Path Planning for Mobile Robot Based on Improved Bat Algorithm

Bat algorithm has disadvantages of slow convergence rate, low convergence precision and weak stability. In this paper, we designed an improved bat algorithm with a logarithmic decreasing strategy and Cauchy disturbance. In order to meet the requirements of global optimal and dynamic obstacle avoidance in path planning for a mobile robot, we combined bat algorithm (BA) and dynamic window approach (DWA). An undirected weighted graph is constructed by setting virtual points, which provide path switch strategies for the robot. The simulation results show that the improved bat algorithm is better than the particle swarm optimization algorithm (PSO) and basic bat algorithm in terms of the optimal solution. Hybrid path planning methods can significantly reduce the path length compared with the dynamic window approach. Path switch strategy is proved effective in our simulations.

[1]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[2]  Chaymaa Lamini,et al.  Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning , 2018 .

[3]  Sudipta Mukhopadhyay,et al.  A robust cooperative multi-robot path-planning in noisy environment , 2010, 2010 5th International Conference on Industrial and Information Systems.

[4]  Yuan Yang.  Application of PSO in robotic path planning , 2007 .

[5]  Min Xue,et al.  A multirobot target searching method based on bat algorithm in unknown environments , 2020, Expert Syst. Appl..

[6]  Han-ye Zhang,et al.  Path Planning for the Mobile Robot: A Review , 2018, Symmetry.

[7]  Jun Xie,et al.  Global Dynamic Path Planning Based on Fusion of A* Algorithm and Dynamic Window Approach , 2019, 2019 Chinese Automation Congress (CAC).

[8]  Anna Gorbenko,et al.  Multi-agent Path Planning , 2012 .

[9]  Volkan Sezer,et al.  A Hybrid Obstacle Avoidance Method: Follow the Gap with Dynamic Window Approach , 2017, 2017 First IEEE International Conference on Robotic Computing (IRC).

[10]  Ahmad Taher Azar,et al.  Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments , 2020, Sensors.

[11]  Zhenbang Xu,et al.  Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows , 2021, Applied Sciences.

[12]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[13]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[14]  Seifedine Kadry,et al.  On the Optimization of Dijkstra’s Algorithm , 2011 .

[15]  Manuel Beschi,et al.  Optimal Robot Motion Planning of Redundant Robots in Machining and Additive Manufacturing Applications , 2019 .

[16]  Urszula Boryczka,et al.  A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP , 2019, Entropy.

[17]  Anthony Stentz,et al.  The D* Algorithm for Real-Time Planning of Optimal Traverses , 1994 .