Control Barrier Function Augmentation in Sampling-based Control Algorithm for Sample Efficiency

Chuyuan Tao, Hunmin Kim, Hyungjin Yoon, Naira Hovakimyan, Petros G Voulgaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For a nonlinear stochastic path planning problem, sampling-based algorithms generate thousands of random sample trajectories to find the optimal path while guaranteeing safety by Lagrangian penalty methods. However, the sampling-based algorithm can perform poorly in obstacle-rich environments because most samples might violate safety constraints, invalidating the corresponding samples. To improve the sample efficiency of sampling-based algorithms in cluttered environments, we propose an algorithm based on model predictive path integral control and control barrier function (CBF). The proposed algorithm needs fewer samples and time-steps and has a better performance in cluttered environments compared to the original model predictive path integral control algorithm.

Original languageEnglish (US)
Title of host publication2022 American Control Conference, ACC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3488-3493
Number of pages6
ISBN (Electronic)9781665451963
DOIs
StatePublished - 2022
Event2022 American Control Conference, ACC 2022 - Atlanta, United States
Duration: Jun 8 2022Jun 10 2022

Publication series

NameProceedings of the American Control Conference
Volume2022-June
ISSN (Print)0743-1619

Conference

Conference2022 American Control Conference, ACC 2022
Country/TerritoryUnited States
CityAtlanta
Period6/8/226/10/22

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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