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Robotics » Robotics Navigation » "Advances in Robot Navigation", book edited by Alejandra Barrera, ISBN 978-953-307-346-0, Published: July 5, 2011 under CC BY-NC-SA 3.0 license. © The Author(s).

Chapter 10

Knowledge Modelling in Two-Level Decision Making for Robot Navigation

By Rafael Guirado, Ramón González, Fernando Bienvenido and Francisco Rodríguez
DOI: 10.5772/18243

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Peoplebot robot: components (ActivMedia Robotics, 2003) and picture in action
Figure 1. Peoplebot robot: components (ActivMedia Robotics, 2003) and picture in action
Navigation architecture
Figure 2. Navigation architecture
CommonKADS kernel set of models
Figure 3. CommonKADS kernel set of models
Simple TMD
Figure 4. Simple TMD
Schema for the proposed two-level multi-agent architecture
Figure 5. Schema for the proposed two-level multi-agent architecture
Representation of a TMD for a pre-filtered localization system
Figure 6. Representation of a TMD for a pre-filtered localization system

Real scenario (University map) and followed trajectory. The mobile robot has guided a person from bus stop (start) to the library (goal)
Figure 7. Real scenario (University map) and followed trajectory. The mobile robot has guided a person from bus stop (start) to the library (goal)
GPS signal during the robot travel
Figure 8. GPS signal during the robot travel
Average suitability values for the localization methods in every path (“a”, “b”, “c”, “d”)
Figure 9. Average suitability values for the localization methods in every path (“a”, “b”, “c”, “d”)

Knowledge Modelling in Two-Level Decision Making for Robot Navigation

Guirado Rafael1, González Ramón, Bienvenido Fernando and Rodríguez Francisco

1. Introduction

In recent years, social robotics has become a popular research field. It aims to develop robots capable of communicating and interacting with humans in a personal and natural way. Social robots have the objective to provide assistance as a human would do it. Social robotics is a multidisciplinary field that brings together different areas of science and engineering, such as robotics, artificial intelligence, psychology and mechanics, among others (Breazeal, 2004). In this sense, an interdisciplinary group of the University of Almería is developing a social robot based on the Peoplebot platform (ActivMedia Robotics, 2003). It has been specifically designed and equipped for human-robot interaction. For that purpose, it includes all the basic components of sensorization and navigation for real environments. The ultimate goal is that this robot acts as a guide for visitors at our university (Chella et al., 2007). Since the robot can move on indoor/outdoor environments, we have designed and implemented a two-level decision making framework to decide the most appropriate localization strategy.

Knowledge modelling is a process of creating a model of knowledge or standard specifications about a kind of process or product. The resulting knowledge model must be interpretable by the computer; therefore, it must be expressed in some knowledge representation language or data structure that enables the knowledge to be interpreted by software and to be stored in a database or data exchange file. CommonKADS is a comprehensive methodology that covers the complete route from corporate knowledge management to knowledge analysis and engineering, all the way to knowledge-intensive systems design and implementation, in an integrated fashion (Schreiber et al., 1999).

There are several studies on the knowledge representation and modelling for robotic systems. In some cases, semantic maps are used to add knowledge to the physical maps. These semantic maps integrate hierarchical spatial information and semantic knowledge that is used for robot task planning. Task planning is improved in two ways: extending the capabilities of the planner by reasoning about semantic information, and improving the planning efficiency in large domains (Galindo et al., 2008). Other studies use the CommonKADS methodology, or any of its extensions, to model the knowledge; some of the CommonKADS extensions that have been used in robotics are CommonKADS-RT, for real time systems, and CoMoMAS (Conceptual Modelling of Multi-Agent Systems), for multi-agent systems. The first one is based on CommonKADS with the addition of necessary elements to model real time restrictions and it is applied to the control of autonomous mobile robots (Henao et al., 2001). The second one extends CommonKADS towards Multi-Agent Systems development. A Nomad200 mobile robot is used to analyse two agent architectures, AIbot and CoNomad, by reverse engineering and to derive conceptual descriptions in terms of agent models (Glaser, 2002). Nowadays the knowledge engineering focuses mainly on domain knowledge, using reusable representations in the form of ontologies (Schreiber, 2008).

