Abstract
This paper focuses on the problem that the current path planning algorithm is not mature enough to achieve the expected goal in a complex dynamic environment. In light of the ant colony optimization (ACO) with good robustness and strong search ability, and the rolling window method (RWM) with better planning effect in local path planning problems, we propose a fusion algorithm named RACO that can quickly and safely reach the designated target area in a complex dynamic environment. This paper first improves the ant colony optimization, which greatly improves the convergence performance of the algorithm and shortens the global path length. On this basis, we propose a second-level safety distance determination rule to deal with the special problem of the research object encountering obstacles with unknown motion rules, in order to perfect the obstacle avoidance function of the fusion algorithm in complex environments. Finally, we carry out simulation experiments through MATLAB, and at the same time conduct three-dimensional simulation of algorithm functions again on the GAZEBO platform. It is verified that the algorithm proposed in this paper has good performance advantages in path planning and dynamic obstacle avoidance.
Highlights
With the development and progress of the discipline, the application areas of the path planning problem have gradually expanded, becoming a key part of technological innovation and upgrading
This paper combines the two algorithms and proposes a fusion algorithm based on the improved ant colony algorithmdynamic window method (DACO), which enables mobile robots to achieve autonomous obstacle avoidance in a dynamic environment in view of obtaining global information
Based on the above experimental analysis, we conclude that under the condition that the global optimal path is known, the dynamic window method combined with the obstacle avoidance strategy proposed in this paper can realize the path planning function in the complex environment, which verifies the effectiveness of the algorithm in this paper
Summary
With the development and progress of the discipline, the application areas of the path planning problem have gradually expanded, becoming a key part of technological innovation and upgrading. In order to solve the problem of convergence and local optimal solution of traditional planning methods in complex environment, Han et al (2018) improved the planning ability of the algorithm by combining repulsive potential field with particle swarm optimization. Local path planning algorithms mainly include artificial potential field method and dynamic window method. Q.Jin et al:Name:Research on Dynamic Path Planning Based on the Fusion Algorithm of Improved Ant Colony Optimization and Dynamic Window Method problem. The fusion algorithm of path planning has a good prospect and is very necessary in solving dynamic and complex environmental problems.
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