###### Abstract
[27] provided a new repulsive potential function by multiplying the relative distance between an agent's current position and its goal location while the agent's motion is controlled by a PID controller based on the particle swarm optimization algorithm. [PERSON] et al. [28] used the same potential function formula as the one used by [PERSON] et al., while they controlled a mobile robot via the model predictive control approach. In addition, [PERSON] et al. [29] and [PERSON] et al. [30] handled the dynamics of a quadcopter with the APF, but their maneuvering is limited to horizontal avoidance at a constant altitude. Although these references generated dynamically feasible trajectories, their available environment is only a 2D space with static obstacles. Motion primitives (MPs) [31; 32] are a widely used trajectory generation method for controlling a quadcopter in a 3D space, so the planned trajectory is dynamically feasible. In fact, the MPs create a smooth and optimal path by minimizing the jerk when given a UAV's current state information, the desired end conditions, and duration period. It results in a time-parameterized polynomial to make capable a fast update when re-planning is required, but the standard MPs themselves do not contain the CA functionality. To tackle all of the known issues related to the APF, this paper proposes a CA approach that combines the APF and the MPs. First, an autonomous UAV rapidly propagates an optimal trajectory using the MPs. Next, once point cloud data are ready, the UAV checks collision risk on the extracted sample points along the path using the k-d tree search. If a collision risk point is detected, one re-plans path candidates connecting intermediate waypoints to avoid the nearest obstacle point. After removing unsafe ones, one selects the best path candidate using the APF. Although mathematical theory and technical fundamentals related to the MP and k-d tree search are already known, the authors emphasize that generating path candidates and re-planning by the APF are our major contributions. Furthermore, the integration of individually known theories to an overall CA process and validation of it in realistic simulations are original and valuable results. Table 1 summarizes the literature review of the CA approaches using the APF, including the proposed approach. Compared to other relevant articles, the proposed approach enables UAVs to avoid static and moving obstacles in a 3D space along dynamically feasible trajectories without the local minima and GNRON problems. The authors' previous research [33] focused on conceptual validation so that it tested with artificially generated obstacles and did not consider the sensor's specifications when calculating the CA algorithm. However, this study adopts realistic obstacles representing an urban environment for validating practicability. In addition, to efficiently generate path candidates beneficial to avoidance, this paper adds perturbation for asymmetry. In case all path candidates are unsafe, the ability to set additional route candidates has also been proposed so that a UAV avoids a larger detour. The remainder of this paper contains the following sections. Section 2 presents our methodology; that is, path propagation using the MPs, collision checking based on the k-d tree search algorithm [34], and re-planning using the APF when potential collision risks are detected. Section 3 shows the simulation results for various scenarios in realistic environments. The last section concludes and plans future work.