AI Article Synopsis
- The bat algorithm traditionally suffers from slow convergence, low precision, and stability issues.
- To address these problems, an improved bat algorithm was developed, incorporating a logarithmic decreasing strategy and Cauchy disturbance for enhanced performance in mobile robot path planning.
- The results indicate that this improved approach outperforms the basic bat algorithm and particle swarm optimization, effectively reducing path length and demonstrating the efficacy of a path switch strategy in simulations.
Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272107 | PMC |
| http://dx.doi.org/10.3390/s21134389 | DOI Listing |
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