Adaptive Formation with Collision Avoidance based on VS-CBF and MPC for Mobile Multi-Robots

Abstract

This work presents an adaptive formation and collision avoidance framework for differential-drive multi-robot systems. The strategy is based on a deformable virtual structure for planning, control barrier functions for safety, and a decentralized model predictive control to drive robot motion. The virtual structure acts as a centralized planner that generates smooth, collision-aware slot trajectories, while each robot uses a local model predictive control to track its assigned reference under kinematic and wheel-speed constraints. Similarly, control barrier functions modify motion and deformation states to guarantee obstacle evasion without requiring rigid formation constraints. The method is validated experimentally using four differential drive mobile robots navigating multiple obstacles, demonstrating safe deformation, coordinated motion, and tracking despite non-ideal initial poses and continuous reference movement. The results suggest that the proposed navigation and control scheme offers a scalable, adaptive, and robust approach for multi-robot formation control with collision avoidance.