A Hybrid Path Planning Method in Unmanned Air/Ground Vehicle (UAV/UGV) Cooperative Systems

TLDR

The study investigates automatic ground map construction and efficient path planning for UAV/UGV cooperative systems. Ground images captured by the UAV are denoised, corrected, and processed for obstacle recognition to build a map, which feeds a hybrid planner that uses a genetic algorithm for global search and a rolling local optimization to refine the path, with experiments evaluating performance. The hybrid planner produces significantly cheaper, real‑time paths than traditional genetic‑algorithm or A‑star methods, enabling effective UAV/UGV cooperation.

Abstract

In this paper, we study the automatic ground map building and efficient path planning in unmanned aerial/ground vehicle (UAV/UGV) cooperative systems. Using the UAV, a ground image can be obtained from the aerial vision, which is then processed with image denoising, image correction, and obstacle recognition to construct the ground map automatically. Image correction is used to help the UGV improve the recognition accuracy of obstacles. Based on the constructed ground map, a hybrid path planning algorithm is proposed to optimize the planned path. A genetic algorithm is used for global path planning, and a local rolling optimization is used to constantly optimize the results of the genetic algorithm. Experiments are performed to evaluate the performance of the proposed schemes. The evaluation results show that our proposed approach can obtain a much less costly path compared to the traditional path planning algorithms such as the genetic algorithm and the A-star algorithm and can run in real-time to support the UAV/UGV systems.