Abstract
Control and path planning are two essential and challenging issues in quadrotor unmanned aerial vehicle (UAV). In this paper, an approach for moving around the nearest obstacle is integrated into an artificial potential field (APF) to avoid the trap of local minimum of APF. The advantage of this approach is that it can help the UAV successfully escape from the local minimum without collision with any obstacles. Moreover, the UAV may encounter the problem of unreachable target when there are too many obstacles near its target. To address the problem, a parallel search algorithm is proposed, which requires UAV to simultaneously detect obstacles between current point and target point when it moves around the nearest obstacle to approach the target. Then, to achieve tracking of the planned path, the desired attitude states are calculated. Considering the external disturbance acting on the quadrotor, a nonlinear disturbance observer (NDO) is developed to guarantee observation error to exponentially converge to zero. Furthermore, a backstepping controller synthesized with the NDO is designed to eliminate tracking errors of attitude. Finally, comparative simulations are carried out to illustrate the effectiveness of the proposed path planning algorithm and controller.
Highlights
In recent years, unmanned aerial vehicle (UAV) has been used in various applications, such as infrastructure management [1], logistics delivery [2], and estimation of aboveground biomass of mangrove ecosystems [3]
A comparison of Genetic algorithm (GA) and particle swarm optimization (PSO) for real-time path planning of UAV is carried out in [19], where the results indicate that, under the consideration of statistical significance, the trajectories produced by GA are superior compared to that produced by PSO when using the same encoding
Another shortcoming of traditional APF (TAPF) is that the goal might be inaccessible for UAV, when obstacles are near the target, as shown in Figures 3 and 4, where Obsi, i = 1, ⋯, 8, denotes the ith obstacle
Summary
UAV has been used in various applications, such as infrastructure management [1], logistics delivery [2], and estimation of aboveground biomass of mangrove ecosystems [3]. An improved fruit fly optimization algorithm is introduced in [22] to address the problem of path planning of multiple UAVs in 3D complicated environments with online changing tasks. Motivated by the above analysis, a novel APF based on parallel search is proposed for path planning of UAV in this paper. (1) A parallel search algorithm is proposed to address local minimum and unreachable target with obstacles nearby in TAPF (2) Compared with existing results of path planning algorithms [34, 39, 40], a shorter path and less time consumption are obtained using the proposed algorithm (3) Compared with ADRC [6, 7], better tracking performance is obtained by the proposed controller based on NDO with exponential convergence when following the planned path.
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