Pseudo-Bacterial Potential Field Based Path Planner for Autonomous Mobile Robot Navigation

This paper introduces the pseudo-bacterial potential field (PBPF) as a new path planning method for autonomous mobile robot navigation. The PBPF allows us to obtain an optimal and safe path, in contrast to the classical potential field approach which is not suitable for path planning because it lacks a means of obtaining the optimal proportional gains. The PBPF uses the pseudo-bacterial genetic algorithm (PBGA) and a fitness function based on the potential field concepts to construct viable paths in dynamical environments to mostly result in the optimal path being obtained. Comparative experiments of sequential and parallel implementations of the PBPF for off-line and online in structured and unstructured conditions are presented; the results are contrasted with the artificial potential field (APF) method to demonstrate how the PBPF proposal overcomes the traditional method.

[1]  László T. Kóczy,et al.  A Novel Version of the Bacterial Memetic Algorithm with Modified Operator Execution Order , 2010 .

[2]  He Bing,et al.  A route planning method based on improved artificial potential field algorithm , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[3]  I. Paterega,et al.  Artificial evolution mechanisms in robot navigation , 2011, 2011 11th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM).

[4]  Kouhei Ohnishi,et al.  Collision detection and distance calculation for safe path planning based on mathematical programming methods , 1992, Proceedings of the 1992 International Conference on Industrial Electronics, Control, Instrumentation, and Automation.

[5]  Yan Meng,et al.  Special Issue on Evolutionary and Developmental Robotics [Guest Editorial] , 2010, IEEE Comput. Intell. Mag..

[6]  László T. Kóczy,et al.  Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms , 2009 .

[7]  Ellips Masehian,et al.  Classic and Heuristic Approaches in Robot Motion Planning A Chronological Review , 2007 .

[8]  T. Furuhashi,et al.  A study on fuzzy rules discovery using Pseudo-Bacterial Genetic Algorithm with adaptive operator , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[9]  Zhaodan Kong,et al.  A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance , 2010, J. Intell. Robotic Syst..

[10]  H. B. Bahar,et al.  Grid roadmap based real time path planning , 2011, The 17th International Conference on Automation and Computing.

[11]  Minrui Fei,et al.  An Optimized Method for Path Planning Based on Artificial Potential Field , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[12]  Tong Heng Lee,et al.  Application of evolutionary artificial potential field in robot soccer system , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[13]  Naoyuki Kubota,et al.  Path Planning in Probabilistic Environment by Bacterial Memetic Algorithm , 2012, IIMSS.

[14]  Petros A. Ioannou,et al.  New Potential Functions for Mobile Robot Path Planning , 2000 .

[15]  Jian Huang,et al.  Evolutionary artificial potential field method based manipulator path planning for safe robotic assembly , 2009, 2009 International Symposium on Micro-NanoMechatronics and Human Science.

[16]  Mohammad Pourmahmood Aghababa,et al.  3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles , 2012 .

[17]  Min Cheol Lee,et al.  Artificial potential field based path planning for mobile robots using a virtual obstacle concept , 2003, Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003).

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

[19]  Shidong Li,et al.  Efficient Path Planning Method Based on Genetic Algorithm Combining Path Network , 2010, 2010 Fourth International Conference on Genetic and Evolutionary Computing.

[20]  Jesus Savage,et al.  Local Autonomous Robot Navigation Using Potential Fields , 2008 .

[21]  Panagiotis Tsiotras,et al.  Multiresolution Motion Planning for Autonomous Agents via Wavelet-Based Cell Decompositions , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Alessandro Saffiotti,et al.  Navigating by stigmergy: A realization on an RFID floor for minimalistic robots , 2009, 2009 IEEE International Conference on Robotics and Automation.

[23]  Christian Scheurer,et al.  Path planning method for palletizing tasks using workspace cell decomposition , 2011, 2011 IEEE International Conference on Robotics and Automation.

[24]  Venkat Krovi,et al.  A Standardized Testing-Ground for Artificial Potential-Field based Motion Planning for Robot Collectives , 2006 .

[25]  Cao Qixin,et al.  An Evolutionary Artificial Potential Field Algorithm for Dynamic Path Planning of Mobile Robot , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Meng Rui,et al.  A Study on Soccer Robot Path Planning with Fuzzy Artificial Potential Field , 2010, 2010 International Conference on Computing, Control and Industrial Engineering.

[27]  Kai Zhang,et al.  An Efficient Stochastic Clustering Auction for Heterogeneous Robotic Collaborative Teams , 2013, J. Intell. Robotic Syst..

[28]  Tao Ju,et al.  Rapidly Exploring Random Tree Algorithm-Based Path Planning for Robot-Aided Optical Manipulation of Biological Cells , 2014, IEEE Transactions on Automation Science and Engineering.

[29]  Xiujuan Lei,et al.  Mobile Robot Path Planning with Complex Constraints Based on the Second-Order Oscillating Particle Swarm Optimization Algorithm , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[30]  Naoyuki Kubota,et al.  Bacterial memetic algorithm for offline path planning of mobile robots , 2012, Memetic Comput..

[31]  Erion Plaku.  Path planning with probabilistic roadmaps and co-safe linear temporal logic , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[32]  Peter A. Lindsay,et al.  A hierarchical conflict resolution method for multi-agent path planning , 2009, 2009 IEEE Congress on Evolutionary Computation.

[33]  Fabricio Ferrari.  A New Parameterized Potential Family for Path Planning Algorithms , 2009, Int. J. Artif. Intell. Tools.

[34]  Naoyuki Kubota,et al.  Path planning for mobile robots by bacterial memetic algorithm , 2011, 2011 IEEE Workshop on Robotic Intelligence In Informationally Structured Space.

[35]  Oscar Castillo,et al.  Ant colony test center for planning autonomous mobile robot navigation , 2013, Comput. Appl. Eng. Educ..

[36]  Ting Chen,et al.  An Obstacle Avoidance Method of Soccer Robot Based on Evolutionary Artificial Potential Field , 2012 .