MIVG: Mode-Isolated Velocity-Guide Algorithm for Quadratic Optimization-Based Obstacle Avoidance
Hangyu Lin, Xiaoqi Chen, Songyin Cai, Xiangrui Lin, Kunpeng Wu
Abstract
Dynamic obstacle avoidance is a challenging problem in robotic control, with many algorithms developed to balance efficiency and real-time performance. Existing resolved-rate motion control (RRMC) methods formulate obstacle avoidance as a quadratic programming (QP) problem. However, the lack of directional guidance for obstacle avoidance and frequent constraint conflicts often lead to execution failures. In this work, we propose the Mode-Isolated Velocity-Guide (MIVG) algorithm that deploys a dual-mode isolation strategy combined with a Velocity-Guide Potential Field (VGPF). This novel approach separates obstacle avoidance from target-driven tasks while providing velocity-based directional guidance. Simulations on a 7-degree-of-freedom Franka Emika Panda robot demonstrate that our approach significantly enhances task success rates while maintaining real-time feasibility, achieving an increase in execution success rates of 35.0% ~ 52.0% compared to the baseline RRMC strategy (NEO). Additionally, we analyze the impact of key parameters through simulations, further validating the effectiveness of the proposed algorithm in dynamic environments.
BibTeX
@inproceedings{iros2025_mivgmodeisolated,
title = {MIVG: Mode-Isolated Velocity-Guide Algorithm for Quadratic Optimization-Based Obstacle Avoidance},
author = {Hangyu Lin and Xiaoqi Chen and Songyin Cai and Xiangrui Lin and Kunpeng Wu},
booktitle = {IROS 2025},
year = {2025}
}