Pavel Nahodil, Michal Petrus, Václav Svatoš Probabilistic aspects of behavioural control Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague Technická 2, 166 27 Prague, Czech Republic Pavel Nahodil, Michal Petrus, Václav Svatoš INTRODUCTION BEHAVIOURAL INFLUENCE SIGNIFICANCE Many tasks in the robot control schedule some navigation in unarranged environment. Their intelligence is independent on sophisticated understanding. The reactive control demonstrates such solution of the problem in the bottom-up way. We deal in the taxe movement case. Our approach also utilise behavioural features of control system (see Fig.1). These ones are inspirated by insect seeking behaviour using gradient attributes of luminescence, pheromone, or another sign substance. The behaviour attributes makes another quality in the temporal evaluation of any instant situation. We talk about internal motivations that play excitating and suppresive role at once. The internal rules stimulate partial sub-goals to its satisfying. The experiments were evidenced a relevance of the behaviour-based method (space cover task) and gradient one (phototaxis) in the robot control. The methods use environment as actual representant of the world without internal map. Therefore, the application depends on the initial arrangenment of the workspace namely. The internal motivations are integrated from history of actual actions and internal sub-goals (system experience) including explicit motivations (system setup). The actions are encoded to the same basal ways as the sensor representants in this concept. Benefit of behavioural stimulation improved orientation to goal at long time horizon atractor and repelor evaluation links from input to motivation Benefit of hototaxis reduces number of the considerous states reflects only instant relations to significant goals in the environment allows to fast react to dynamic changes Benefit of probabilistic processing probabilistic measure of affect input into motivation eliminates a low sensitivity caused by small diversity of input signal the selection mechanism operates with higher uncertainty Fig. 1 Interface definition of control system T ACTION COMMAND NOTICE CONTROL SYSTEM LUMINESCENCE SENSOR ENVIRONMENT STEP MOTOR GUIDE IR PROXIMITY SENSOR EXISTENCE INTENSITY The advanced behavioural synthesis also shows an expert capability of the relevance weighting by its possibility. Frontal movement -1 Side movement 1 Fig. 3 An action mapping example Forward Backward Right Left Corresponding homogenous areas cover a map of the basic actions (see Fig. 3). The rating is similar to fuzzy grade of desiarability of action. Behavioural part activity appetence and motivation behavioral estimation Reactive part environmental feedback critical actions providing The system includes: The distribution of fields in the map of the basic actions depends on the practice or an improved adaptation. We prefere a reinforcement learning to any activity cluster. The gradient prereqisity was approved in our experiments as well. The behavioural trends does not performed in the brief experiments yet. SUITABLE METHODS SELECTION MECHANISM RELATED METHODS Our alternative decision omits a knowledge synthesis based on the geometric interpretation of the workspace. We prefer an instant perception of the local neighbourhood [1]. Such processing is based on a functional decomposition that reflect a specific occasion. Singular inputs and internal stimuli are competed in the following order flow: The proposed approach could be itemized in the comparation with above-considered methods as follows: Fig. 4. Schema of the behavioural control system Fuzzy command fusion comprehensive design of rules multi-valued input rules to compositon of basic axction available only for continuous integrity inputs Probabilistic decision powerful reduction of computational complexity uncertainty messure injection Global potential field reliability in boundary workspace requirements of known location of objects and their importance less learning capabilities Deliberative product planning complex behaviour realization time consument and computation complexity requirements of explicit knowledge about envioronment Behavioural set adaptation learning in process of activity uncertainty activity injection + This approach utilizes following domains: + Signal standartization unification of various sort of input signal input range equalizer superposition principle Probabilistic model of the reactive base evaluation of input data reliability quality messure of input data believness messure Behavioural motivations collecting of external stimuli into motivations integration of motivation tendences evaluation of action reliability Fuzzy-based command fussion input data fashion actions evolving + + The reactive rule was yet encoded by the reactictive representation to potential field. Now, we select the most preferred action throug assignment of extreme in the field. Proabilistic aspects of selection mechanism fuzzy assignement between input and action improving quality and sensitivity in the input channel exact decision + The probabilistic estimation works in the probabilistic field, which is construed at the same level as the potential field. + REACTIVE REPRESENTATION EXPERIMENTS REFERENCES We process two principal sort of robot incoming information: Existence pure reactive stimuli binary values (infra-red proximity detection) Intensity advanced knowledge for reactive decision continuous range of input values Proposed system architecture was tested on the mobile robot platform[3]. There were achieved experiments focused on gradient-based navigation in unknown enviroment. Implemented robot’s behaviour embodies reactive character. The executed actions was educed from local environmental information without design of internal representation. [1] Brooks, R. A.: Intelligence without Representation. In: Artificial Intelligence, Vol. 47, pp.139-160. MIT Press, 1991 [2] Starý, J.: Rozšířený HW pro mobilního robota. Diploma work, CTU Prague 2000. In czech. [3] Kurzveil, J - Maixner, V - Svatoš, V.: Autonomous Mobile Robot Platform. In: Proceeding of 3th International Student Conference on Electrical Engineering "Poster 1999", p. IC 26, Prague, May 1999. [4] Khatib, O.: Real-time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotic Research, Vol.5 No.1, pp.90-98, 1986. [5] Balch, T.: Behavioral Diversity in learning Robot Teams. Doctoral thesis, Georgia Institute of Technology, December 1998. [6] Large, E. W. - Christensen, H. I. - Bajcsy, R.: Scaling the Dynamic Approach to Path Planning and Control: Competition among Behavioural Constraints. The International Journal of Robotic Research, Vol.18 No.1, pp.37-58, 1999. These factors take a potential field representation [4] by its goal rate: Attractor representant goal reinforcement stimuli motivation rate Repelor representant conflicts against goal suppresive rate (see Fig.2) Fig. 5. The robot trace in the light labyrint Fig. 2 Repelors in frontal detection case Rate Frontal movement Side movement -1 1 lunminescence sensor (light intensity related to movement rate) [2] infra-red bumpers (existence to behavioural reduction) beacon extetion (guide notice/alert) Robot equipement This research has been supported by the Ministry of Education, Youth and Sports of Czech Republic. GRANT FRVŠ No.23-20001-333