Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing
This addresses the challenge of sample-efficient online replanning for UAV-based active sensing in environmental monitoring, though it is incremental as it builds on existing RL and robotic methods.
The paper tackles the problem of efficiently planning paths for UAVs to maximize information gain in unknown environments, achieving performance comparable to existing methods while reducing runtime by 8-10x.
Aerial robots are increasingly being utilized for environmental monitoring and exploration. However, a key challenge is efficiently planning paths to maximize the information value of acquired data as an initially unknown environment is explored. To address this, we propose a new approach for informative path planning based on deep reinforcement learning (RL). Combining recent advances in RL and robotic applications, our method combines tree search with an offline-learned neural network predicting informative sensing actions. We introduce several components making our approach applicable for robotic tasks with high-dimensional state and large action spaces. By deploying the trained network during a mission, our method enables sample-efficient online replanning on platforms with limited computational resources. Simulations show that our approach performs on par with existing methods while reducing runtime by 8-10x. We validate its performance using real-world surface temperature data.