Obstacle Avoidance Learning for Robot Motion Planning in Human-Robot Integration Environments
In the human-robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the current mainstream robot navigation algorithms only treat pedestrians as dynamic obstacles and passively avoid pedestrians in local planning...
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| Vydané v: | IEEE transactions on cognitive and developmental systems Ročník 15; číslo 4; s. 1 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Piscataway
IEEE
01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2379-8920, 2379-8939 |
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| Abstract | In the human-robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the current mainstream robot navigation algorithms only treat pedestrians as dynamic obstacles and passively avoid pedestrians in local planning. When encountering fast-moving pedestrians, local path planning often fails, causing the robot to stagnate, spin or shake in place, which in turn reduces the navigation efficiency and results in unnatural navigation trajectories. To address this problem, it is desirable for the robot to find a safe and convenient temporary target to avoid the collision with fast-moving pedestrians. In this paper, we propose an obstacle avoidance learning method with the temporary target for the robot motion planning in the human-robot integration environment. The temporary target distribution is learned from imitations by using a Conditional Variational Autoencoder (CVAE) framework, whereby the dynamic scenario information including pedestrian information, the environmental information, and the robot information are considered as the generation conditions. With the proposed method, the mobile robot first navigates to the temporary target area, and then plans the path toward the final target point. Experimental studies reveal that the proposed method can achieve satisfactory performance with respect to different scenario conditions. |
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| AbstractList | In the human-robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the current mainstream robot navigation algorithms only treat pedestrians as dynamic obstacles and passively avoid pedestrians in local planning. When encountering fast-moving pedestrians, local path planning often fails, causing the robot to stagnate, spin or shake in place, which in turn reduces the navigation efficiency and results in unnatural navigation trajectories. To address this problem, it is desirable for the robot to find a safe and convenient temporary target to avoid the collision with fast-moving pedestrians. In this paper, we propose an obstacle avoidance learning method with the temporary target for the robot motion planning in the human-robot integration environment. The temporary target distribution is learned from imitations by using a Conditional Variational Autoencoder (CVAE) framework, whereby the dynamic scenario information including pedestrian information, the environmental information, and the robot information are considered as the generation conditions. With the proposed method, the mobile robot first navigates to the temporary target area, and then plans the path toward the final target point. Experimental studies reveal that the proposed method can achieve satisfactory performance with respect to different scenario conditions. In the human–robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the current mainstream robot navigation algorithms only treat pedestrians as dynamic obstacles and passively avoid pedestrians in local planning. When encountering fast-moving pedestrians, local path planning often fails, causing the robot to stagnate, spin or shake in place, which in turn reduces the navigation efficiency and results in unnatural navigation trajectories. To address this problem, it is desirable for the robot to find a safe and convenient temporary target to avoid the collision with fast-moving pedestrians. In this article, we propose an obstacle avoidance learning method with the temporary target for the robot motion planning in the human–robot integration environment. The temporary target distribution is learned from imitations by using a conditional variational autoencoder (CVAE) framework, whereby the dynamic scenario information, including pedestrian information, the environmental information, and the robot information, is considered as the generation conditions. With the proposed method, the mobile robot first navigates to the temporary target area, and then plans the path toward the final target point. Experimental studies reveal that the proposed method can achieve satisfactory performance with respect to different scenario conditions. |
| Author | Chi, Wenzheng Sun, Lining Hong, Yang Yuan, Yuan Ding, Zhiyu |
| Author_xml | – sequence: 1 givenname: Yang surname: Hong fullname: Hong, Yang organization: School of Mechanical and Electric Engineering, Robotics and Microsystems Center, Soochow University, Suzhou, China – sequence: 2 givenname: Zhiyu surname: Ding fullname: Ding, Zhiyu organization: School of Mechanical and Electric Engineering, Robotics and Microsystems Center, Soochow University, Suzhou, China – sequence: 3 givenname: Yuan surname: Yuan fullname: Yuan, Yuan organization: School of Mechanical and Electric Engineering, Robotics and Microsystems Center, Soochow University, Suzhou, China – sequence: 4 givenname: Wenzheng orcidid: 0000-0002-8121-2624 surname: Chi fullname: Chi, Wenzheng organization: School of Mechanical and Electric Engineering, Robotics and Microsystems Center, Soochow University, Suzhou, China – sequence: 5 givenname: Lining surname: Sun fullname: Sun, Lining organization: School of Mechanical and Electric Engineering, Robotics and Microsystems Center, Soochow University, Suzhou, China |
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| Snippet | In the human-robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the... In the human–robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the... |
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| SubjectTerms | Algorithms Collision avoidance Conditional Variational Autoencoder Dynamic Obstacle Avoidance Human motion Human-Robot Integration Environment Learning Motion planning Navigation Obstacle avoidance Path planning Pedestrians Planning Predictive models Robot dynamics Robot motion Robots Trajectory |
| Title | Obstacle Avoidance Learning for Robot Motion Planning in Human-Robot Integration Environments |
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