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Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading

Author

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  • Giuseppe Baruffa

    (Department of Engineering, University of Perugia, I-06125 Perugia, Italy)

  • Luca Rugini

    (Department of Engineering, University of Perugia, I-06125 Perugia, Italy)

Abstract

This paper deals with the optimization of the operational efficiency of a fleet of mobile robots, assigned with delivery-like missions in complex outdoor scenarios. The robots, due to limited onboard computation resources, need to offload some complex computing tasks to an edge/cloud server, requiring artificial intelligence and high computation loads. The mobile robots also need reliable and efficient radio communication with the network hosting edge/cloud servers. The resource assignment aims at minimizing the total latency and delay caused by the use of radio links and computation nodes. This minimization is a nonlinear integer programming problem, with high complexity. In this paper, we present reduced-complexity algorithms that allow to jointly optimize the available radio and computation resources. The original problem is reformulated and simplified, so that it can be solved by also selfish and greedy algorithms. For comparison purposes, a genetic algorithm (GA) is used as the baseline for the proposed optimization techniques. Simulation results in several scenarios show that the proposed sequential minimization (SM) algorithm achieves an almost optimal solution with significantly reduced complexity with respect to GA.

Suggested Citation

  • Giuseppe Baruffa & Luca Rugini, 2025. "Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading," Future Internet, MDPI, vol. 17(1), pages 1-27, January.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:1:p:39-:d:1568638
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    References listed on IDEAS

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    1. Alexandros Nikitas & Kalliopi Michalakopoulou & Eric Tchouamou Njoya & Dimitris Karampatzakis, 2020. "Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
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