Parameter prediction of control barrier function parameters for robotic manipulator obstacle avoidance

Stephen McIlvanna, Mien Van*, Yuzhu Sun, Nhat Nguyen Minh, Wasif Naeem

*Corresponding author for this work

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

Abstract

In this paper we present the implementation of a Control Barrier Function (CBF) safety filter that provides obstacle avoidance for a robotic manipulator arm system in a simulated environment (Simulink). CBF is a control technique that has developed over the past decade and has been extensively explored in the literature on its mathematical foundations and potential applications for a variety of safety-critical control systems. In this work we will look at the design of CBF for the robotic manipulator obstacle avoidance, discuss the selection of the CBF parameters and present a search algorithm to find parameters that provide the most efficient trajectory for different obstacles. We then create a data-set across a range of obstacle scenarios that is used to train a Neural-Network (NN) model that can be used within the control scheme to allow the system to efficiently adapt to different obstacle scenarios.

Original languageEnglish
Title of host publication49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023): proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350331820
ISBN (Print)9798350331837
DOIs
Publication statusPublished - 16 Nov 2023
EventIECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society - , Singapore
Duration: 16 Oct 202319 Oct 2023

Publication series

NameIECON - Annual Conference of the IEEE Industrial Electronics Society: proceedings
ISSN (Print)1553-572X
ISSN (Electronic)2577-1647

Conference

ConferenceIECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
Country/TerritorySingapore
Period16/10/202319/10/2023

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