## 1 Introduction
[10] combined AI with a computer vision system onboard a UAV to enable real-time human detection during search and rescue operations. The integration of the two aforementioned key technologies--AI and computer vision--provides environment acquaintance to UAVs. This helps the UAVs to plan their collision-free paths. For autonomous navigation, a UAV has to have either a predetermined path or a capacity to plan a path in real-time. A mission with predetermined route requires less number of sensors as compared to a mission with real-time path planning. The challenges with real-time path planning are the complexity of multiple sensors integration, input data synchronization, and computational burdens thereof. [PERSON] et al. [11] developed techniques to enrich a UAV with capabilities of localizing itself and autonomously navigate in a GPS-denied environment. In their report, stereo cameras on-board the UAV-based vision data were used for UAV localization and to build a 3D map of the surroundings. Based on this information, an improved \(A^{*}\) path-planning algorithm was implemented for autonomous navigation of the UAV collision-free along the shortest path to the goal. System-resource-intensive computational burdens on the companion computer onboard a UAV is always a setback to real-time path planning for the UAV. The companion computer has to deal with visual data processing for UAV localization, obstacle detection, and path planning. A comprehensive literature review on vision-based UAV localization, obstacle avoidance, and path planning was reported by [PERSON] et al. [12]. In their study, the challenges of acquiring real-time data processing for safe navigation of the UAV are reflected. They also reported the challenges of autonomous navigation of a UAV due to intensive computation and high storage consumption of 3D map of the surroundings. [PERSON] et al. [13] developed a computer-simulation-based deep reinforcement learning technique towards real-time path planning for UAV in dynamic environments. Although this is a promising step towards real-time path planning in dynamic environments, the assumption of predetermined global situational data and the absence of real flight test that verifies the efficiency the technique may degrade its attention. To ease the computational burden on a companion computer dedicated to UAV localization, obstacle detection, and 3D path planning, we propose the integration of the fastest object detection algorithm with a light-weight 3D path planner that relies on few obstacle-free points to generate a 3D path. The proposed 3D path planner is based on AI acquired through YOLO (You Only Look Once ), which is the fastest object detection algorithm. The study presented in this report is organized into sections. In Section 2, the problem to be addressed in this study is stated and the implemented methodology is explained. In Section 3, the overall descriptions of the implemented hardware and software components and their configurations are given. The machine learning approach for object detection is explained in Section 4. Then, the commonly known 3D path planning algorithms are discussed with their advantages and disadvantages in Section 5. The developed real-time 3D path planner is detailed in this section, followed by its performance tests in Section 6. Results and discussion are given in the final Section 7.