Automated Flight Technology for Integral Path Planning and Trajectory Tracking of the UAV

Abstract

In view of the problem that path planning and trajectory tracking are rarely solved simultaneously in the current research, which hinders their practical implementation, this paper focuses on enhancing the autonomous flight planning capability of unmanned aerial vehicles (UAVs) by investigating integrated path planning and trajectory tracking technologies. The autonomous flight process is divided into two sub-problems: waypoint designing/optimizing and waypoint tracking. Firstly, an improved DB-RRT* algorithm is proposed for waypoint planning to make the algorithm have higher planning efficiency, better optimization results, and overcome the defects of accidental and low reliability of single RRT* planning results. Secondly, the scheme of “offline design + online flight” is adopted to lead the UAV to fly online according to the waypoints’ instructions by using the sliding mode guidance based on angle constraint with finite-time convergence so that it can fly to the destination autonomously. In order to check the performance of the proposed algorithm, a variety of simulations are conducted to verify the feasibility of the proposed algorithm.

A Fusion Approach for UAV Onboard Flight Trajectory Management and Decision Making Based on the Combination of Enhanced A* Algorithm and Quadratic Programming

“…A typical approach to guarantee the safety margin is to introduce offsets of user-defined size or inflate the occupied voxels. The EPU cost is implemented as a soft constraint in this paper and defined as Equation (7).…”

“…In Equation ( 6), σ east , σ north , and σ up denote the estimated deviation of the positioning error in three directions. In Equation (7), r U AV denotes the true radius of the size of the UAV, and EPU is the estimated EPU at node n calculated by Equation ( 6), while dis nearest_obs (n, map * local ) is the distance between node n and its nearest occupied voxel, named as the safety margin in Figure 4. The navigation performance cost, h 3 (n), is the ratio of the UAV's equivalent radius and the distance margin for the safety requirements.…”

“…One is flight path planning performing at a global level. Some conventional and intelligent algorithms are used for global optimal path seeking according to selected criteria (e.g., energy consumption, flight time, or operational costs minimization) and the imposed UAV dynamic and kinematic characteristics constraints [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In this case, the main focus is economical path seeking.…”

“…Chen et al [6] established a double-tree to enhance the efficiency of the RRT algorithm. Gao et al [7] presented an improved DB-RRT* algorithm coupling with the sliding mode guidance based on angle constraints with finite-time convergence to control the UAV flying to the destination autonomously. Besides, since the sampling-based algorithm is not relying on a well-established map, Gao et al [8] presented a framework using a safe-region RRT* algorithm for online generating safe and dynamically feasible trajectories directly on the point cloud, which could constrain the position and kino-dynamics of the trajectory entirely within the flight corridors and given physical limits.…”