One fundamental task to achieve our goal is robot navigation, which includes the subtasks of path planning, motion control, and localization. Generally, in the process of developing robots, robotics engineers select, at design time, a single method (algorithm) to solve each of these tasks. However, in the particular case of social robots (usually designed with a generic purpose, since its ultimate goal is to act as a human) it would be more interesting to provide several alternatives to solve a specific task and the criteria for selecting the best solution according to the current environment conditions. For instance, for the specific task of localization, the robot could decide to use a GPS-like solution, if it is moving on an open space, or dead-reckoning if it is in an indoor environment.

The main contribution of this work is the development of an operational knowledge model for robot navigation. This model leads to a generic and flexible architecture, which can be used for any robot and any application, with a two-level decision mechanism. In the first level, the robotics engineer selects the methods to be implemented in the social robot. In the second level, robot applies dynamic selection to decide the proper method according to the environment conditions, taking into account a suitability criteria table. Dynamic selection of methods (DSM) lets to choose the best alternative to perform a task. It uses several suitability criteria, criterium weights, selection data and knowledge, and an aggregation function to make the decision (Bienvenido et al., 2001).

The chapter is organized as follows. The second section presents the description of the robot system used in this work. In the third section, the methodology for the knowledge representation based on DSM is shown. Next, the fourth section shows the knowledge modelling for the localization subsystem needed to develop the generic multi-agent system for the social robot Peoplebot. The next section discusses the results of a physical experiment carried out to analyze the proposed methodology. The last section is devoted to conclusions and further works.

2. System description

In this work, the mobile robot called Peoplebot of ActivMedia Robotics Company has been used to test through physical experiments the proposed decision making approach. It is a mobile robot designed and equipped specifically for human-robot interaction research and applications. It includes all the basic components of sensorization and navigation in real environments, which are necessary for this interaction (see Fig. 1). It has two-wheel differential with a balancing caster and it feeds on three batteries that give an operational range of about ten hours. It also has installed a touch screen which displays a map of the University of Almería. Furthermore, for speech communication, it has two microphones to capture voice and two speakers. In this way, a user can interact with the robot either by manually selecting a target in the touch screen showing the environment map or by speaking directly to the robot.


Figure 1.

Peoplebot robot: components (ActivMedia Robotics, 2003) and picture in action


Figure 2.

Navigation architecture

For navigation purposes, a typical four-layer navigation architecture has been implemented (see Fig. 2). The top layer is devoted to path planning, that is, the generation of the reference trajectory between the current robot position and the target commanded by the user (touch screen or speech recognition modules). Then, a motion controller based on pure-pursuit (Coulter, 1992) is used to generate the actual wheel velocities. In order to ensure that the wheels move at the desired setpoints two low-level PID controllers were tuned. Finally, a layer devoted to localization is implemented. This localization layer is detailed subsequently.

3. Methodology

The knowledge model, about the localization for social robots described in this work, is based on some extensions of knowledge representation methodologies (like CommonKADS) and the DSM. Here, we introduce those approaches and a short summary of the localization algorithms implemented in the system.

3.1. Knowledge representation: the CommonKADS methodology

The CommonKADS methodology was consolidated as a knowledge engineering technique to develop knowledge-based systems (KBS) in the early 90’s (Schreiber et al., 1994). This method provides two types of support for the production of KBS in an industrial approach: firstly, a lifecycle enabling a response to be made to technical and economic constraints (control of the production process, quality assurance of the system,...), and secondly a set of models which structures the development of the system, especially the tasks of analysis and the transformation of expert knowledge into a form exploitable by the machine (Schreiber et al., 1999). Our proposal supposes to work in the expertise or knowledge model, one of the six models in CommonKADS. The rest are organizational (it supports the analysis of an organization, in order to discover problems and opportunities for knowledge systems), task (it analyzes the global task layout, its inputs and outputs, preconditions and performance criteria, as well as needed resources and competences), agent (it describes the characteristics of agents, in particular their competences, authority to act, and constraints in this respect), communication (it models the communicative transactions between the agents involved in the same task, in a conceptual and implementation-independent way) and design models (it gives the technical system specification in terms of architecture, implementation platform, software modules, representational constructs, and computational mechanisms needed to implement the functions laid down in the knowledge and communication models). Fig. 3 presents the kernel set of models used in the CommonKADS methodology (Schreiber et al., 1994).


