We focus on the online multi-object k-coverage problem (OMOkC), where mobile robots are required to sense a mobile target from k diverse points of view, coordinating themselves in a scalable and possibly decentralised way. There is active research on OMOkC, particularly in the design of decentralised algorithms for solving it. We propose a new take on the issue: Rather than classically developing new algorithms, we apply a macro-level paradigm, called aggregate computing, specifically designed to directly program the global behaviour of a whole ensemble of devices at once. To understand the potential of the application of aggregate computing to OMOkC, we extend the Alchemist simulator (supporting aggregate computing natively) with a novel toolchain component supporting the simulation of mobile robots. This way, we build a software engineering toolchain comprising language and simulation tooling for addressing OMOkC. Finally, we exercise our approach and related toolchain by introducing new algorithms for OMOkC; we show that they can be expressed concisely, reuse existing software components and perform better than the current state-of-the-art in terms of coverage over time and number of objects covered overall.

Pianini, D., Pettinari, F., Casadei, R., Esterle, L. (2022). A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems. ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS, 17(1-2), 1-39 [10.1145/3547145].

A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems

Pianini, Danilo
Primo
;
Casadei, Roberto;Esterle, Lukas
2022

Abstract

We focus on the online multi-object k-coverage problem (OMOkC), where mobile robots are required to sense a mobile target from k diverse points of view, coordinating themselves in a scalable and possibly decentralised way. There is active research on OMOkC, particularly in the design of decentralised algorithms for solving it. We propose a new take on the issue: Rather than classically developing new algorithms, we apply a macro-level paradigm, called aggregate computing, specifically designed to directly program the global behaviour of a whole ensemble of devices at once. To understand the potential of the application of aggregate computing to OMOkC, we extend the Alchemist simulator (supporting aggregate computing natively) with a novel toolchain component supporting the simulation of mobile robots. This way, we build a software engineering toolchain comprising language and simulation tooling for addressing OMOkC. Finally, we exercise our approach and related toolchain by introducing new algorithms for OMOkC; we show that they can be expressed concisely, reuse existing software components and perform better than the current state-of-the-art in terms of coverage over time and number of objects covered overall.
2022
Pianini, D., Pettinari, F., Casadei, R., Esterle, L. (2022). A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems. ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS, 17(1-2), 1-39 [10.1145/3547145].
Pianini, Danilo; Pettinari, Federico; Casadei, Roberto; Esterle, Lukas
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/903534
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