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

Danilo Pianini, Federico Pettinari, Roberto Casadei , Lukas Esterle

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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.

Original languageEnglish
Article number4
JournalACM Transactions on Autonomous and Adaptive Systems
Pages (from-to)1-39
Number of pages39
Publication statusPublished - Jun 2022


  • Internet of things
  • Location based services
  • aggregate computing
  • multi-robot
  • online multi-object k-coverage
  • smart cameras


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