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

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.

Comparison Study of the PSO and SBPSO on Universal Robot Trajectory Planning

“…In Equations ( 3) and ( 4), repulsive potential field and repulsive force respectively [22,23]. The repulsive potential field is dependent on distant.…”

“…The fitness value of each particle is then calculated to determine which particle is the best (lines 4-11) and for the scout particles (lines 12-19). In the next step the best particle will be found and named the new gBest particle (lines [20][21][22][23][24][25][26]. After this, the particles start the inspection process at the proximity of the gBest particle (lines [27][28][29][30][31].…”

“…4. Rastrigin function-it is a highly multimodal function; however, the locations of the various local minima are regularly distributed and represented in Equation (22):…”

Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment

“…However, there is a main problem in the APF that is local minima [41]. There are some types of local minima, such as local minima in one obstacle, local minima in two obstacles [42], local minima in goal not reachable (GNRON) [43] [44] [45] and dynamic or moving obstacle. However, most researchers only consent to solve one of those problems by modifying the algorithm [46].…”

Intelligent cooperative collision avoidance via fuzzy potential fields

“…However, the parameters are optimized for only obstacles of equal size, and the simple environment with only one static or dynamic obstacle case is considered. Lee et al [28] proposed a CA approach that combines the APF and motion primitives (MPs). When a collision checker detects collision risk on extracted sample points from the expected trajectory, replanned path candidates were generated.…”