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Journal Articles Automatica Year : 2023

Coordinated Multi-Robot Trajectory Tracking Control over Sampled Communication ⋆


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.
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hal-04010855 , version 1 (02-03-2023)





Enrica Rossi, Marco Tognon, Luca Ballotta, Ruggero Carli, Juan Cortés, et al.. Coordinated Multi-Robot Trajectory Tracking Control over Sampled Communication ⋆. Automatica, 2023, 151, pp.110941. ⟨10.1016/j.automatica.2023.110941⟩. ⟨hal-04010855⟩
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