Integer GA for mobile robot path planning with using another GA as repairing function

In this study integer genetic algorithm is applied for path planning of mobile robot in the grid form environment. The novel representation is proposed for definition of chromosome which reduced the computational complexity of genetic algorithm which was used before for path planning. Comparison with other encoding of chromosome is done to show the capability of proposed algorithm. Another genetic algorithm is used to repair some paths which collide with obstacles. Mamadani fuzzy rule is used to describe difficulty of passing from cells which are sandy or have slope.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[4]  Zvi Shiller,et al.  Optimal motion planning of autonomous vehicles in three dimensional terrains , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[5]  Yuval Davidor,et al.  Genetic Algorithms and Robotics - A Heuristic Strategy for Optimization , 1991, World Scientific Series in Robotics and Intelligent Systems.

[6]  Narendra Ahuja,et al.  Gross motion planning—a survey , 1992, CSUR.

[7]  Takanori Shibata,et al.  Intelligent motion planning by genetic algorithm with fuzzy critic , 1993, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[8]  Dinesh K. Pai,et al.  Multiresolution rough terrain motion planning , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[9]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[10]  Thierry Siméon,et al.  Motion planning on rough terrain for an articulated vehicle in presence of uncertainties , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[11]  John Smith,et al.  Genetic algorithms for adaptive motion planning of an autonomous mobile robot , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[12]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[13]  Byoung-Tak Zhang,et al.  An evolutionary method for active learning of mobile robot path planning , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[14]  Alonzo Kelly,et al.  Rough Terrain Autonomous Mobility—Part 2: An Active Vision, Predictive Control Approach , 1998, Auton. Robots.

[15]  Barry Brumitt,et al.  Framed-quadtree path planning for mobile robots operating in sparse environments , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[16]  Andreas Stafylopatis,et al.  Autonomous vehicle navigation using evolutionary reinforcement learning , 1998, Eur. J. Oper. Res..

[17]  Andreas C. Nearchou,et al.  Path planning of a mobile robot using genetic heuristics , 1998, Robotica.

[18]  Andreas C. Nearchou,et al.  Adaptive navigation of autonomous vehicles using evolutionary algorithms , 1999, Artif. Intell. Eng..

[19]  Kalyanmoy Deb,et al.  A genetic-fuzzy approach for mobile robot navigation among moving obstacles , 1999, Int. J. Approx. Reason..

[20]  Mo Jamshidi,et al.  Soft computing for autonomous robotic systems , 2000 .

[21]  Nobuyuki Kita,et al.  3D simultaneous localisation and map-building using active vision for a robot moving on undulating terrain , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[22]  Thierry Siméon,et al.  Motion generation for a rover on rough terrains , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

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

[24]  Ray Jarvis,et al.  Path planning for a mobile robot in a rough terrain environment , 2002, Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02..

[25]  Steven Dubowsky,et al.  Multi-Sensor Terrain Estimation for Planetary Rovers , 2003 .

[26]  Chia-Ju Wu,et al.  Fuzzy Motion Planning of Mobile Robots in Unknown Environments , 2003, J. Intell. Robotic Syst..

[27]  Shahriar Negahdaripour,et al.  Vision-based positioning and terrain mapping by global alignment for UAVs , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[28]  Simon Lacroix,et al.  High resolution terrain mapping using low attitude aerial stereo imagery , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[29]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[30]  Steven Dubowsky,et al.  High-speed hazard avoidance for mobile robots in rough terrain , 2004, SPIE Defense + Commercial Sensing.

[31]  G. Swaminathan Robot Motion Planning , 2006 .