AgilePilot: DRL-Based Drone Agent for Real-Time Motion Planning in Dynamic Environments by Leveraging Object Detection

Autonomous drone navigation in dynamic environments remains a critical challenge, especially when dealing with unpredictable scenarios including fast-moving objects with rapidly changing goal positions. While traditional planners and classical optimization methods have been extensively used to address this dynamic problem, they often face real-time, unpredictable changes that ultimately lead to suboptimal performance in terms of adaptiveness and real-time decision-making. In this work, we propose a novel motion planner, AgilePilot, based on deep reinforcement learning (DRL) that is trained in dynamic conditions, coupled with real-time computer vision (CV) for object detections during flight. The training-to-deployment framework bridges the Sim2Real gap, leveraging sophisticated reward structures that promote both safety and agility depending upon environmental conditions. The system can rapidly adapt to changing environments while achieving a maximum allowable speed of $3.0 ~\mathrm{m} / \mathrm{s}$ in real-world scenarios. In comparison, our approach outperforms classical algorithms such as the Artificial Potential Field (APF)-based motion planner by 3 times, both in performance and tracking accuracy of dynamic targets by using velocity predictions while exhibiting 90% a success rate in 75 conducted experiments. This work highlights the effectiveness of DRL in tackling real-time dynamic navigation challenges, offering intelligent safety and agility.