Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-7· 0 citations· 18 references
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.
Trajectory curvature constraints are inherent in practical multi-robot systems due to the limited turning capabilities of the robots. Without properly accounting for these constraints, robots may fail to accomplish assigned tasks, and their trajectories may diverge from the intended paths. This paper proposes a distrib...
Zhou-Ru Xiao, Tao Teng, Wei-Jia Yao et al.· 0 citations
Autonomous navigation in mobile robots requires reliable path following and obstacle avoidance, especially in dynamic environments. Differential drive robots have limitations due to non-holonomic constraints, making accurate path tracking more challenging. This paper proposes a Model Predictive Control (MPC) for path f...
T. Agustinah, Fadlan Hafiz Harahap, Y. E. Nugraha et al.· International Seminar on Int...· 0 citations
In this paper, we propose a new robust navigation framework for path following tasks in robots operating within unknown, cluttered environments. Our approach ensures reactive safety through obstacle avoidance and guaranteed convergence to a target path, while simultaneously mitigating the impact of unknown-but-bounded...
Arthur H. D. Nunes, V. M. Goncalves, G. Raffo et al.· 1 citation
Simulation results for a three-UAV swarm in a cluttered environment demonstrate that the proposed distributed NMPC-based trajectory planning method can generate dynamically feasible and collision-free trajectories, while enabling the swarm to reach the assigned target positions and preserve the desired formation within...
Ying-Ting Cui, Tong-Xin Zeng, Bin Li· Drones· 0 citations
Autonomous mobile robots face significant challenges when navigating complex environments that contain some static and dynamic obstacles. Traditional safety control methods based on control barrier functions (CBFs) with quadratic programming (QP) often suffer from high computational burden and vulnerability to deadlock...
Junming Ren, Lihan Chen, Lijun Long· Transactions of the Institut...· 0 citations
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