Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146762
Title: RoboTSP - a fast solution to the robotic task sequencing problem
Authors: Suárez-Ruiz, Francisco
Lembono, Teguh Santoso
Pham, Quang-Cuong
Keywords: Engineering::Mechanical engineering
Issue Date: 2018
Source: Suárez-Ruiz, F., Lembono, T. S., & Pham, Q.-C. (2018). RoboTSP - a fast solution to the robotic task sequencing problem. Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA), 1611-1616. doi:10.1109/ICRA.2018.8460581
Project: NGF-2016-01-028
NG000074-ENG
Abstract: In many industrial robotics applications, such as spot-welding, spray-painting or drilling, the robot is required to visit successively multiple targets. The robot travel time among the targets is a significant component of the overall execution time. This travel time is in turn greatly affected by the order of visit of the targets, and by the robot configurations used to reach each target. Therefore, it is crucial to optimize these two elements, a problem known in the literature as the Robotic Task Sequencing Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a fast, near-optimal, algorithm to solve RTSP. The key to our approach is to exploit the classical distinction between task space and configuration space, which, surprisingly, has been so far overlooked in the RTSP literature. Second, we provide an open-source implementation of the above algorithm, which has been carefully benchmarked to yield an efficient, ready-to-use, software solution. We discuss the relationship between RTSP and other Traveling Salesman Problem (TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and show experimentally that our method finds motion sequences of the same quality but using several orders of magnitude less computation time than existing approaches.
URI: https://hdl.handle.net/10356/146762
ISBN: 9781538630815
DOI: 10.1109/ICRA.2018.8460581
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/ICRA.2018.8460581.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers

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