A study on unmanned combat vehicle path planning for collision avoidance with enemy forces in dynamic situations

Abstract

Abstract This study focuses on the path planning problem for unmanned combat vehicles (UCVs), where the goal is to find a viable path from the starting point to the destination while avoiding collisions with moving obstacles, such as enemy forces. The objective is to minimize the overall cost, which encompasses factors like travel distance, geographical difficulty, and the risk posed by enemy forces. To address this challenge, we have proposed a heuristic algorithm based on D* Lite. This modified algorithm considers not only travel distance but also other military-relevant costs, such as travel difficulty and risk. It generates a path that navigates around both fixed unknown obstacles and dynamically moving obstacles (enemy forces) that change positions over time. To assess the effectiveness of our proposed algorithm, we conducted comprehensive experiments, comparing and analyzing its performance in terms of average pathfinding success rate, average number of turns, and average execution time. Notably, we examined how the algorithm performs under two UCV path search strategies and two obstacle movement strategies. Our findings shed light on the potential of our approach in real-world UCV path planning scenarios.