Figure 3.

CommonKADS kernel set of models

The purpose of the knowledge model is to detail the types and structures of the knowledge used in performing a task. It provides an implementation-independent description of the role that different knowledge components play in problem solving, in a way that is understandable for humans. This makes the knowledge model an important vehicle for communication with experts and users about the problem solving aspects of a knowledge system, during both development and system execution (Schreiber et al., 1999). So, its final goal is to analyze the tasks (objectives), methods (possible solution mechanisms), inferences (algorithms or agents) and domain knowledge elements (context and working data) for the KBS to be developed. These four elements permit to represent the knowledge involved in our mobile robot system. So, we have decided to use this knowledge engineering methodology.

The Task-Method Diagrams (TMD) (Schreiber et al., 1999) to model the solution mechanism of the general problem represented by the highest-level task (main objective) are used. TMD presents the relation between one task to be performed and the methods that are suitable to perform that task, followed by the decomposition of these methods in subtasks, transfer functions and inferences (final implemented algorithms). Fig. 4 shows an example of TMD tree, where the root node represents the main task (Problem). It can be solved using two alternative methods (Met 1 and Met 2). First of them is implemented by the inference Inf 1, a routine executed by an agent. Second method requires the achievement of three tasks (really are two transfer functions Tran. Fun. 1 and Tran. Fun. 2 –special type of task, so it is represented by the same symbol- and one task Task 1). Transfer functions are tasks whose resolution is responsible for an external agent (for instance, it could be used for manual tasks). There are two methods to solve Task 1; they are Met 3 and Met 4. Second one is implemented by the inference Inf 2, while Met 3 requires the performance of four tasks: Task 3, Task 4, Task 5 and Task 6; each one is solved by a correspondent method (Met 5, Met 6, Met 7 and Met 8, respectively). These four methods are implemented by the inferences Inf 3, Inf 4, Inf 5 and Inf 6.

CommonKADS proposes that the different elements (tasks, methods and inferences) of the TMD are modelled using schemas like CML or CML2 (Guirado et al., 2009). These schemas formalize all the knowledge associated to each one of these elements.


Figure 4.

Simple TMD

3.2. Dynamic selection of methods

A given task, at any level, can be performed by several alternative methods, and these can be only applied at specific conditions. DSM is based on a general decision module that, taking into account the suitability criteria defined for each alternative method and actual data, would activate the most appropriate method. These suitability criteria have assigned weights whose values are calculated through functions that depend on the current knowledge of the problem and modify the suitability criteria values of the alternative methods to solve a given task (Bienvenido et al., 2001). For example, Table 1 shows the structure of the suitability criteria for a set of alternative methods. There are criteria that must be completely fulfilled, and others are conveniently weighted to offer a condition that increase or not the suitability of a given method. This technique was previously used in greenhouses design (Bienvenido et al., 2001), and robot navigation (Guirado et al., 2009).

MethodCriterion 1Criterion 2Criterion 3Criterion 4Criterion 5
Method 143f1( )1g1( )
Method 211f2( )3g2( )
Method 322f3( )2g3( )
Method 455f4( )1g4( )
Method 522f5( )2g5( )

Table 1.

Example of structure of the suitability criteria table

In this example, criteria 3 and 5 are hard constraints or critical (C). Notice that corresponding functions fM() and gM() can only take the values 0 or 1 (depending on environment conditions), where a value of 0 means that the method is not applicable if this criterion is not met, and a value of 1 means that it can be used. The other criteria (C1, C2 and C4) can take values between 1 and 5 according to the suitability of the method. These criteria are called soft constraints or non-critical (N).

In this case, the global suitability value S for the method M (M = {1, 2, 3, 4, 5}) is given by the following equation:

SM= fM() * gM() * (+ W1 * C1M+ W2 * C2M+ W4 * C4M)

Where CiM is the value of the criterion i for the method M, and Wi is the weight for the criterion i. These weights depend on the environment conditions and their sum must be equal to 1. For instance, assuming that W1 = 0.5, W2 = W4 = 0.25 and that the suitability criteria table is as shown in the table above (with f1() = f5() = 0, f2() = f3() = f4() = 1, g1() = g2() = g3() =1, and g4() = g5() = 0), then the selected method would be the number 3 (S1 = 0, S2 = 2.5, S3 = 3, S4 = 0, and S5 = 0). Notice that if there are two or more methods with the highest suitability value, the current method remains as selected, and if not, the method is selected randomly.

