This paper addresses the problem of motion planning for an autonomous, tracked mobile robot whose mathematical model depends on uncertain parameters, with constrained moving capabilities, operating in a cluttered environment. The proposed algorithm exploits the concept of one-step ahead controllable sets. In particular, motion sequences compatible with uncertainties and nonlinear model dynamics are determined in the off-line phase with the aim to determine a collision-free path capable to accomplish the given mission. Conversely, the on-line operations are devoted to determine the most appropriate control action by solving a computationally simple optimization problem. Finally, some preliminary numerical results are instrumental to testify the effectiveness of the proposed approach.

Set-theoretic approach for autonomous tracked vehicles involved in post-disaster first relief operations / Scordamaglia, V.; Ferraro, Alessia; Tedesco, F.; Franze, G.. - 28:(2024), pp. 2006-2011. ( International Conference on Control, Decision and Information Technologies (CoDIT) La Valletta 1-4 luglio 2024) [10.1109/CoDIT62066.2024.10708445].

Set-theoretic approach for autonomous tracked vehicles involved in post-disaster first relief operations

Scordamaglia V.
;
Ferraro Alessia;
2024-01-01

Abstract

This paper addresses the problem of motion planning for an autonomous, tracked mobile robot whose mathematical model depends on uncertain parameters, with constrained moving capabilities, operating in a cluttered environment. The proposed algorithm exploits the concept of one-step ahead controllable sets. In particular, motion sequences compatible with uncertainties and nonlinear model dynamics are determined in the off-line phase with the aim to determine a collision-free path capable to accomplish the given mission. Conversely, the on-line operations are devoted to determine the most appropriate control action by solving a computationally simple optimization problem. Finally, some preliminary numerical results are instrumental to testify the effectiveness of the proposed approach.
2024
979-8-3503-7397-4
Uncertainty, Tracking, Computational modeling, Dynamics, Mathematical models, Planning, Numerical models, Vehicle dynamics, Collision avoidance, Optimization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/152187
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