# Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading
###### 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.  
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efficient offloading in edge computing include latency, data rate, energy consumption, seamless access, and network connectivity, which are typically traded off with throughput, service blockage, handover, and computation accuracy [11]. Figure 1 depicts the general scenario we are considering. A fleet of AVs are connected to a network of servers located in remote data centers (DCs), over which each AV offloads an AI task that, due to its complex nature, is central processing unit (CPU)-intensive. For instance, the AV might request object detection on the scene captured by its onboard cameras: the detection results, coming back from the server, are used to make important decisions for the safety of the AV and/or of persons that are close. In one case, the AV could decide to move along a path that skips particular objects (e.g., obstacle avoidance), while in another case, it could choose to follow a specific object (e.g., person tracking). Note that, depending on the celerity at which responses are received from the servers, these decisions could be more or less delayed, thus negatively impacting the global performance of the task assigned to the AV fleet, which has to fulfill a delivery or exploratory mission by visiting a number of waypoints [12]. This scenario includes obstacles, which have to be recognized and avoided using onboard sensors and (possibly offloaded) object detection services. Image streaming is quite data-intensive, especially if high-resolution or depth cameras are used onboard, so the bit rate produced by the AVs needs high-speed wireless links. For this purpose, a network of base stations (BS) located in the area of interest can be used to access the Internet and, then, to reach the servers. Radio resources are limited, so when multiple AVs try to access the servers using the same BS, the link is overloaded, generating additional delays and an effective access bit rate reduction. Similarly, if multiple AVs offload their tasks to the same DC, its computation resources can be overwhelmed, and the operating system (OS) scheduler or threading model in the service would produce an increased delay in the response. In summary, the typical latency that characterizes each offloading service is incremented by such additional delays, which are encountered either at the radio layer or at the service layer. The described scenario is an evolution of that in [10], which describes a testbed composed of a single AV and up to three different BSs. Namely, the AV is a ground mobile robot equipped with a camera and multiple radio access technologies (Wi-Fi, 5G, etc.