CPP: a path planning method taking into account obstacle shadow hiding

Abstract

Path planning algorithms are crucial for the autonomous navigation and task execution of unmanned vehicles in battlefield environments. However, existing path planning algorithms often overlook the concealment effects of obstacles, which can lead to significant safety risks for unmanned vehicles during operation. To address this issue, we proposed a novel path planning method—Covert Path Planning (CPP)—that incorporated considerations for the shadow occlusion caused by obstacles. By accounting for these concealment effects, CPP aimed to enhance the safety and effectiveness of unmanned vehicles in complex and dynamic battlefield scenarios. It started by designing shadow areas in the configuration environment based on solar azimuth and altitude angles. A gravitational field model was then created using these shadow areas and the target point’s position to guide the path point movement, achieving a path with a higher safety coefficient. The method also dynamically adjusted step length according to gravitational forces to boost planning efficiency. Additionally, a deformed ellipse-based obstacle avoidance technique was introduced to enhance the vehicle’s ability to navigate around obstacles. We simplified the path by considering the relationship between path points and shadows. We also proposed a Minimum-Jerk Trajectory Optimization method with controllable path noise points, which enhanced path smoothness and reduced predictability. Comparative analysis showed that CPP significantly outperformed five other algorithms—RRT, Improved B-RRT, RRT*, Informed RRT*, and Potential Field-by reducing running time by 46.01% to 93.3%, increasing path safety by 10.42% to 83.44%, and improving path smoothness, making it particularly effective for path planning in tactical scenarios involving unmanned vehicles.