ASSESSMENT OF THE HARDWARE COMPOSITION OF ONBOARD COMPUTING SYSTEMS OF ROBOTIC COMPLEXES BASED ON THE TASKS BEING SOLVED
Abstract
In the development of modern robotic complexes (RC), there is a significant diversity in both hardware and software solutions, which creates additional challenges in selecting a rational hardware and software composition to ensure the required computational power and to effectively address the assigned tasks. On one hand, it is often necessary to work with an already installed set of computing systems (CS) that form the onboard computing system (OCS) of the RC, which substantially limits the possibilities for modifying the software composition and necessitates the adaptation of algorithms to fixed hardware resources. On the other hand, when there is an opportunity to modify or create a new hardware composition, it becomes necessary to choose a hardware configuration that can meet the computational requirements of the tasks being solved. This article proposes a methodology for assessing the hardware composition of the OCS of RCs based on the tasks being solved, relying on the use of multiversion programming and the creation of solution passports. Each variant of the software solution for a specific task is supplemented by a structured passport that contains both quantitative and qualitative characteristics, allowing for a detailed comparative analysis. Based on these solution passports, a mathematical model is developed that enables the selection of a set of computing devices capable of executing all the assigned tasks while simultaneously minimizing the total cost, energy consumption, or other operational characteristics of the OCS. Mathematically the problem under consideration is reduced to a generalized multiplicative multidimensional knapsack problem with multi-choice and additional constraints, which allows both resource and topological dependencies among the tasks being solved to be taken into account. Experimental results obtained using the developed simulation platform are presented, demonstrating the practical applicability of the methodology and confirming the possibility of using it to obtain quantitative estimates of the hardware composition variants of the OCS of RCs. This approach can be adapted for various types of RCs, which facilitates its use in related studies in the field of optimizing computing systems for robotic complexes
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