The multi-dimensional actions control approach for obstacle avoidance based on reinforcement learning
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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20
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SYMMETRY, Volume 13, issue 8
Abstract
In robotics, obstacle avoidance is an essential ability for distance sensor-based robots. This type of robot has axisymmetrically distributed distance sensors to acquire obstacle distance, so the state is symmetrical. Training the control policy with a reinforcement learning method is a trend. Considering the complexity of environments, such as narrow paths and right-angle turns, robots will have a better ability if the control policy can control the steering direction and speed simultaneously. This paper proposes the multi-dimensional action control (MDAC) approach based on a reinforcement learning technique, which can be used in multiple continuous action space tasks. It adopts a hierarchical structure, which has high and low-level modules. Low-level policies output concrete actions and the high-level policy determines when to invoke low-level modules according to the environment’s features. We design robot navigation experiments with continuous action spaces to test the method’s performance. It is an end-to-end approach and can solve complex obstacle avoidance tasks in navigation.Description
Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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Wu, M, Gao, Y, Wang, P, Zhang, F & Liu, Z 2021, 'The multi-dimensional actions control approach for obstacle avoidance based on reinforcement learning', SYMMETRY, vol. 13, no. 8, 1335. https://doi.org/10.3390/sym13081335