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

Abstract

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

Path Planning Using Combined Informed Rapidly- Exploring Random Tree Star and Particle Swarm Optimization Algorithms

“…Constructing a path from the RRT* tree (𝑇) begins from the goal configuration (𝑞 𝑔𝑜𝑎𝑙 ). Next, a path from 𝑞 𝑔𝑜𝑎𝑙 back to the root node (𝑞 𝑖𝑛𝑖𝑡 ) is traced by following the parent-child relationship between nodes [113]. This results in a trajectory that connects 𝑞 𝑠𝑡𝑎𝑟𝑡 to 𝑞 𝑔𝑜𝑎𝑙 , representing a feasible route from the initial position to the goal.…”

UAV Formation Trajectory Planning Algorithms: A Review

“…Luo et al [39] proposed a convergent method to ensure autonomous non-collision trajectory planning of UAVs in the presence of static obstacles and dynamic threats. They extended the jump point search algorithm (JPS), parent node transfer law, seventh-order polynomial interpolation method of minimum capture, virtual gravity field and improved artificial potential field (APF) algorithm to a three-dimensional UAV.…”

Path Planning Technique for Mobile Robots: A Review

“…The A* algorithm takes into account the cost to reach each node from the starting node and the estimated cost to reach the goal node. In response to issues such as low search efficiency and high memory overhead in the traditional A* algorithm, various classic variants and improvements of A* have emerged, including Weighted A* (WA*) [53], Adaptive A* (AA*) [54], Theta* [55], and Jump Point Search (JPS) [56], as depicted in Table 7 and Figure 8. Pu et al [57] introduced a dual adaptive A* algorithm, which encompasses adaptive multi-objective heuristic functions and adaptive node expansion strategies.…”

“…The Theta* algorithm, initially introduced by Daniel et al [59], permits path searches along arbitrary angles, and subsequently marked the more efficient Lazy-Theta* [60]. Moreover, Luo et al [56] applied the variable step-size concept of the Theta* algorithm to the JPS algorithm, further improving the path quality and smoothness of the traditional JPS.…”