3.3. Localization algorithms

Robot localization is defined as the process in which a mobile robot determines its current position and orientation relative to an inertial reference frame. Localization techniques have to deal with the particular features of environment conditions, such as a noisy environment (vibrations when the robot moves, disturbance sources, etc.), changing lighting conditions, high degrees of slip, and other inconveniences and disturbances.

MethodIndoor/OutdoorComputing TimeLight ConditionsPrecisionCostSensorsFault-tolerant
OdometryBoth, not advisable for slip conditionsFastThere is no inconve-nienceError grows with distanceCheapEncodersIt only depends on encoders readings
Dead-reckoningBothFastThere is no inconve-nienceError grows with distance, although it is reduced taking IMU dataMore expensive than odometryEncoders and IMUIt depends on encoders and IMU
BeaconsMainly indoorMiddleBeacons must be observable from robotAbsolute position (no error growth)Expensive (installation of markers)Beacons, landmarks, etc.It uses many beacons
GPS-basedOnly outdoorMiddleThere is no inconve-nienceAbsolute position (no error growth)High cost of accurate GPSGPS, DGPS, RTK-GPSIt depends on the number of available satellites
Visual odometryBoth, advisable for slip conditionsUsually highIt depends on light conditionsError grows with distance, although it is reduced taking visual dataCheapCamera(s)It depends on camera(s)
Kalman-filter-basedBothUsually highThere is no inconve-nienceSmall error (redundant sources)Expensive (redundant sensors)It depends on fused sensorsYes, since it generally uses several redundant sources

Table 2.

Main characteristics of the localization techniques

In this work, we have analyzed different localization methods, in order to evaluate the most appropriate ones according to the activity of the robot. In order to achieve this objective, we have firstly studied the typical localization methods for the mobile robotics community and we discuss the advantages and disadvantages of these methods to our specific case.

The most popular solutions are wheel-based odometry and dead-reckoning (Borenstein & Feng, 1996). These techniques can be considered as relative or local localization. They are based on determining incrementally the position and orientation of a robot from an initial point. In order to provide this information, it uses various on-board sensors, such as encoders, gyroscopes, accelerometers, etc. The main advantage of wheel-based odometry is that it is a really straightforward method. The main drawback is, above all, an unbounded growth of the error along time and distance, particularly in off-road slip conditions (González, 2011).

We have also analyzed global or absolute localization techniques, which determine the position of the robot with respect to a global reference frame (Durrant-Whyte & Leonard, 1991), for instance using beacons or landmarks. The most popular technique is GPS-like solutions such as Differential GPS (DGPS) and Real-Time Kinematics GPS (RTK-GPS). In this case, the error growth is mitigated and the robot position does not depend on time and initial position. The main problems in relation to GPS are a small accuracy of data (improved using DGPS and RTK-GPS) and the signal is lost in closed spaces (Lenain et al., 2004). Other solutions such as artificial landmarks or beacons require a costly installation of the markers on the area where the robot operates.

On the other hand, there are some localization techniques based on visual information (images). One of the most extended approaches is visual odometry or Ego-motion estimation, which is defined as the incremental on-line estimation of robot motion from an image sequence (Nistér et al., 2006). It constitutes a straightforward-cheap method where a single camera can replace a typical expensive sensor suite, and it is especially useful for off-road applications, since visual information estimates the actual velocity of the robot, minimizing slip phenomena (Angelova et al., 2007).

Finally, probabilistic techniques based on estimating the localization of the mobile robot combining measurements from different data sources are becoming popular. The most extended technique is the Kalman filter (Thrun et al., 2005). The main advantage of these techniques is that each data source is weighted taken into account statistical information about reliability of the measuring devices and prior knowledge about the system. In this way, the deviation or error is statistically minimized.

Summing up, in Table 2 the considered localization methods for our social robot are presented. We also detail some key parameters to decide the most appropriate solution, depending on the task to be performed.

