Multi-Target Path Planning with Probabilistic Detection in Cluttered Environments

Abstract

Autonomous Unmanned Aerial Vehicles (UAVs) offer substantial advantages for tasks such as surveillance, disaster management, and environmental monitoring, where human intervention can be risky. With advancements in their agility and autonomy, UAVs are becoming essential for critical tasks in combat, reconnaissance, wildfire monitoring, and disaster search and rescue. This paper addresses a key challenge in UAV path planning: efficiently visiting multiple unknown mobile targets in complex, obstacle-filled environments. We leverage the Deep Deterministic Policy Gradient (DDPG) framework to continuously control UAV movement to enable effective obstacle avoidance and sequential target visitation. Our approach allows the UAV to learn the unknown distribution of mobile targets and determine optimal paths while navigating around obstacles. With limited environment information, the agent receives rewards based on the confidence of detecting targets within its observation field. We validate the effectiveness of our method through comparison with an optimal benchmark that assumes perfect knowledge of target mobility and obstacle locations. Results indicate that increasing target numbers significantly impacts the agent's performance by requiring additional training time. Moreover, heavily cluttered environments reduce mission success rates for target visitation.