A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment

Abstract

This paper presents a geometrical path planning method, and it can help unmanned aerial vehicle to find a collision-free path in two-dimensional and three-dimensional (2D and 3D) complex environment quickly. First, a list of tree is designed to describe obstacles, and it is used to query the obstacles which block the line from starting point to finishing point (blocking obstacle). Specially, the list also stores the edge information of blocking obstacle. For the obstacles with short distance, a reasonable way to fly over is studied. Then, a shortest path planning method based on geometrical computation is proposed according to different shapes of obstacles. The obstacles are convex and divided into two cases of 2D and 3D. 2D environment includes rectangular obstacle, trapezoidal obstacle, triangular obstacle, circular obstacle and elliptic obstacle. In 3D, it includes cuboid, sphere and ellipsoid. To compare with other methods, the simulation is made in different environments. In 2D environment with circular obstacles, the method is similar to the artificial potential field. In 2D environment with rectangular obstacles, the performance of the proposed method is better than A-star. Compared with genetic algorithm, the proposed method gives a better result in 3D environment with cuboid obstacles. In 3D environment with hybrid obstacles, it is similar to interfered fluid dynamical system. Through comprehensive comparison and analysis, the conclusion is that the method has good adaptability and does not require grid modeling. It can find a shorter path in 2D/3D complex environment within a short time, so it has the ability of real-time path planning. Keywords Unmanned aerial vehicle (UAV) • Path planning • Geometrical shortest path • 2D/3D complex environment • Convex obstacles • Optimal and real-time planning B Xiao Liang

Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

“…Among the path generators in the specialized literature, the A-Star algorithm is a well-known efficient alternative that deterministically finds sub-optimal feasible paths in complex scenarios [60]. A-Star operates over a grid discretization of the workspace that considers the cells containing obstacles or threats as prohibited regions.…”

Collaborative Pursuit-Evasion Strategy of UAV/UGV Heterogeneous System in Complex Three-Dimensional Polygonal Environment

“…In Figure 4(a), Property I indicates that the subgoal S must be in the shortest path between P 2 and E. e role of the subgoal of P 2 is to drive and force E so that the situation is more beneficial to pursuers. When P 1 performs 3D interception, the subgoal of P 1 can be obtained according to our previous works [31,32]. Figure 4(b) shows the calculation of a 3D subgoal by taking a cuboid obstacle as an example.…”

Automatic Separation Management Between Multiple Unmanned Aircraft Vehicles in Uncertain Dynamic Airspace Based on Trajectory Prediction

“…According to formulas (9), (10), (13) and (14), Wmax, V, t, ρ and r were set to 0.013/s, 4 m/s, 10 s, 307.69 m and 3 m, respectively. Then, the boundaries of the reachable set RSxy, i.e.…”

“…Many reviews have been completed on relevant issues in the past two years [5][6][7][8]. Popular collision avoidance algorithms include geometric method [9][10][11], sampling-based method [12][13][14], numerical optimization [15][16][17][18][19], and artificial potential field (APF) [20][21][22][23][24][25][26][27][28]. The geometric method considers the geometric representation of the collision scene in the search for collision avoidance maneuvers.…”