Coordinated Multi-Robot Trajectory Tracking Control over Sampled Communication
This addresses the challenge of reliable multi-robot coordination in real-world applications like aerial manipulation, but it is incremental as it builds on existing control methods with a novel hybrid design for sampled communication.
The paper tackles the problem of coordinated trajectory tracking for multi-robot systems under sampled communication, where large sampling times disrupt standard control guarantees, by proposing an inverse-kinematics controller that combines sampled feedback and continuous feedforward to achieve stability and convergence with distributed communication. It provides closed-form stability regions and shows comparable performance to centralized approaches in simulations of aerial manipulation.
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.