Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning

Choi, Taeyeong and Cielniak, Grzegorz (2021) Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning. In: 2021 European Conference on Mobile Robots (ECMR), 31 Aug - 03 Sep 2021, Bonn, Germany.

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Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning
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In our previous work, we designed a systematic policy to prioritize sampling locations to lead significant accuracy
improvement in spatial interpolation by using the prediction uncertainty of Gaussian Process Regression (GPR) as “attraction force” to deployed robots in path planning. Although the integration with Traveling Salesman Problem (TSP) solvers was also shown to produce relatively short travel distance, we here hypothesise several factors that could decrease the overall prediction precision as well because sub-optimal locations may eventually be included in their paths. To address this issue, in this paper, we first explore “local planning” approaches adopting various spatial ranges within which next sampling locations are prioritized to investigate their effects on the prediction performance as well as incurred travel distance. Also, Reinforcement Learning (RL)-based high-level controllers are trained to adaptively produce blended plans from a particular set of local planners to inherit unique strengths from that selection depending on latest prediction states. Our experiments on use cases of temperature monitoring robots demonstrate that the dynamic mixtures of planners can not only generate sophisticated, informative plans that a single planner could
not create alone but also ensure significantly reduced travel distances at no cost of prediction reliability without any assist of additional modules for shortest path calculation.

Keywords:Informative Path Planning, Robotic Exploration, Robotic Information Gathering, Robotic Environment Mapping, Hierarchical Robot Planning, Mobile robotics, Mobile Robot Navigation., Deep Reinforcement Learning
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:46371
Deposited On:05 Oct 2021 14:37

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