Work

Adaptive Parameter Avoidance Control and Safety-Corrected Tracking Framework for Multi-Agent Differential Drive Vehicles

Year: 2026

Type: article

Abstract: This paper presents a closed-form tracking and collision avoidance framework for multi-agent differential drive robots. Existing reactive methods often rely on purely geometric proximity, leading to conservative detours and local minima. A state-dependent adaptive avoidance strategy is developed to dynamically modulate repulsive forces using the time-derivative of fractional barrier risk functions, alleviating unnecessary evasive maneuvers. Within a convergence vector field (CVF) architecture, an active safety-corrected tracking mechanism orthogonally strips hazardous velocity projections from the spatial error. This mitigates the inherent conflict between target tracking and obstacle repulsion. A matrix projection-based Lyapunov approach demonstrates the finite-time convergence of the vehicle orientation, bounded tracking errors, and collision-free properties of the closed-loop system, with effectiveness further validated through simulations.

Source: Actuators

DOI: 10.3390/act15040229

Authors: Wenxue Zhang, Bingkun Shi, Dušan M. Stipanović, Ning Zong

Institutions: Hebei University of Technology, University of Illinois Urbana-Champaign

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Hebei Provincial Department of Bureau of Science and Technology