Multi-Robot Formation Control Via Consensus-Based Sliding Mode and Obstacle-Aware Adaptive Scaling
Hsien-I Lin, Yu-Xian Chen
AI summary
Problem
Existing multi-robot formation control methods struggle to simultaneously maintain high tracking accuracy, formation consistency, and real-time adaptability in constrained environments, while lacking unified metrics and comprehensive real-world validation.
Approach
A consensus-based sliding mode controller that combines graph-theoretic coordination with robust sliding-mode control and dynamic formation scaling, enabling robots to maintain precise shapes while navigating obstacles in real-time.
Key results
- Bounded integral sliding mode controller design
- LiDAR-fused perception with obstacle-aware adaptive scaling
- Superior tracking accuracy and formation consistency vs. SMC and flocking baselines
- Validated in NVIDIA Isaac Sim and real-world Mecanum-robot tests
Why it matters
Enables reliable, scalable multi-robot coordination for real-world industrial and exploration applications where dynamic environments and strict formation requirements are common.
Abstract
This paper proposes a consensus-based sliding mode controller (CSMC) for multi-robot formation control. The framework integrates Laplacian-based consensus with sliding- mode robustness and adaptive formation scaling to simultane- ously achieve accurate formation tracking and high formation consistency, while ensuring flexibility in constrained environ- ments. The approach is validated in NVIDIA Isaac Sim and real-world experiments with Mecanum-wheeled robots. Com- pared with conventional sliding mode control (SMC), CSMC achieves consistent improvements in formation consistency, tracking accuracy, and overall performance in both simulation and real-world experiments. When compared with flocking- based approaches, CSMC provides substantially improved tracking performance and achieves better overall performance under consistency-prioritized evaluation metrics. These results demonstrate the effectiveness of CSMC in achieving reliable formation tracking, consistent coordination, and adaptive for- mation scaling for multi-robot navigation.