Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing

TL;DR

This paper presents a deep reinforcement learning-based method for UAV path planning that maximizes information gain during environmental exploration, enabling efficient online replanning with reduced computational costs.

Contribution

It introduces a novel RL-based approach combining tree search and neural networks for informative path planning in high-dimensional robotic tasks.

Findings

Achieves comparable performance to existing methods.

Reduces runtime by 8-10 times during online replanning.

Validated with real-world temperature data.

Abstract

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.