4. Modelling the localization system

In order to model the knowledge that the social robot needs to take decisions, we have analyzed the characteristics of the localization methods to decide the necessary parameters for the best selection in different environment conditions. Firstly, all available alternatives have been evaluated. Since it would be inefficient to implement all the methods in the robot, it is applied a first decision level in which the human experts select the methods that the social robot may need taking into account the scenarios to be found at the University. In this sense, we are considering a social mobile robot working at indoor and outdoor scenarios. The main purpose of this mobile robot is to guide to the people at our University, that means, the robot could guide a person inside a building (for instance, the library) or it could work outdoors between buildings.

We propose a two-level multi-agent architecture for knowledge modelling of the localization strategy. Fig. 5 shows a schema for this architecture. Firstly, the expert selected the most proper methods for the kind of activities that the robot has to make (move at the campus of the University of Almería). These localization methods were: wheel-based odometry since it is a straightforward method to estimate the robot position. This approach is especially used for indoor environments (like inside the library). On the other hand, for outdoor motions, the visual odometry approach and a DGPS-like solution are used. Finally, it is also considered to use a Kalman filter fusing data from visual odometry and DGPS.


Figure 5.

Schema for the proposed two-level multi-agent architecture

The first selection process (filter applied by the engineer) lets that the robot chooses only between useful and independent methods, according to the kind of activities to be accomplished by the mobile robot. In this way, redundant and useless localization methods will be avoided.

The second decision level of this architecture considers a general scheduler module implemented in the social robot. This planner is permanently running. When the robot has to take a decision (selecting an alternative among several options to accomplish a particular task) it calls to the scheduler agent. This agent uses the context information, the suitability criteria table and a dynamic cost function (depending on the scenario) to select the most appropriate localization method.

Some of the main advantages of this architecture are that the robot can choose the most appropriate localization method according to the surrounding environment and new decisions can be incorporated simply including its suitability criteria table.

Fig. 6 shows the lower-level TMD elements, simplified to four testing alternatives of localization. This is a branch of the most general navigation subsystem TMD (Guirado et al., 2009).

DSM is applied to choose the most efficient method using an aggregation function that integrates the suitability criteria and the weights to generate a suitability value for each method. In our particular case, the criteria for decision-making are Computing Time (CT), GPS-Signal Necessity (GN), Luminosity (L), Fault-Tolerance (FT) and Precision (P). These criteria are related to the method characterization done in the previous section. CT, L, FT and P are directly considered in the Table 2, while GN is related to the Indoor/Outdoor and Sensors method parameters. The economic Cost of implementation is used by the expert in the first decision level in order to choose the methods to be implemented in the robot, but it does not make sense to use it as a suitability criterion for selecting the best alternative method among those that are implemented in the robot.


Figure 6.

Representation of a TMD for a pre-filtered localization system

CT is inversely proportional to the execution time of each method, favouring the faster method to calculate the exact position of the robot. We have considered this criterion because some instances need a fast response and it is necessary to use the fastest algorithm. CT is considered a non-critical (N) and static (S) criterion that means it is not used to discard any alternative method and its value is considered fixed for each method because the variations in testing are minimal.

GN indicates if a method needs a good GPS signal to be considered in the selection process. This criterion is critical (C) only for the DGPS-based method because the robot cannot apply it if the received signal to get the position is low (less than 4 satellite signals). The other methods do not use the GN criterion because they do not use the GPS data; so, it is convenient or non-critical (N) for those methods. The criterion is dynamic (D) for all the methods, taking values 0 or 1 for DGPS-based method, and values between 1 and 5 for the rest.

L represents the intensity of the light in the place where the robot is. If the luminosity is low, algorithms that require the use of conventional cameras for vision cannot be used. This is a dynamic (D) criterion since the robot must operate in places more or less illuminated with natural or artificial light. So, the value of this criterion is changing and its value is discretized between 1 and 5. As this criterion does not exclude any method in the selection process, it is considered non-critical (N). Notice that, in our case, luminosity is obtained analyzing the histogram of an image.

FT is a parameter that indicates if the robot system is able to continue operating, possibly at a reduced level, rather than failing completely, when the applied method fails. This criterion is static (S) for each method. Its values have been obtained from our experiences. As in the previous criterion, this is also considered non-critical (N).

