Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82415
Title: Probabilistic Trajectory Estimation based Leader Following for Multi-Robot Systems
Authors: Shan, Mao
Zou, Ying
Guan, Mingyang
Wen, Changyun
Lim, Kwang-Yong
Ng, Cheng-Leong
Tan, Paul
Keywords: trajectory control
multi-robot systems
Issue Date: 2016
Source: 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.
Conference: 2016 14th International Conference on Control, Automation, Robotics & Vision (ICARCV)
Abstract: 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.
URI: https://hdl.handle.net/10356/82415
http://hdl.handle.net/10220/42306
DOI: 10.1109/ICARCV.2016.7838742
Schools: School of Electrical and Electronic Engineering 
Rights: © 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].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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