With the continuous advancement of unmanned aerial vehicle (UAV) technology, UAVs have been widely adopted in both civilian and military applications, including geographic monitoring, disaster relief, aerial surveillance, logistics delivery, and tactical reconnaissance. However, single UAVs are increasingly unable to meet the demands of modern complex or large-scale missions owing to inherent limitations in flight endurance, sensing range, and processing capability. Consequently, deploying multiple UAVs for cooperative operations (i.e., UAV swarms) has become a critical development trend. During actual flight missions, whether operating individually or in swarms, UAVs may encounter unexpected no-fly zones or obstacles. Failure to reprogram and adjust flight paths in real time significantly reduces mission success rates and jeopardizes operational safety. To address this challenge, we propose a dynamic obstacle-avoidance path programming method based on the A* algorithm, integrated with onboard perception data. This system enables UAVs to autonomously calculate alternative bypass routes when encountering no-fly zones or obstacles and return to the original navigation waypoint afterward to ensure mission continuity. To enhance real-time performance and energy efficiency, the system utilizes a low-power onboard edge computing platform to perform immediate path reprogramming and rapidly transmit the updated route information to the flight controller for execution, thereby considerably reducing dependence on ground stations and minimizing communication latency. Experimental results demonstrate that the proposed system can effectively perform real-time obstacle avoidance and maintain smooth flight trajectories across various simulated obstacle scenarios, showcasing excellent flexibility and reliability. The outcomes of this study significantly strengthen UAVs’ autonomous decision-making capabilities in dynamic environments, enhancing safety and operational efficiency during complex missions. This approach holds substantial potential for future applications in multi-UAV collaborative inspections, real-time disaster area monitoring, and high-risk area operations.