An Effective Dynamic Path Planning Approach for Mobile Robots Based on Ant Colony Fusion Dynamic Windows

To further improve the path planning of the mobile robot in complex dynamic environments, this paper proposes an enhanced hybrid algorithm by considering the excellent search capability of the ant colony optimization (ACO) for global paths and the advantages of the dynamic window approach (DWA) for local obstacle avoidance. Firstly, we establish a new dynamic environment model based on the motion characteristics of the obstacles. Secondly, we improve the traditional ACO from the pheromone update and heuristic function and then design a strategy to solve the deadlock problem. Considering the actual path requirements of the robot, a new path smoothing method is present. Finally, the robot modeled by DWA obtains navigation information from the global path, and we enhance its trajectory tracking capability and dynamic obstacle avoidance capability by improving the evaluation function. The simulation and experimental results show that our algorithm improves the robot's navigation capability, search capability, and dynamic obstacle avoidance capability in unknown and complex dynamic environments.

Automated summary

This paper proposes an enhanced hybrid algorithm that combines the advantages of ant colony optimization (ACO) and dynamic window approach (DWA) to further improve the path planning of mobile robots in complex dynamic environments. By establishing a new dynamic environment model, improving the traditional ACO algorithm, designing a strategy to solve the deadlock problem, and enhancing the trajectory tracking and dynamic obstacle avoidance capabilities, the algorithm has been proven effective in improving the robot's navigation capability, search capability, and dynamic obstacle avoidance capability in unknown and complex dynamic environments.