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Probability Dueling DQN active visual SLAM for autonomous navigation in indoor environment

Probability Dueling DQN active visual SLAM for autonomous navigation in indoor environment This paper aims to use the Monodepth method to improve the prediction speed of identifying the obstacles and proposes a Probability Dueling DQN algorithm to optimize the path of the agent, which can reach the destination more quickly than the Dueling DQN algorithm. Then the path planning algorithm based on Probability Dueling DQN is combined with FastSLAM to accomplish the autonomous navigation and map the environment.Design/methodology/approachThis paper proposes an active simultaneous localization and mapping (SLAM) framework for autonomous navigation under an indoor environment with static and dynamic obstacles. It integrates a path planning algorithm with visual SLAM to decrease navigation uncertainty and build an environment map.FindingsThe result shows that the proposed method offers good performance over existing Dueling DQN for navigation uncertainty under the indoor environment with different numbers and shapes of the static and dynamic obstacles in the real world field.Originality/valueThis paper proposes a novel active SLAM framework composed of Probability Dueling DQN that is the improved path planning algorithm based on Dueling DQN and FastSLAM. This framework is used with the Monodepth depth image prediction method with faster prediction speed to realize autonomous navigation in the indoor environment with different numbers and shapes of the static and dynamic obstacles. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Robot: The International Journal of Robotics Research and Application Emerald Publishing

Probability Dueling DQN active visual SLAM for autonomous navigation in indoor environment

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0143-991X
DOI
10.1108/ir-08-2020-0160
Publisher site
See Article on Publisher Site

Abstract

This paper aims to use the Monodepth method to improve the prediction speed of identifying the obstacles and proposes a Probability Dueling DQN algorithm to optimize the path of the agent, which can reach the destination more quickly than the Dueling DQN algorithm. Then the path planning algorithm based on Probability Dueling DQN is combined with FastSLAM to accomplish the autonomous navigation and map the environment.Design/methodology/approachThis paper proposes an active simultaneous localization and mapping (SLAM) framework for autonomous navigation under an indoor environment with static and dynamic obstacles. It integrates a path planning algorithm with visual SLAM to decrease navigation uncertainty and build an environment map.FindingsThe result shows that the proposed method offers good performance over existing Dueling DQN for navigation uncertainty under the indoor environment with different numbers and shapes of the static and dynamic obstacles in the real world field.Originality/valueThis paper proposes a novel active SLAM framework composed of Probability Dueling DQN that is the improved path planning algorithm based on Dueling DQN and FastSLAM. This framework is used with the Monodepth depth image prediction method with faster prediction speed to realize autonomous navigation in the indoor environment with different numbers and shapes of the static and dynamic obstacles.

Journal

Industrial Robot: The International Journal of Robotics Research and ApplicationEmerald Publishing

Published: Aug 3, 2021

Keywords: Path planning; FastSLAM; Active SLAM; Deep reinforcement learning

References