P is related to the accuracy of the sensor data that each method uses. It has a dynamic (D) value because the environment conditions are changing. For instance, GPS signal quality is fine in an open area; therefore, the precision of DGPS-based method is high. This is another non-critical (N) criterion because it does not discard any method by itself.

As previously explained, the human expert has chosen four localization methods in the first decision level. These alternatives are wheel-based odometry (O), DGPS-based (G), Kalman-filter-based (K) and visual odometry (V); each of them has assigned a set of suitability criteria.

The cost function considers the criteria with their associated weights,

SM= GNM() * (+ WCT* CTM+ WL* LM+ WFT* FTM+ WP* PM)

The weights (Wi) are dynamic functions, so they can change depending on environment and performance requirements.

The function for the critical criterion GN is defined as follow.


So, it can only be equal to 0 for the DGPS-based method, and the GPS signal must also be insufficient.

The description of the elements (tasks, methods and inferences) has been represented using the CML notation, as CommonKADS methodology proposes (Schreiber et al., 1999). Here is an example for the localization task:


Each selection criterion has two letters in front of his name. The first one is the severity of the criterion, where N indicates non-critical and C indicates critical, and the second one is if the criteria can change or not, using D for dynamic and S for static.

5. Results

The proposed methodology was tested through several physical experiments showing how the robot applies the knowledge model-based architecture using the suitability criteria values (depending on the environmental conditions) to select the appropriate method in every moment.

In this section, we analyze the proposed methodology in a real scenario. Our real case has been that the mobile robot has guided a person at our University (see Fig. 7) from the bus stop (start) to the library (goal). Firstly, the visitor tells the robot to guide him to the library. In this case, the user used the touch screen. Then, the mobile robot calculated the optimal route according to several parameters (we are not detailing it here). The solution of this stage was the line marked in Fig. 7 (left). The mobile robot is moving at 0.5 m/s with a sampling time of 0.2 s. In order to avoid sudden transitions from one method to another, due to sensor noises and disturbances, we have tuned a filter, where a decision will not be taken until a method is not selected 10 consecutive times.

In this case, the robot moves through four areas along the trajectory. The path labelled with “a” is a wide-open space. The path labelled with “b” is a narrow way with some trees. Finally, the path labelled with “c” is open space but close to buildings. Notice that the robot moved on a pavement terrain, which leads to slip phenomena, is not expected. The real trajectory followed by the robot is shown in Fig. 7 (right); note that the x-axis has a different scale from y-axis in the plot.


Figure 7.

Real scenario (University map) and followed trajectory. The mobile robot has guided a person from bus stop (start) to the library (goal)

As previously explained, the GN criterion is critical for the DGPS-based method. This means that method is not selectable if GPS signal is insufficient (less than 4 satellites available). So, we represent in Fig. 8 the number of satellites detected by the GPS justifying the necessity to use other alternatives localization methods in some trajectory paths.

CT and FT are static criteria and so they have the same values in all situations, since they are related to independent characteristics of the environment (CTO=5, CTG=2, CTK=1, CTV=4, FTO=2, FTG=4, FTK=5 and FTV=3). Other criteria (GN, L and P) are dynamic, that means they can change depending on the environment conditions.


Figure 8.

GPS signal during the robot travel

In the first area (“a”), the GN and L criteria was equal for all methods, since all of them could be used without problems in current conditions. In addition, the robot initially considered the same weights for all criteria (WCT = WL = WFT = WP = 0.25). Applying the cost function, robot obtained the following suitability values for each method:

SO= 1 * (+ 0.25 * 5 + 0.25 * 5 + 0.25 * 2 + 0.25 * 2) = 4.5
SG= 1 * (1 + 0.25 * 2 + 0.25 * 5 + 0.25 * 4 + 0.25 * 5) = 5
SK= 1 * (+ 0.25 * 1 + 0.25 * 5 + 0.25 * 5 + 0.25 * 3) = 4.5
SV= 1 * (+ 0.25 * 4 + 0.25 * 5 + 0.25 * 3 + 0.25 * 3) = 4.75

As expected, robot used the DGPS-based localization method, since it obtains the larger suitability value. Notice in Fig. 8 that there are more than three satellites available during this path.

