Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

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 APF are safe only in a certain environment. This research proposes CA approach that combines motion primitives (MPs) to tackle problems associated with APF. Since MPs solve for locally optimal trajectory respect allocated time, obtained by is verified feasible. When collision checker k-d tree search algorithm detects risk extracted sample points from trajectory, generating re-planned path candidates avoid obstacles performed. After rejecting unsafe route candidates, one applies select best among remaining safe-path candidates. To validate proposed approach, we simulated two meaningful scenario cases—the presence of static situation dynamic environments multiple UAVs present. The simulation results show provides smooth, efficient, feasible pathing compared