Deep Reinforcement Learning for Time-Critical Wilderness Search And Rescue Using Drones
This addresses the critical need for faster and more efficient search operations in wilderness areas, potentially saving lives, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of optimizing drone search paths for wilderness search and rescue by using deep reinforcement learning with a priori probability distribution maps, resulting in a 160% improvement in search times compared to traditional algorithms.
Traditional search and rescue methods in wilderness areas can be time-consuming and have limited coverage. Drones offer a faster and more flexible solution, but optimizing their search paths is crucial. This paper explores the use of deep reinforcement learning to create efficient search missions for drones in wilderness environments. Our approach leverages a priori data about the search area and the missing person in the form of a probability distribution map. This allows the deep reinforcement learning agent to learn optimal flight paths that maximize the probability of finding the missing person quickly. Experimental results show that our method achieves a significant improvement in search times compared to traditional coverage planning and search planning algorithms. In one comparison, deep reinforcement learning is found to outperform other algorithms by over $160\%$, a difference that can mean life or death in real-world search operations. Additionally, unlike previous work, our approach incorporates a continuous action space enabled by cubature, allowing for more nuanced flight patterns.