首页|Recent Research from University of Virginia Highlight Findings in Robotics (Fold : Fog-dew Infrastructure-aided Optimal Workload Distribution for Cloud Robotic O perations)
Recent Research from University of Virginia Highlight Findings in Robotics (Fold : Fog-dew Infrastructure-aided Optimal Workload Distribution for Cloud Robotic O perations)
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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.”
CharlottesvilleVirginiaUnited StatesNorth and Central AmericaEmerging TechnologiesMachine LearningNano-robotRoboticsRobotsUniversity of Virginia
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