Abstract
Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.
Highlights
A number of studies for collision avoidance (CA) have been conducted [1,2]
To tackle all of the known issues related to the artificial potential field (APF), this paper proposes a CA approach that combines the APF and the motion primitives (MPs)
The last challenging case is a complex environment with multiple unmanned aerial vehicles (UAVs) in the presence of static obstacles
Summary
A number of studies for collision avoidance (CA) have been conducted [1,2]. Among several CA approaches, the graph search algorithms are widely used because they are known to provide successful results in general. Graph search algorithm-based approach to find a collision-free path. Bezier curves are applied to the CA during multi-robot operations [5], which offers smooth trajectories for avoiding collisions. Dynamic constraints such as position, velocity, and acceleration changes are not bounded in some circumstances. Zhang et al [6] utilizes an optimization technique to find a collision-free trajectory that minimizes a vehicle’s total travel distance. This problem is sensitive to initial guesses and requires a high computational burden. Numerous researchers have investigated the APF to develop path planning algorithms that avoid obstacles [9,10,11,12]
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