Coordinated Multi-Robot Trajectory Tracking Control over Sampled Communication

TL;DR

This paper introduces a distributed inverse-kinematics control method for multi-robot systems operating over sampled communication, ensuring coordinated trajectory tracking despite communication delays and sampling constraints.

Contribution

It presents a novel distributed control approach combining sampled proportional feedback with a continuous-time feedforward, along with stability analysis and real-world simulation validation.

Findings

Achieves coordinated trajectory tracking with minimal communication overhead.

Provides stability and convergence analysis in sampled communication scenarios.

Demonstrates comparable performance to centralized control in simulations.

Abstract

In this paper, we propose an inverse-kinematics controller for a class of multi-robot systems in the scenario of sampled communication. The goal is to make a group of robots perform trajectory tracking in a coordinated way when the sampling time of communications is much larger than the sampling time of low-level controllers, disrupting theoretical convergence guarantees of standard control design in continuous time. Given a desired trajectory in configuration space which is precomputed offline, the proposed controller receives configuration measurements, possibly via wireless, to re-compute velocity references for the robots, which are tracked by a low-level controller. We propose joint design of a sampled proportional feedback plus a novel continuous-time feedforward that linearizes the dynamics around the reference trajectory: this method is amenable to distributed communication implementation where only one broadcast transmission is needed per sample. Also, we provide closed-form expressions for instability and stability regions and convergence rate in terms of proportional gain $k$ and sampling period $T$. We test the proposed control strategy via numerical simulations in the scenario of cooperative aerial manipulation of a cable-suspended load using a realistic simulator (Fly-Crane). Finally, we compare our proposed controller with centralized approaches that adapt the feedback gain online through smart heuristics, and show that it achieves comparable performance.