A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments

Zhang, Kaihuai; Zahmatkesh, Mohsen; Stefanec, Martin; Arvin, Farshad; Hu, Junyan

Abstract

This paper proposes a real-time multi-robot navigation method that integrates the Rapidly-exploring Random Tree (RRT) algorithm with the improved Artificial Potential Field (APF) approach. Since traditional path planning methods often face problems such as generating non-smooth paths and inefficient obstacle avoidance in changing environments, the RRT algorithm is used for initial path planning to pass through obstacles. Aiming to obtain a smooth collision-free path, Catmull-Rom spline smoothing is then introduced, which smooths the initially obtained trajectory and ensures that the curvature of the trajectory remains continuous. By combining the improved APF method, networked robots can then achieve safe navigation and effective obstacle avoidance in dynamic environments. The effectiveness of the proposed RRT-APF method is verified by both simulations and hardware experiments using real micro unmanned aerial vehicles.

Citation

Zhang, K., Zahmatkesh, M., Stefanec, M., Arvin, F., & Hu, J. (2025, March). A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments. Presented at 2025 IEEE International Conference on Industrial Technology, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2025 IEEE International Conference on Industrial Technology
• Start Date: Mar 26, 2025
• End Date: Mar 28, 2025
• Acceptance Date: Jan 31, 2025
• Deposit Date: Mar 3, 2025
• Peer Reviewed: Peer Reviewed
• Public URL: https://durham-repository.worktribe.com/output/3670748
• Publisher URL: https://icit2025.ieee-ies.org/