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Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading
| Publication type | Journal paper |
|---|---|
| Year of publication | 2025 |
| Authors | Giuseppe Baruffa and Luca Rugini |
| Title | Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading |
| Journal title | Future Internet |
| Volume | 17 |
| Issue | 1 |
| Pages | 1–27 |
| Editor | |
| Publisher | MDPI |
| Date | January 2025 |
| Place | art. no. 39 |
| ISSN number | 1999-5903 |
| ISBN number | |
| Key words | Mobile robots, radio resource assignment, task offloading, metaheuristic optimization, latency minimization |
| 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 need also a 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 the 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 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. |
| URL | https://www.mdpi.com/1999-5903/17/1/39 |
| DOI | http://dx.doi.org/10.3390/fi17010039 |
| Other information | |
| Paper |  (portable document format, 1258628 Bytes) |