Journal
2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022)
Volume -, Issue -, Pages 4473-4479Publisher
IEEE
DOI: 10.1109/ICRA46639.2022.9812025
Keywords
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Funding
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC 2070 -390732324]
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Aerial robots are being used more often for environmental monitoring and exploration, but efficiently planning paths to maximize the value of collected data in unknown environments is a challenge. To tackle this problem, the authors propose a new approach for informative path planning using deep reinforcement learning. By combining tree search with an offline-learned neural network, their method is able to handle high-dimensional state and large action spaces. Simulation results show that the approach performs as well as 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.
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