A Fuzzy Action Selection Method for Virtual Agent Navigation in Unknown Virtual Environments

This paper presents an action selection method using fuzzy logic. The objective is to solve behaviour conflict in behaviour-based architectures for virtual agent navigation in unknown virtual environments. Two main problems have been identified: how to decide which behaviour should be activated at each instant; and how to combine the results from different behaviours into one action. The method uses fuzzy alpha-levels to compute behaviour weight for each behaviour and the final action is selected using the Huwicz criterion. The results clearly demonstrate the mapping of inputs to output with a near-optimum path in every navigation task.

[1]  Jafreezal Jaafar,et al.  Behaviour Coordination of Virtual Agent Navigation using Fuzzy Logic , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[2]  Ronald C. Arkin,et al.  Temporal coordination of perceptual algorithms for mobile robot navigation , 1994, IEEE Trans. Robotics Autom..

[3]  Yufei Yuan.  Criteria for evaluating fuzzy ranking methods , 1991 .

[4]  Wei Li,et al.  Fuzzy-logic-based reactive behavior control of an autonomous mobile system in unknown environments , 1994 .

[5]  Mark Humphreys,et al.  Action selection methods using reinforcement learning , 1997 .

[6]  Maja J. Mataric,et al.  A decision-theoretic approach to fuzzy behavior coordination , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).

[7]  Homayoun Seraji,et al.  Behavior-based robot navigation on challenging terrain: A fuzzy logic approach , 2002, IEEE Trans. Robotics Autom..

[8]  Fang Wang,et al.  A multi-agent based evolutionary artificial neural network for general navigation in unknown environments , 1999, AGENTS '99.

[9]  Bakir Lacevic,et al.  A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches , 2006, Robotics Auton. Syst..

[10]  V. Scott Gordon,et al.  Evolving sparse direction maps for maze pathfinding , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[11]  G. Bekey,et al.  An architecture for behaviour coordination learning , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[12]  Tsong Yueh Chen,et al.  On the criteria of allocating test cases under uncertainty , 1997, Proceedings of Joint 4th International Computer Science Conference and 4th Asia Pacific Software Engineering Conference.

[13]  Robert A. Ghanea-Hercock,et al.  Disturbed behavior in co-operating autonomous robot , 1999, AGENTS '99.

[14]  A. Safiotti,et al.  Fuzzy logic in autonomous robotics: behavior coordination , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[15]  Meng Wang,et al.  Autonomous robot navigation using fuzzy logic controller , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[16]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1986 .

[17]  Shigeaki Mabuchi,et al.  An approach to the comparison of fuzzy subsets with an α-cut dependent index , 1988, IEEE Trans. Syst. Man Cybern..

[18]  Rae A. Earnshaw,et al.  Real-time path planning for navigation in unknown environment , 2003, Proceedings of Theory and Practice of Computer Graphics, 2003..

[19]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[20]  Maja J. Matarić,et al.  Behavior-Based Robotics , 1999 .

[21]  Henrik I. Christensen,et al.  Behavior coordination using multiple-objective decision making , 1997, Other Conferences.

[22]  Miguel Lozano,et al.  A neural approach to an attentive navigation for 3D intelligent virtual agents , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[23]  Daniel Thalmann,et al.  Behavioural Animation of Autonomous Virtual Agents Helped by Reinforcement Learning , 2003, IVA.

[24]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[25]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[26]  Edward Tunstel,et al.  Behavior Hierarchy for Autonomous Mobile Robots: Fuzzy-Behavior Modulation and Evolution , 1997, Intell. Autom. Soft Comput..

[27]  John Yen,et al.  A fuzzy logic based extension to Payton and Rosenblatt's command fusion method for mobile robot navigation , 1995, IEEE Trans. Syst. Man Cybern..

[28]  Alessandro Saffiotti.  Fuzzy logic in Autonomous Robot Navigation - a case study , 1997 .

[29]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[30]  Xiong Chen,et al.  Path planning approach based on probabilistic roadmap for sensor based car-like robot in unknown environments , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[31]  Alessandro Saffiotti.  Autonomous robot navigation , 1998 .

[32]  Masafumi Hagiwara,et al.  A simple path planning system using fuzzy rules and a potential field , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[33]  Peter Stahlecker,et al.  The Minimax, the Minimin, and the Hurwicz Adjustment Principle , 2002 .

[34]  P. Maes.  How to Do the Right Thing , 1989 .

[35]  Maja J. Matarić,et al.  Behavior-Based Coordination in Multi-Robot Systems , 2018, Autonomous Mobile Robots.

[36]  Dinesh Manocha,et al.  Interactive navigation in complex environments using path planning , 2003, I3D '03.

[37]  F. Choobineh,et al.  An index for ordering fuzzy numbers , 1993 .