Generating Reactive Robots' Behaviors using Genetic Algorithms

In this paper, we analize and benchmark three genetically-evolved reactive obstacle-avoidance behaviors for mobile robots. We buit these behaviors with an optimization process using genetic algorithms to find the one allowing a mobile robot to best reactively avoid obstacles while moving towards its destination. We compare three approaches, the first one is a standard method based on potential fields, the second one uses on finite state machines (FSM), and the last one relies on HMM-based probabilistic finite state machines (PFSM). We trained the behaviors in simulated environments to obtain the optimizated behaviors and compared them to show that the evolved FSM approach outperforms the other two techniques.

[1]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[2]  Gregory J. Barlow,et al.  Article in Press Robotics and Autonomous Systems ( ) – Robotics and Autonomous Systems Fitness Functions in Evolutionary Robotics: a Survey and Analysis , 2022 .

[3]  Mohamed Tayeb Laskri,et al.  The path planning of cleaner robot for coverage region using Genetic Algorithms , 2016, J. Innov. Digit. Ecosyst..

[4]  D.B. Fogel,et al.  Nils Barricelli - artificial life, coevolution, self-adaptation , 2006, IEEE Computational Intelligence Magazine.

[5]  Dario Floreano,et al.  Active vision and feature selection in evolutionary behavioral systems , 2002 .

[6]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[7]  Alex Bateman,et al.  An introduction to hidden Markov models. , 2007, Current protocols in bioinformatics.

[8]  Friedrich Fraundorfer,et al.  Visual Odometry Part I: The First 30 Years and Fundamentals , 2022 .

[9]  HardwareHo-Sik Seok.  An On-Line Learning Method for Object-Locating Robots using Genetic Programming on Evolvable Hardware , 2007 .

[10]  Jean-Claude Latombe,et al.  Robot Motion Planning with Uncertainty in Control and Sensing , 1991, Artif. Intell..

[11]  Alexander Zelinsky,et al.  Mobile Robot Navigation based on localisation using Hidden Markov Models. , 1998 .

[12]  António Abreu.  Robot localization from minimalist inertial data using a Hidden Markov Model , 2014, 2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[13]  Wolfgang Banzhaf,et al.  Generating Adaptive Behavior using Function Regression within Genetic Programming and a Real Robot , 1997 .

[14]  Phil Husbands,et al.  Evolutionary robotics , 2014, Evolutionary Intelligence.

[15]  Ambuja Salgaonkar,et al.  Probabilistic Approach to Robot Motion Planning in Dynamic Environments , 2020, SN Comput. Sci..

[16]  Lukas König.  Complex Behavior in Evolutionary Robotics , 2015 .

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

[18]  J. Savage,et al.  A Motion-Planning System for a Domestic Service Robot , 2018, SPIIRAS Proceedings.

[19]  Luis Contreras,et al.  Map representation using hidden markov models for mobile robot localization , 2018 .

[20]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[21]  Sanjiv Kumar,et al.  Accelerating Large-Scale Inference with Anisotropic Vector Quantization , 2019, ICML.