Probabilistic Trajectory Estimation based Leader Following for Multi-Robot Systems
The paper is concerned with the multi-robot leader-following problem in the presence of frequent dropouts in vision detection. In many scenarios, for instance a structured environment, it is inevitable to experience outage of vision detection due to reasons such as the target moving out of view, vis...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/82415 http://hdl.handle.net/10220/42306 |
| _version_ | 1826126807928143872 |
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| author | Shan, Mao Zou, Ying Guan, Mingyang Wen, Changyun Lim, Kwang-Yong Ng, Cheng-Leong Tan, Paul |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Shan, Mao Zou, Ying Guan, Mingyang Wen, Changyun Lim, Kwang-Yong Ng, Cheng-Leong Tan, Paul |
| author_sort | Shan, Mao |
| collection | NTU |
| description | The paper is concerned with the multi-robot leader-following problem in the presence of frequent dropouts in vision detection. In many scenarios, for instance a structured environment, it is inevitable to experience outage of vision detection due to reasons such as the target moving out of view, vision occlusion, motion blurring, etc. The paper proposes a Bayesian trajectory estimation based leader-following approach that can offer accurate path following given intermittent vision observations. The follower robot estimates the trajectory of the leader robot based on the noise-corrupted odometry information of both robots, and inter-robot relative observations based on detection of fiducial markers using an RGBD camera. A linear trajectory-following control method is employed to track a historical pose of the leader robot on the estimated trajectory. Results are obtained based on evaluating the proposed leader-following approach in tests with a zig-zag shaped trajectory and with a trajectory that contains sharp turns. |
| first_indexed | 2024-10-01T06:58:42Z |
| format | Conference Paper |
| id | ntu-10356/82415 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:58:42Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/824152020-03-07T13:24:44Z Probabilistic Trajectory Estimation based Leader Following for Multi-Robot Systems Shan, Mao Zou, Ying Guan, Mingyang Wen, Changyun Lim, Kwang-Yong Ng, Cheng-Leong Tan, Paul School of Electrical and Electronic Engineering 2016 14th International Conference on Control, Automation, Robotics & Vision (ICARCV) trajectory control multi-robot systems The paper is concerned with the multi-robot leader-following problem in the presence of frequent dropouts in vision detection. In many scenarios, for instance a structured environment, it is inevitable to experience outage of vision detection due to reasons such as the target moving out of view, vision occlusion, motion blurring, etc. The paper proposes a Bayesian trajectory estimation based leader-following approach that can offer accurate path following given intermittent vision observations. The follower robot estimates the trajectory of the leader robot based on the noise-corrupted odometry information of both robots, and inter-robot relative observations based on detection of fiducial markers using an RGBD camera. A linear trajectory-following control method is employed to track a historical pose of the leader robot on the estimated trajectory. Results are obtained based on evaluating the proposed leader-following approach in tests with a zig-zag shaped trajectory and with a trajectory that contains sharp turns. Accepted version 2017-05-03T04:06:40Z 2019-12-06T14:55:09Z 2017-05-03T04:06:40Z 2019-12-06T14:55:09Z 2016-11-01 2016 Conference Paper Shan, M., Zou, Y., Guan, M., Wen, C., Lim, K.-Y., Ng, C.-L., et al. (2016). Probabilistic trajectory estimation based leader following for multi-robot systems. 2016 14th International Conference on Control, Automation, Robotics & Vision (ICARCV), 1-6. https://hdl.handle.net/10356/82415 http://hdl.handle.net/10220/42306 10.1109/ICARCV.2016.7838742 198730 en © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://doi.org/10.1109/ICARCV.2016.7838742]. 6 p. application/pdf |
| spellingShingle | trajectory control multi-robot systems Shan, Mao Zou, Ying Guan, Mingyang Wen, Changyun Lim, Kwang-Yong Ng, Cheng-Leong Tan, Paul Probabilistic Trajectory Estimation based Leader Following for Multi-Robot Systems |
| title | Probabilistic Trajectory Estimation based Leader Following for Multi-Robot Systems |
| title_full | Probabilistic Trajectory Estimation based Leader Following for Multi-Robot Systems |
| title_fullStr | Probabilistic Trajectory Estimation based Leader Following for Multi-Robot Systems |
| title_full_unstemmed | Probabilistic Trajectory Estimation based Leader Following for Multi-Robot Systems |
| title_short | Probabilistic Trajectory Estimation based Leader Following for Multi-Robot Systems |
| title_sort | probabilistic trajectory estimation based leader following for multi robot systems |
| topic | trajectory control multi-robot systems |
| url | https://hdl.handle.net/10356/82415 http://hdl.handle.net/10220/42306 |
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