PSO optimization of mobile robot trajectories in unknown environments

The Canonical Force Field (CF2) method is an approach of mobile robot path planning. The variations of CF2 parameters P, c, k, Q and ρ0 are however vital to its performance. In this paper, we used the multi-objective particle swarm optimization (PSO) approach to optimize these parameters. The computation of the optimal parameters is restarted in each new position of the robot. PSO is used to minimize the distance between this position and the target and to maximize the safe distance between this position and the obstacles. The effectiveness of the method is demonstrated by computer simulations in the Webots environment. Simulations are carried out in various known and unknown environments. In the known environments, the obstacle position is recognized by the robot at the beginning of navigation and the path planning is global. But in the unknown environments, the robot localization is based on the sensor readings and the path planning is local.

[1]  Reid G. Simmons,et al.  The curvature-velocity method for local obstacle avoidance , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[2]  Gamini Dissanayake,et al.  A Variable Speed Force Field Method for Multi-Robot Collaboration , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  D Wang,et al.  A generic force field method for robot real-time motion planning and coordination , 2009 .

[4]  Yoram Koren,et al.  The vector field histogram-fast obstacle avoidance for mobile robots , 1991, IEEE Trans. Robotics Autom..

[5]  D.K. Liu,et al.  A Subgoal-Guided Force Field Method for Robot Navigation , 2008, 2008 IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications.

[6]  Ellips Masehian,et al.  Multi-objective robot motion planning using a particle swarm optimization model , 2010, Journal of Zhejiang University SCIENCE C.

[7]  Gamini Dissanayake,et al.  An efficient strategy for robot navigation in cluttered environments in the presence of dynamic obstacles , 2007 .

[8]  Nadia Adnan Shiltagh,et al.  Optimal Path Planning For Intelligent Mobile Robot Navigation Using Modified Particle Swarm Optimization , 2013 .

[9]  Hung-Yuan Chung,et al.  Automatic Navigation of a wheeled mobile robot using Particle Swarm Optimization and Fuzzy Control , 2011, 2013 IEEE International Symposium on Industrial Electronics.

[10]  Wolfram Burgard,et al.  Controlling synchro-drive robots with the dynamic window approach to collision avoidance , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[11]  B. B. V. L. Deepak,et al.  Target seeking behaviour of an intelligent mobile robot using advanced particle swarm optimization , 2013, 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE).

[12]  B. B. V. L. Deepak,et al.  PSO based path planner of an autonomous mobile robot , 2012, Central European Journal of Computer Science.

[13]  Hamid Haj Seyyed Javadi,et al.  Using Particle Swarm Optimization for Robot Path Planning in Dynamic Environments with Moving Obstacles and Target , 2009, 2009 Third UKSim European Symposium on Computer Modeling and Simulation.

[14]  G. Dissanayake,et al.  A Force Field Method Based Multi-Robot Collaboration , 2006, 2006 IEEE Conference on Robotics, Automation and Mechatronics.

[15]  Mohamed Chtourou,et al.  PSO-CF2: A new method for the path planning of a mobile robot , 2015, 2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15).

[16]  Saeed Bagheri Shouraki,et al.  Optimization of dynamic mobile robot path planning based on evolutionary methods , 2015, 2015 AI & Robotics (IRANOPEN).

[17]  Dalong Wang,et al.  Ranked Pareto Particle Swarm Optimization for Mobile Robot Motion Planning , 2009 .

[18]  Peter J. Bentley,et al.  Perceptive particle swarm optimisation: an investigation , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

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

[20]  Gamini Dissanayake,et al.  PSO-Tuned F2 method for multi-robot navigation , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.