# Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading
###### Abstract
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Figure 1: The scenario of interest, comprising a fleet of AVs, a network of BSs, and remote DCs offering AI services. which moves in a complex indoor environment. Object detection is offloaded to external edge/cloud computers for the purpose of identifying objects in the captured scene, and tracking is employed to follow the object in the environment (person tracking). There, the choice of the access radio UL and of the offloading computer was implemented by a very simple algorithm, which did not take into account a global optimization strategy, which is necessary when multiple AVs struggle to exploit the same radio and computation resources. In this work, we propose some novel algorithms that can be used to assign the optimal configuration of BSs and DCs that connects each AV to an offloading service so that a selected KPI (e.g., the average latency, the handover rate, the access bit rate) can be optimized. Each AV expects a response from the server after a certain delay or latency, and we chose to minimize the average latency of all AVs. Differently from previous works in the literature, such as [13, 14], which use a queuing model, our simpler model accepts all the requests in parallel, and proportionally delays all the responses to satisfy the constraints on the limited radio and computation resources. The concurrent selection of access BS and offloading DC, for all AVs, defines a network configuration that may increase or decrease the average fleet latency, depending on the chosen links and on the possibility of resource overloading. The configuration should be found quickly so that the fleet operation remains functional and optimal also in rapidly changing scenarios. Since this problem falls in the category of combinatorial optimization, we resort to well-known techniques for providing an estimate of the solution, such as random search or metaheuristic algorithms. We also propose a new iterative algorithm, based on sequential minimization, that can produce near-optimal results with a reasonable complexity, with performance comparable to that of exhaustive and metaheuristic methods. The problem of latency minimization has also been investigated in [15], which proposed the minimum latency (MLAT) algorithm. MLAT provides a low-complexity suboptimal solution to the latency minimization; however, we show that, despite the name of the algorithm, the average latency of MLAT is significantly larger than the latency obtained by the sequential minimization algorithm newly proposed in this paper. This paper is organized as follows: Section 2 discusses previous works on this subject and highlights similarities and differences with our work. The mathematical model of the system introduced here is detailed in Section 3, where we also provide an example, formulate the optimization problem with a mathematical notation, and discuss its complexity. Section 4 is dedicated to the presentation of the proposed algorithms, which try to solve the optimization problem. Simulations are described and their results discussed in Section 5, while conclusions are drawn in Section 6.