent sleep, total sleep time and sleep efficiency [85] | \n
Diphenhydramine | \nAgonist of the histaminergic receptors | \nNo clear beneficial impact on sleep [86] | \n
New drugs used to insomnia management.
On the other hand, Type 1 narcolepsy (narcolepsy with hypocretin deficiency) is a chronic neurodegenerative sleep disorder caused by a deficiency of hypocretin-producing neurons in the lateral hypothalamus (LH). Hypocretin neurons are involved in the control of the sleep-wake cycle [87]. Treatment of narcolepsy is traditionally based on amphetamine-like stimulants that enhance dopaminergic release to improve narcoleptic symptoms. Nonetheless, a new group of drugs is arising as a forthcoming treatment of narcolepsy.
\nPitolisant (Wakix®) is an inverse agonist of the histamine H3 auto-receptor that not only blocks the braking effect of histamine or H3 receptor agonists on endogenous histamine release from depolarized synaptosomes but also enhances histamine release over the basal level (even at low nanomolar concentrations) in the structures as hypothalamus and cerebral cortex [88]. The administration of 20 mg/kg of Pitolisant promoted wakefulness, and decreased abnormal direct REM sleep onset in narcoleptic hypocretin knockout mice by enhancing histaminergic and noradrenergic activity [89]. Pitolisant seem a safe therapeutic option since doses of 120 mg once a day in the morning, that represent six times the therapeutic, doses did not produce adverse effects and plasma levels reduced at the end of the day, ensuring a lack of waking effect during the night [90]. Additionally, adverse effects due to metabolic drug-drug interaction are low since Pitolisant is metabolized by two distinct CYP450 isoforms. For example, the administration of 40 mg of Pitolisant together with 10 mg of Olanzapine to a group of healthy volunteers did not change drug plasma levels compared to only one drug administration [91].
\nAny chapter on behavioral pharmacology would be incomplete without a section reviewing the effects of certain hormones. Behavioral, emotional and affective states are influenced by plasma and brain concentration of steroid hormones in diverse organisms. Particularly, in nonhuman primates and humans there is significant sexual dimorphism respect to behavior and emotional states. Initially, the attributed properties of steroid hormones were related to the maintaining of secondary sexual characters and reproductive function, but some decades ago, it has been established that steroid hormones also influence behavior and some psychiatric disorders. Expression of anxiety- and depression-related behaviors depends on plasma and brain levels of steroid hormones; which in vulnerable subjects could predispose to development of some psychiatric disorder [92].
\nIn humans, anxiety and depression symptoms are more frequent in women than men in a proportion of 3:1. These differences have been attributed to differences in the concentration of steroid hormones. Particularly in women, a high incidence of anxiety and depression symptoms has been identified during physiological states characterized by low concentration of steroid hormones (i.e., estradiol, progesterone and their reduced metabolites) as naturally occur during premenstrual period, post-partum period, and transition to menopause [93, 94]. However, it also occurs when women are subjected to a surgical procedure to remove the ovaries (i.e., oophorectomy) with or without the uterus (i.e., hysterectomy), where an abrupt reduction in steroid hormones concentrations occurs [95] affecting behavioral response. Apparently, the significant reduction of steroid concentration produces anatomical, physiological, and neurochemical changes in the brain, that negatively impact on behavior, emotional, and affective states [96, 97].
\nPreclinical research with laboratory animals has made possible identify the behavioral and emotional changes associated with a reduced concentration of steroid hormones when rats are undergoing to an extirpation of both ovaries (i.e., ovariectomy), which increases vulnerability to stress that can be reverted by injection of severe doses of estradiol [22]. The long-term ovariectomy (> 8 weeks post-ovariectomy) is considered then as a surgical menopause model that explores the behavioral, neurobiological, emotional and affective changes associated with oophorectomy that occurs in women [98]. In the long-term ovariectomized rats display higher anxiety- and depression-like behavior in experimental models such as elevated plus maze and forced swim test, respectively. These behavioral changes are correlated with a reduced neurochemical activity on serotonergic, noradrenergic, dopaminergic, and GABAergic pathways; in addition to a reduction in the number of dendritic spines and neuronal activity in some brain structures (i.e., hippocampus, amygdala, lateral septum, prefrontal cortex, among others). Through behavioral analysis is possible identifying the gradual changes associated with surgical menopause in rats. It was observed that after 3-week post-ovariectomy, rats showed high anxiety-like behavior (i.e., there is a reduction of exploration of the open arms) in the elevated plus maze with respect to cycling rats with intact ovaries, but after 6-week post-ovariectomy, additionally to anxiety-like behavior, rats also displayed high depression-like behavior in the forced swim test (i.e., increase in the total time of immobility), which negatively correlates with the Fos-immunoreactive cells in limbic brain structures such as the lateral septal nucleus [25]. The behavioral and neurochemical characterization of long-term ovariectomy allows the pharmacological research of different substances that could be potentially relevant to the development of pharmacological therapies to ameliorate anxiety and depression symptoms that occur during natural or surgical menopause.
\nAs mentioned before, anxiety-like behavior is dependent on the post-ovariectomy time frame in rats. After 12-weeks post ovariectomy rats show high anxiety-like behavior respect to rats at 3-weeks post-ovariectomy in the burying behavior parading. This high anxiety-like behavior is reduced after injection of 1–2 mg/kg diazepam, a typical anxiolytic benzodiazepine drug [23]. Similarly, i.p. injection of 0.5 and 1 mg/kg phytoestrogen genistein (a secondary metabolite obtained from soybeans) significantly reduces anxiety-like behavior in rats at 12-week post-ovariectomy in the light/dark behavioral paradigm through action on the estrogen receptor-β [99, 100]. Additionally, s.c. injection of 0.9 or 0.18 mg/kg genistein exerts similar anxiolytic-like effects in the elevated plus maze than 17β-estradiol in rats subjected to surgical menopausal model. This is consistent with clinical observations that estradiol reduces anxiety symptoms associated with natural and surgical menopause, and additionally supports the potential use of phytoestrogens as an alternative therapy to ameliorate emotional symptoms associated to menopause.
\nResearch in behavioral pharmacology has contributed to the study of pharmacological actions of natural products. In rats at 12-weeks post-ovariectomy, 50 mg/kg by oral rout of the aqueous crude extract of
As mentioned before, behavioral pharmacology is an interdisciplinary field. The present chapter tried to reflect briefly the essence of behavioral pharmacology through an anecdotical review of its developments in areas familiar to the authors. All findings mentioned above underline the importance of the research in behavioral pharmacology on the understanding of the neurobiology of different disorders and the mechanism of action of drugs used to treat such disorders, and at the same time, provide a perspective on the current research done in this growing area, which is and will be a cornerstone in the understanding of human behavior and mental health.
\nThe authors do not have any conflict of interest.
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