Fusion of improved RRT and ant colony optimization for robot path planning

Abstract

To address the issues of poor guidance at the beginning of the Ant Colony Optimization (ACO), non-smooth paths, and its tendency to fall into local optima, This paper proposes a path planning approach based on Rapidly-exploring Random Tree (RRT) and Ant Colony Optimization (ACO). Firstly, a sub-optimal trajectory produced by the improved RRT is used to create a differentiated pheromone distribution, guiding the initial direction of the ant colony. Secondly, dynamic strategies are introduced into the evaporation coefficient and heuristic factor, changing the weights according to the number of iterations to enhance the attraction of the target point to the ants. Then, a reward-punishment mechanism updates the pheromone to solve the local optima problem. Finally, a pruning optimization strategy removes redundant nodes, making the path smoother. Multiple simulation results confirm that the algorithm possesses good global search capabilities and robustness under various conditions.