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.
Highlights
IntroductionPath Planning for Mobile Robot BasedPath planning means that the robot searches in a state space using the distance transform or heuristics to find the path with the lowest cost from the initial state to the target state according to a certain performance index [1,2]
Path Planning for Mobile Robot BasedPath planning means that the robot searches in a state space using the distance transform or heuristics to find the path with the lowest cost from the initial state to the target state according to a certain performance index [1,2]
This paper proposes a global dynamic path planning method combining bat algorithm (BA) and dynamic window approach (DWA)
Summary
Path Planning for Mobile Robot BasedPath planning means that the robot searches in a state space using the distance transform or heuristics to find the path with the lowest cost from the initial state to the target state according to a certain performance index [1,2]. Its essence is to obtain the optimal or feasible solution under multiple constraints It is the application direction of autonomous mobile robots, and a core problem of multi-agent systems (MAS) [3,4]. The diversity of results is insufficient, and these algorithms can fall into a local optimum due to excessive “Greedy” For this reason, many bionic intelligence algorithms have been proposed and applied to the path planning of mobile robots. Many bionic intelligence algorithms have been proposed and applied to the path planning of mobile robots These algorithms usually do not rely on specific problems. The problem-solving process and final result are random on a certain level This provides the possibility of obtaining the optimal solution. Evaluation of the path can rely on the length and on other factors
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