
Departmental Papers (ESE)
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Abstract
In robotics, it is often practically and theoretically convenient to design motion planners for approximate low-order (e.g., position- or velocity-controlled) robot models first, and then adapt such reference planners to more accurate high-order (e.g., force/torque-controlled) robot models. In this paper, we introduce a novel provably correct approach to extend the applicability of low-order feedback motion planners to high-order robot models, while retaining stability and collision avoidance properties, as well as enforcing additional constraints that are specific to the high-order models. Our smooth extension framework leverages the idea of reference governors to separate the issues of stability and constraint satisfaction, affording a bidirectionally coupled robot-governor system where the robot ensures stability with respect to the governor and the governor enforces state (e.g., collision avoidance) and control (e.g., actuator limits) constraints. We demonstrate example applications of our framework for augmenting path planners and vector field planners to the second-order robot dynamics.
Sponsor Acknowledgements
This work was supported in part by AFRL grant FA865015D1845 (subcontract 669737–1).
Document Type
Conference Paper
Subject Area
GRASP, Kodlab
Date of this Version
5-29-2017
Publication Source
2017 IEEE International Conference on Robotics and Automation (ICRA2017)
Start Page
4414
Last Page
4421
DOI
10.1109/ICRA.2017.7989510
Keywords
smooth extensions; command governors; reference governors; collision avoidance;robot dynamics;stability;collision avoidance;constraint satisfaction;feedback motion planners;path planners;reference governors;robot models;robot-governor system;robotics;second-order robot dynamics;smooth extensions;stability;vector field planners;Asymptotic stability;Collision avoidance;Computational modeling;Dynamics;Navigation;Robots;Stability analysis
Bib Tex
@InProceedings{arslan_koditschek_ICRA2017, Title = {Smooth Extensions of Feedback Motion Planners via Reference Governors}, Author = {Omur Arslan and Daniel E. Koditschek}, Booktitle = {2017 IEEE International Conference on Robotics and Automation}, Year = {2017},
Pages = {4414-4421},
DOI = {10.1109/ICRA.2017.7989510}
}
Date Posted: 02 October 2017
This document has been peer reviewed.