Work

Dynamic path planning for multiple unmanned ground vehicles based on improved dynamic window approach

Year: 2025

Type: article

Abstract: This study proposes an improved dynamic window approach for path planning and collision avoidance of multi-Unmanned Ground Vehicle (UGV) in dynamic environments. The proposed method enhances the Predictive Collision Avoidance Dynamic Window Approach (PCA-DWA) by incorporating the kinematic and geometric models of UGVs. Two nonlinear sampling methods, power function sampling and exponential function sampling, are introduced to replace the traditional linear sampling. Additionally, a priority strategy is implemented to further improve the algorithm’s performance. Simulation results and comparative analysis show that the proposed dynamic window approach successfully achieves path planning and collision avoidance for multiple UGVs. The nonlinear sampling methods offer higher control accuracy compared to traditional linear sampling, and the dynamic window approach with the priority strategy significantly enhances the overall efficiency of path planning. These conclusions validate the effectiveness and superiority of the proposed approach.

Source: Chinese Journal of Aeronautics

DOI: 10.1016/j.cja.2025.104007

Authors: Jianqing LI, Zilong LI, Jingjie XIE, Zhaohui SONG

Institutions: Hangzhou Dianzi University, Hong Kong Polytechnic University

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,000