Multi-Robot Segregation Using Finite-Time MPC with Chernoff Bound-Based Asynchronous Motion Smoothing
Richa Dubey, Shreyash Gupta, Saurabh Chaudhary, Niladri Sekhar Tripathy, Suril Vijaykumar Shah
AI summary
Problem
Existing multi-robot segregation methods lack guaranteed finite-time convergence, robust collision avoidance, and systematic, non-heuristic thresholds to mitigate external state perturbations.
Approach
The authors develop a Finite-time MPC controller that uses control invariant sets to guarantee bounded convergence time, paired with a data-driven Chernoff bound threshold to trigger asynchronous motion smoothing only when perturbations exceed statistically derived limits.
Key results
- Analytical upper bound on convergence time for segregated formation
- Data-driven Chernoff bound threshold for asynchronous motion smoothing
- On-demand linearized collision avoidance constraint within MPC
- Validated segregation of five robots into two groups via simulation and hardware experiments
Why it matters
It provides a theoretically guaranteed, robust control framework for dynamic multi-robot coordination, directly benefiting real-world applications like search-and-rescue and surveillance that require precise timing and collision-free operation.
Abstract
For multi-robot systems operating in dynamic envi- ronments, collision-free segregation into a desired set of groups in finite time is an essential task requirement in many applica- tions. This work presents a control framework for such systems, utilizing Finite-time Model Predictive Control. The objective is to guide the robots toward a segregated formation while adhering to leader-follower dynamics and effectively avoiding collisions. To ensure finite-time convergence, the concept of a control invariant set is incorporated. Furthermore, the letter derives an upper bound on the required time steps for the robots to achieve the segre- gated formation. In order to maintain a smooth motion profile in the face of external state perturbations, this work proposes a data-driven Chernoff bound-based triggering method that enables Asynchronous Motion Smoothing for the robots. To validate the effectiveness of the proposed control framework, both simulations and hardware experiments are conducted, focusing on the segre- gation of five robots into two distinct groups.