3D JPS Path Optimization Algorithm and Dynamic-Obstacle Avoidance Design Based on Near-Ground Search Drone

As various fields and industries have progressed, the use of drones has grown tremendously. The problem path planning for flying at low altitude in urban as well mountainous areas will be crucial performing search-and-rescue missions. In this paper, we propose a convergent approach to ensure autonomous collision-free presence both static obstacles dynamic threats. Firstly, paper extends jump point search algorithm (JPS) three dimensions drone generate paths based on environments. Next, parent node transfer law is proposed used implement JPS any-angle planning, which further shortens drones. Furthermore, optimized are smoothed by seventh-order polynomial interpolation minimum snap continuity nodes. Finally, improves artificial potential field (APF) method virtual gravitational 3D Bresenham’s line achieve obstacle avoidance dynamic-threat conflict environment. performance verified simulation experiments. results show that can effectively solve autonomous-obstacle-avoidance problems low-altitude flight