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Learning “where to look” for visual exploration of autonomous mobile robot in indoor unknown environments

Published online by Cambridge University Press:  04 September 2025

Sheng Jin Open the ORCID record for Sheng Jin [Opens in a new window] ,
Shuai-Dong Yang ,
Hao-Yu Wang ,
Chen-Yang Zhang  and
Qing-Hao Meng
Sheng Jin
Affiliation:
Institute of Robotics and Autonomous Systems, Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System, School of Electrical and Information Engineering, Tianjin University, Tianjin, China Advanced Perception and Intelligent Equipment Engineering Research Center of Jiangsu Province, School of Smart Manufacturing and Intelligent Transportation, Suzhou City University, Suzhou, China
Shuai-Dong Yang
Affiliation:
Advanced Perception and Intelligent Equipment Engineering Research Center of Jiangsu Province, School of Smart Manufacturing and Intelligent Transportation, Suzhou City University, Suzhou, China
Hao-Yu Wang
Affiliation:
Institute of Robotics and Autonomous Systems, Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System, School of Electrical and Information Engineering, Tianjin University, Tianjin, China
Chen-Yang Zhang
Affiliation:
School of Civil Engineering and Architecture, Changzhou Institute of Technology, Changzhou, China School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
Qing-Hao Meng*
Affiliation:
Institute of Robotics and Autonomous Systems, Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System, School of Electrical and Information Engineering, Tianjin University, Tianjin, China
*
Corresponding author: Qing-Hao Meng; Email: qh_meng@tju.edu.cn
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Abstract

Visual exploration is a task in which a camera-equipped robot seeks to efficiently visit all navigable areas of an environment within the shortest possible time. Most existing visual exploration methods rely on a static camera fixed to the robot’s body to control its own movements. However, coupling the orientation of camera with robot’s body limits the extra degrees of freedom to obtain more visual information. In this work, we adjust the camera orientation during robot motion by using a novel camera view planning (CVP) policy to improve the exploration efficiency. Specifically, we reformulate the CVP problem as a reinforcement learning problem. However, two new challenges need to be addressed: 1) determining how to learn an effective CVP policy in complex indoor environments and 2) figuring out how to synchronize it with the robot motion. To solve the above issues, we create a reward function considering factors such as exploration area, observed semantic objects, and the motion conflicts between the camera and the robot’s body. Moreover, to better coordinate the policies of the camera and the robot’s body, the CVP policy takes the body actions and the egocentric 2D spatial maps with exploration, occupancy, and trajectory information into account to make motion decisions. Experimental results show that after using the proposed CVP policy, the exploration area is expanded by 21.72% and 25.6% on average in the small-scale indoor scene with few structured obstacles and large-scale indoor scene with cluttered obstacles, respectively.

Keywords

motion planningmobile robotsvisual explorationcamera view planningdeep reinforcement learning

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Type
Research Article
Information
Robotica , First View , pp. 1 - 20
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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