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}
}

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Date Posted: 02 October 2017

This document has been peer reviewed.