摘要
由一位新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-机器人的新研究是一份报告的主旨。根据NewsRx记者来自弗吉尼亚州夏洛茨维尔的新闻报道，研究表明：“在我们这个快节奏、技术驱动的世界里，多机器人系统已经成为应对从工业自动化到灾难应对的重大挑战的关键解决方案，特别是在人类干预的范围受到很大限制的情况下。”有相当数量的事件驱动操作触发机器人执行大量任务。我们的新闻记者从Virginia大学的研究中获得了一句话：“尽管如此，由于许多机器人系统固有的有限计算能力，任务的完成被证明是具有挑战性的。尽管云计算解决方案可以通过将工作负载分配到云中来解决这些限制。”由于机器人所面临的通信瓶颈，如何保证最优性能仍然是一个巨大的挑战，而机器人的能量约束和严格的实时服务要求进一步加剧了这种负载分配问题。本文介绍了一种基于雾露的机器人集成电路系统，该系统在协调机器人之间关键任务分配决策的同时降低了延迟和能耗，将决策任务的执行概念化为多目标优化问题，针对多目标优化的NP难性，提出了一种基于元启发式二进制粒子群算法的创新解决方案。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subjec t of a report. According to news originating from Charlottesville, Virginia, by NewsRx correspondents, research stated, “In our fast -paced, technology -driven world, multi -robot systems have emerged as crucial solutions to tackle contempo rary challenges, from industrial automation to disaster response, especially whe re the scope of human interventions is significantly constrained. In such scenar ios, a notable number of event -driven operations trigger robots to perform a su bstantial amount of tasks.” Our news journalists obtained a quote from the research from the University of V irginia, “Nonetheless, completion of the tasks proves challenging due to the lim ited computational capabilities inherent to many robotic systems. Although cloud computing solutions can be integrated to address these limitations by distribut ing the workload to clouds, ensuring optimized performance remains a formidable challenge due to the communication bottlenecks encountered by the robots. Moreov er, the presence of robots’ energy constraints and stringent real-time service r equirements further exacerbate this workload distribution problem. In response t o the aforementioned challenges, this paper introduces a fog -dew -enabled robot ic system designed to mitigate latency and energy consumption while orchestratin g crucial workload distribution decisions among robots. The execution of decisio n -making tasks is conceptualized as a multiobjective optimization problem. Due to the NP -hardness of the multi -objective optimization, we propose an innovati ve solution based on a meta -heuristic Binary Particle Swarm Optimization algori thm.”
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