In the second area (“b”), the GN and L criteria remained the same for all methods. Factors for P criterion changed for some methods with respect to the previous area. The GPS signal was frequently lost due to the trees and the error increased considerably (see Fig. 8). In addition, the user increased the velocity of the robot, which led to give a higher weight to TC criterion, keeping a constant value for the other (WCT = 0.4; WL = WFT = WP = 0.2). These were the obtained suitability values for each method:

SO= 1 * (+ 0.* 5 + 0.* 5 + 0.* 2 + 0.* 2) = 4.8
SG= 0 * (+ 0.* 2 + 0.* 5 + 0.* 4 + 0.* 2) = 0
SK= 1 * (+ 0.* 1 + 0.* 5 + 0.* 5 + 0.* 3) = 4
SV= 1 * (1 + 0.4 * 4 + 0.2 * 5 + 0.2 * 3 + 0.2 * 4) = 5

The selected method was visual odometry. DGPS method got a low suitability value due to “b” was a cover area (trees) and the GPS signal was temporary lost (see Fig. 8).

In the third area (“c”), the GN and L criteria remained the same for all methods. Factors for the P criterion slightly changed from the previous area (GPS signal was slightly better since there were not trees, although still affected by the proximity to the buildings). The user reduced the velocity of the robot, and it led to reduce the weight of the TC criterion, keeping a constant value for the other (WCT = 0.1; WL = WFT = WP = 0.3). The obtained suitability values were:

SO= 1 * (+ 0.* 5 + 0.* 5 + 0.* 2 + 0.* 2) = 4.2
SG= 1 * (+ 0.* 2 + 0.* 5 + 0.* 4 + 0.* 3) = 4.8
SK= 1 * (1 + 0.1 * 1 + 0.3 * 5 + 0.3 * 5 + 0.3 * 3) = 5
SV= 1 * (+ 0.* 4 + 0.* 5 + 0.* 3 + 0.* 3) = 4.7

The Kalman-filter-based obtained the larger suitability value since “c” was an open area where DGPS and visual odometry work fine.

In the last area (inside the library), the GN criterion was zero for the DGPS-based method, since the signal was completely lost; moreover, the L criterion decreased slightly for visual odometry method due to changing light conditions. When the robot goes inside the library, it considers the same weights for all criteria again (WCT = WL = WFT = WP = 0.25). The obtained suitability values were:

SO= 1 * (1 + 0.25 * 5 + 0.25 * 5 + 0.25 * 2 + 0.25 * 3) = 4.75
SG= 0 * (+ 0.25 * 2 + 0.25 * 5 + 0.25 * 4 + 0.25 * 1) = 0
SK= 1 * (+ 0.25 * 1 + 0.25 * 5 + 0.25 * 5 + 0.25 * 3) = 4.5
SV= 1 * (+ 0.25 * 4 + 0.25 * 4 + 0.25 * 3 + 0.25 * 3) = 4.5

Finally, as expected, when the mobile robot guided to the person inside the library, wheel-based odometry method obtained the larger suitability value.

Fig. 9 shows the average values during the experiment for the localization methods. This information has been used in the test of the proposed methodology.

6. Conclusions and future works

The main objective of this work is to take a further step in developing a generic and flexible decision mechanism to select the most proper localization algorithm for a social robot. We present the preliminary results for a single decision between four alternatives (selected by the human expert in the first decision level). More tests will be performed within the same operating environment in the future.

The main advantages of the proposed architecture are to facilitate further addition of new algorithms that could be developed in the future and the capacity of deciding in real-time the most appropriate technique to be used in the current conditions.

From a practical point of view, and according to our physical experiments, the proposed methodology permits to successfully guide users at our university by choosing the best localization method taking into account the surrounding environment.


Figure 9.

Average suitability values for the localization methods in every path (“a”, “b”, “c”, “d”)

Here we have applied a direct DSM that means the best method is the one with the highest suitability value (or one of them if there is more than one), but we are considering to incorporate fuzzy logic to the cost function and to apply other types of membership functions to the DSM.

In order to follow evaluating the proposed mechanisms of DSM in robotics, we are extending the use of these techniques to other social robot tasks. The final goal is to build an ontology in the domain of social robotic.


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