Robot navigation using convex model predictive control and approximate operating region optimization

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)

Abstract

A method for real-time robot navigation with obstacle avoidance is presented. A two-stage approach is proposed: the use of online Simulated Annealing (SA) to optimize a convex operating region in configuration space is paired with Model Predictive Control (MPC) to determine a locally optimal motion plan. The method retains recursive feasibility guarantees from MPC and speed of solution of the convex optimal control problem. Meanwhile, the convexification performed by the SA enables the method to operate with unstructured environment representations, such as point clouds or line scans.
Original languageEnglish
Title of host publication2017 IEEE/RSJ Conference on Intelligent Robots and Systems (IROS 2017)
Subtitle of host publicationProceedings of a meeting held 24-28 September 2017, Vancouver, British Columbia, Canada
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2171-2176
Number of pages6
ISBN (Electronic)9781538626825
ISBN (Print)9781538626832
DOIs
Publication statusPublished - Feb 2018
EventIntelligent Robots and Systems - Vancouver, Canada
Duration: 25 Sep 2017 → …

Publication series

Name
ISSN (Print)2153-0866

Conference

ConferenceIntelligent Robots and Systems
Abbreviated titleIROS
CountryCanada
CityVancouver
Period25/09/17 → …

    Fingerprint

Cite this

Bali, C., & Richards, A. (2018). Robot navigation using convex model predictive control and approximate operating region optimization. In 2017 IEEE/RSJ Conference on Intelligent Robots and Systems (IROS 2017): Proceedings of a meeting held 24-28 September 2017, Vancouver, British Columbia, Canada (pp. 2171-2176). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IROS.2017.8206035