DGVO: A Dynamically Constrained Gradient Velocity Obstacle Approach for Mobile Robots in Dynamic Environments
Bowen Xiao, Bo Zhang, Danyu Zhang, Peiyan Xie, Xinyu Wang, Ruocheng Li
Abstract
In this paper, we propose a framework based on velocity obstacles to address dynamic obstacle avoidance problem for constrained mobile robots. The framework establishes a nonlinear mapping from the control domain to the velocity space based on the robot’s kinematic model and input constraints. This mapping defines the Velocity Feasible Region (VFR) as the set of reachable velocities at the next time step. Utilizing the VFR, we propose a gradient field, called the Dynamically Constrained Gradient Velocity Obstacle (DGVO), to represent the feasible motion region for mobile robots. DGVO preserves the original feasible region of the mobile robot. Based on DGVO, we formulate an unconstrained gradient descent optimization problem to compute collision-free velocities in real time. This framework enables real-time online computation of collision-free velocities for any constrained mobile robot, and it exhibits strong robustness to sensor noise. Extensive simulations and real-world experiments have validated the effectiveness of the proposed method. The introduction of the entire work can be found at the following link: https://youtu.be/HrTNTSOhKvE.
BibTeX
@inproceedings{iros2025_dgvoadynamically,
title = {DGVO: A Dynamically Constrained Gradient Velocity Obstacle Approach for Mobile Robots in Dynamic Environments},
author = {Bowen Xiao and Bo Zhang and Danyu Zhang and Peiyan Xie and Xinyu Wang and Ruocheng Li},
booktitle = {IROS 2025},
year = {2025}
}