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Intelligent Service Robotics
Erschienen in: Intelligent Service Robotics 2/2020

03.02.2020 | Original Research

Reinforcement learning path planning algorithm based on obstacle area expansion strategy

verfasst von: Haiyang Chen, Yebiao Ji, Longhui Niu

Erschienen in: Intelligent Service Robotics | Ausgabe 2/2020

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Abstract

We improve the traditional Q(\( \lambda \))-learning algorithm by adding the obstacle area expansion strategy. The new algorithm is named OAE-Q(\( \lambda \))-learning and applied to the path planning in the complex environment. The contributions of OAE-Q(\( \lambda \))-learning are as follows: (1) It expands the concave obstacle area in the environment to avoid repeated invalid actions when the agent falls into the obstacle area. (2) It removes the extended obstacle area, which reduces the learning state space and accelerates the convergence speed of the algorithm. Extensive experimental results validate the effectiveness and feasibility of OAE-Q(\( \lambda \))-learning on the path planning in complex environments.
Vorheriger Artikel Onboard plane-wise 3D mapping using super-pixels and stereo vision for autonomous flight of a hexacopter
Nächster Artikel Toward a robot swarm protecting a group of migrants

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Metadaten
Titel
Reinforcement learning path planning algorithm based on obstacle area expansion strategy
verfasst von
Haiyang Chen
Yebiao Ji
Longhui Niu
Publikationsdatum
03.02.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Intelligent Service Robotics / Ausgabe 2/2020
Print ISSN: 1861-2776
Elektronische ISSN: 1861-2784
DOI
https://doi.org/10.1007/s11370-020-00313-y

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