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Hierarchical and Safe Motion Control for Cooperative Locomotion of Robotic Guide Dogs and Humans: A Hybrid Systems Approach

Akbari Hamed, Kaveh and Kamidi, Vinay R. and Ma, Wen-Loong and Leonessa, Alexander and Ames, Aaron D. (2020) Hierarchical and Safe Motion Control for Cooperative Locomotion of Robotic Guide Dogs and Humans: A Hybrid Systems Approach. IEEE Robotics and Automation Letters, 5 (1). pp. 56-63. ISSN 2377-3766. https://resolver.caltech.edu/CaltechAUTHORS:20190626-133626148

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Abstract

This letter presents a hierarchical control strategy based on hybrid systems theory, nonlinear control, and safety-critical systems to enable cooperative locomotion of robotic guide dogs and visually impaired people. We address high-dimensional and complex hybrid dynamical models that represent collaborative locomotion. At the high level of the control scheme, local and nonlinear controllers, based on the virtual constraints approach, are designed to induce exponentially stable dynamic gaits. The local controller for the leash is assumed to be a nonlinear controller that keeps the human in a safe distance from the dog while following it. At the lower level, a real-time quadratic programming (QP) is solved for modifying the local controllers of the robot as well as the leash to avoid obstacles. In particular, the QP framework is set up based on control barrier functions (CBFs) to compute optimal control inputs that guarantee safety while being close to the local controllers. The stability of the complex periodic gaits is investigated through the Poincaré return map. To demonstrate the power of the analytical foundation, the control algorithms are transferred into an extensive numerical simulation of a complex model that represents cooperative locomotion of a quadrupedal robot, referred to as Vision 60, and a human model. The complex model has 16 continuous-time domains with 60 state variables and 20 control inputs.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/LRA.2019.2939719DOIArticle
https://arxiv.org/abs/1904.03158arXivDiscussion Paper
ORCID:
AuthorORCID
Akbari Hamed, Kaveh0000-0001-9597-1691
Kamidi, Vinay R.0000-0002-4227-6131
Ma, Wen-Loong0000-0002-0115-5632
Leonessa, Alexander0000-0001-9317-2714
Ames, Aaron D.0000-0003-0848-3177
Additional Information:© 2019 IEEE. Manuscript received April 4, 2019; accepted August 17, 2019. Date of publication September 5, 2019; date of current versionNovember 13, 2019. This letter was recommended for publication by Associate Editor S. Oh and Editor N. Tsagarakis upon evaluation of the reviewers’ comments. The work of K. Akbari Hamed was supported in part by the National Science Foundation (NSF) under Grants 1854898, 1906727, 1923216, and 1924617. The work of V. R. Kamidi was supported in part by the NSF Grant 1854898. The work of A. D. Ames was supported in part by the NSF under Grants 1544332, 1724457, 1724464, 1923239, and 1924526 and in part by Disney Research LA. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation (NSF).
Funders:
Funding AgencyGrant Number
NSFCMMI-1854898
NSFCMMI-1906727
NSFCMMI-1923216
NSF1924617
NSFCMMI-1854898
NSFCNS-1544332
NSFCNS-1724457
NSFIIS-1724464
NSFCMMI-1923239
NSF1924526
Disney Research LAUNSPECIFIED
Subject Keywords:Legged Robots, Motion Control, Dynamics
Issue or Number:1
Record Number:CaltechAUTHORS:20190626-133626148
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190626-133626148
Official Citation:K. A. Hamed, V. R. Kamidi, W. Ma, A. Leonessa and A. D. Ames, "Hierarchical and Safe Motion Control for Cooperative Locomotion of Robotic Guide Dogs and Humans: A Hybrid Systems Approach," in IEEE Robotics and Automation Letters, vol. 5, no. 1, pp. 56-63, Jan. 2020. doi: 10.1109/LRA.2019.2939719
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96743
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:26 Jun 2019 20:50
Last Modified:15 Nov 2019 18:13

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