Sensors, Vol. 19, Pages 3837: Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning
Sensors, Vol. 19, Pages 3837: Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning
Sensors doi: 10.3390/s19183837
Authors:
Junjie Zeng
Rusheng Ju
Long Qin
Yue Hu
Quanjun Yin
Cong Hu
In this paper, we propose a novel Deep Reinforcement Learning (DRL) algorithm which can navigate non-holonomic robots with continuous control in an unknown dynamic environment with moving obstacles. We call the approach MK-A3C (Memory and Knowledge-based Asynchronous Advantage Actor-Critic) for short. As its first component, MK-A3C builds a GRU-based memory neural network to enhance the robot’s capability for temporal reasoning. Robots without it tend to suffer from a lack of rationality in face of incomplete and noisy estimations for complex environments. Additionally, robots with certain memory ability endowed by MK-A3C can avoid local minima traps by estimating the environmental model. Secondly, MK-A3C combines the domain knowledge-based reward function and the transfer learning-based training task architecture, which can solve the non-convergence policies problems caused by sparse reward. These improvements of MK-A3C can efficiently navigate robots in unknown dynamic environments, and satisfy kinetic constraints while handling moving objects. Simulation experiments show that compared with existing methods, MK-A3C can realize successful robotic navigation in unknown and challenging environments by outputting continuous acceleration comm...
Source: Sensors - Category: Biotechnology Authors: Junjie Zeng Rusheng Ju Long Qin Yue Hu Quanjun Yin Cong Hu Tags: Article Source Type: research
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