2018/12/04 by Masayuki Ohzeki, Akira Miki, Ohzeki, Masayuki +5 · 5 citations
Computer Science · Engineering · Neuroscience · #Disordered Systems and Neural Networks (cond-mat.dis-nn) #Extremum Seeking Control Systems #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Multiagent Systems (cs.MA) #Quantum Physics (quant-ph) #Robotic Path Planning Algorithms #Robotics (cs.RO) #Sleep and Wakefulness Research #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1812.01532
openalex publication_date 2018/12/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We formulate an optimization problem to control a large number of automated\nguided vehicles in a plant without collision. The formulation consists of\nbinary variables. A quadratic cost function over these variables enables us to\nutilize certain solvers on digital computers and recently developed\npurpose-specific hardware such as D-Wave 2000Q and the Fujitsu digital\nannealer. In the present study, we consider an actual plant in Japan, in which\nvehicles run, and assess efficiency of our formulation for optimizing the\nvehicles via several solvers. We confirm that our formulation can be a powerful\napproach for performing smooth control while avoiding collisions between\nvehicles, as compared to a conventional method. In addition, comparative\nexperiments performed using several solvers reveal that D-Wave 2000Q can be\nuseful as a rapid solver for generating a plan for controlling the vehicles in\na short time although it deals only with a small number of vehicles, while a\ndigital computer can rapidly solve the corresponding optimization problem even\nwith a large number of binary variables.\n