Deadlock-Resistant Safety-Critical Mobile Robot Navigation Using Guiding Vector Fields and Model Predictive Control.

Liang Gao, Yongfeng Gao, Xinhui Zhao, Xuefeng Li, Mingde He, Yuhu Wu

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Liang GaoKey Laboratory of Intelligent Control and Optimization for Industrial Equipment of the Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.ORCID 0009-0006-7399-3332

Yongfeng GaoKey Laboratory of Intelligent Control and Optimization for Industrial Equipment of the Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.

Xinhui ZhaoKey Laboratory of Intelligent Control and Optimization for Industrial Equipment of the Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.

Xuefeng LiKey Laboratory of Intelligent Control and Optimization for Industrial Equipment of the Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.ORCID 0009-0006-9976-8723

Mingde HeKey Laboratory of Intelligent Control and Optimization for Industrial Equipment of the Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.

Yuhu WuKey Laboratory of Intelligent Control and Optimization for Industrial Equipment of the Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.ORCID 0000-0001-9317-1404

Funding

National Natural Science Foundation of China U24A20263 , 62473071

6 · The paper itself

Abstract

This paper investigates the safe navigation of mobile robots in complex-static-obstacle environments and proposes a deadlock-resistant navigation control method. First, convex polygonal obstacles are modeled using the Log-Sum-Exp (LSE) function. Goal and obstacle avoidance guiding vector fields (GVFs) are then constructed, and an obstacle-bypassing method is developed using the goal direction and local obstacle geometry. The goal and obstacle avoidance GVFs are further blended to construct a navigation GVF, for which a regional sufficient condition excluding complete cancellation is established. On this basis, a safe robot navigation controller is designed by combining the navigation GVF with a model predictive control (MPC) framework incorporating discrete-time control barrier function (DCBF) constraints. We also give conditions for obstacle avoidance throughout each sampling interval under bounded one-step position prediction errors. Numerical simulations show that, in the tested scenarios, wherethe tested artificial potential field (APF) and conventional MPC-DCBF configurations fail to complete the navigation task, the proposed methodcan bypass obstacles and reach the goal. Hardware experiments further validate the effectiveness of the proposed method on a physical robot platform in a preconfigured environment.

Indexed as

deadlock avoidancediscrete-time control barrier functionguiding vector fieldmobile robot navigationmodel predictive control

Identifiers

PMID42884505

DOI10.3390/s26196304

PMCPMC13667760

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.