###### 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. collision avoidance; obstacle avoidance; path planning artificial potential field; motion primitives; unmanned aerial vehicle; dynamically feasible trajectory April 20211130309/app110731030  
go further in front of an obstacle since the attractive and repulsive potential field makes a balance. In the GNRON problem, an agent does not reach the goal location because an obstacle is too close to the goal location; that is, the goal is located within a certain area influenced by the repulsive potential field. To solve those problems, some scholars proposed the modified attractive and/or repulsive potential functions. As an illustration, [PERSON] and [PERSON] [15] added extra fractional equations to the attractive and repulsive potential functions. To resolve the APF's two major drawbacks, the only repulsive potential function was modified in the following three approaches: [PERSON] et al. [16] added fractional equations to the repulsive potential function; [PERSON] et al. [17] multiplied the repulsive potential function by an exponential function; [PERSON] et al. [14] multiplied the navigation potential function into the repulsive potential function. Although they tried to solve the two major problems of the conventional APF, planned paths based on the above modified potential functions are safe only in a certain static environment [18]. In other words, prior mapping is meaningless in dynamic environments [19; 20]. To avoid dynamic obstacles, the potential functions with polynomial equations were replaced with exponential functions [21] or the Gaussian function [22]. Furthermore, [PERSON] et al. [12] enhanced the potential field, applying the concept of the curl-free vector field [23] in the aspect of the local minima and dynamic obstacles. However, these approaches did not consider the GNRON problem. Also, they are only applicable for 2-dimensional (2D) dynamic environments. Furthermore, to resolve the GNRON issue, [PERSON] et al. [24] proposed the optimized APF (OAPF) algorithm, which multiplies the repulsive potential function with the distance between an agent and the goal. Even though the OAPF approach avoids dynamic and static obstacles in a 3D environment, it simulated only a scenario in which all unmanned aerial vehicles (UAVs) as relative dynamic obstacles go toward the same goal location, so it does not assure the CA's robustness for dynamic obstacles flying to other directions. Applying the conventional APF to the CA systems is not feasible for six degrees-of-freedom (DOF) UAVs since its formula does not consider the dynamics of vehicles [19; 20; 24]. To illustrate vehicles' dynamics in the APF, the fuzzy inference system models the repulsive potential field is used [25; 26]. Moreover, [PERSON] et al.