基于IBVS-预设性能约束的机器人编队与静态避障控制

IBVS-Prescribed Performance Constrained Formation Control andStatic Obstacle Avoidance for Mobile Robots

摘要:

针对基于图像视觉伺服(IBVS)的领航-跟随编队控制和静态避障问题,提出了一种非线性增益控制律与人工势场法相结合的编队控制方法。首先,通过预设性能函数与误差非线性映射将受视场角约束的像素误差转换到无约束空间下,设计了一种非线性增益控制律,调节跟随机器人的运动速度以保持编队队形；其次,利用传感器获得的环境中障碍物的距离,构建斥力势场,并将该斥力叠加到编队控制律中,形成融合斥力的控制输入,且像素误差收敛至预设的性能边界内；并基于Lyapunov稳定性理论及一致最终有界(UUB)分析证明了闭环系统的稳定性；最后,通过Matlab仿真与实物平台实验验证该方法可有效实现移动机器人的队形保持与避障功能。

Abstract:

Aimed at vision-based leader-follower formation control and static obstacle avoidance utilizing image-based visual servoing (IBVS), a formation control method integrating a nonlinear gain control law with the artificial potential field (APF) is proposed. To maintain formation geometry, a nonlinear gain control law is designed to regulate the motion velocity of follower robots by transforming pixel errors constrained by field-of-view (FOV) limitations into an unconstrained space through preset performance functions and nonlinear error mapping. Repulsive potential fields are constructed from sensor-acquired obstacle distances and fused into the control framework as repulsion-integrated inputs, ensuring that the pixel error converges to the predefined performance boundary. The stability of the closed-loop system is proven using Lyapunov stability theory and uniformly ultimately bounded (UUB) analysis. The effectiveness of the proposed method is demonstrated through Matlab simulations and physical experiments in achieving mobile robot formation and obstacle avoidance performance.