Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105499
Title: A new approach to time-optimal path parameterization based on reachability analysis
Authors: Pham, Hung
Pham, Quang-Cuong
Keywords: Controllability
Motion Planning
DRNTU::Engineering::Mechanical engineering
Issue Date: 2018
Source: Pham. H., & Pham. Q.-C. (2018). A new approach to time-optimal path parameterization based on reachability analysis. IEEE Transactions on Robotics, 34(3), 645-659. doi:10.1109/TRO.2018.2819195
Series/Report no.: IEEE Transactions on Robotics
Abstract: Time-Optimal Path Parameterization (TOPP) is a well-studied problem in robotics and has a wide range of applications. There are two main families of methods to address TOPP: Numerical Integration (NI) and Convex Optimization (CO). NIbased methods are fast but difficult to implement and suffer from robustness issues, while CO-based approaches are more robust but at the same time significantly slower. Here we propose a new approach to TOPP based on Reachability Analysis (RA). The key insight is to recursively compute reachable and controllable sets at discretized positions on the path by solving small Linear Programs (LPs). The resulting algorithm is faster than NI-based methods and as robust as CO-based ones (100% success rate), as confirmed by extensive numerical evaluations. Moreover, the proposed approach offers unique additional benefits: Admissible Velocity Propagation and robustness to parametric uncertainty can be derived from it in a simple and natural way.
URI: https://hdl.handle.net/10356/105499
http://hdl.handle.net/10220/47825
ISSN: 1552-3098
DOI: 10.1109/TRO.2018.2819195
Rights: © 2018 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: https://doi.org/10.1109/TRO.2018.2819195
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Journal Articles
MAE Journal Articles

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