A Time-space Network Model For Collision-free Routing Of Planar Motions In A Multirobot Station

Since its invention,industrial robots have developed into intelligent automatic equipment integrated with multidisciplinary technology,and with the large-scale use of automated production lines in manufacturing systems,industrial robots have become standardized equipment on production lines,playing an increasingly important role in the production of manufacturing systems.With the increase of the requirement of productivity development,the application research of multi-robot co-working units highlights its importance.However,some problems faced by multi-robot systems,such as the routing of robots,collision avoidance between multiple robots,and the energysaving problems of robots often need to be planned manually in advance.And this kind of artificial planning way often exists planning time is long,the path planning is unreasonable,the energy consumption of robots is high and so on,so the study of multirobot system collision-free path planning and energy-saving touchless path planning for shortening the beat,improve productivity,reduce the energy consumption of the multi-robot system and give full play to the advantages of manufacturing resources has important engineering significance.This thesis investigates a new collision-free routing problem of a multirobot system.The objective is to minimize the cycle time of operation tasks for each robot while avoiding collisions.The focus is set on the operation of the end-effector and its connected joint,and the operation is projected onto a circular area on the plane.We propose to employ a time-space network(TSN)model that maps the robot location constraints into the route planning framework,leading to a mixed integer programming(MIP)problem.A dedicated genetic algorithm is proposed for solving this MIP problem and a new encoding scheme is designed to fit the TSN formulation.Simulation experiments indicate that the proposed model can obtain the collision-free route of the considered multirobot system.Simulation results also show that the proposed genetic algorithm can provide fast and high-quality solutions,compared to two state-of-the-art commercial solvers and a practical approach.Then,this thesis investigates a novel routing problem of a multi-robot station in a manufacturing cell.In the existing literature,the objective is to minimize the cycle time or energy consumption separately.The routing problem considered in this paper aims to reduce the cycle time and energy consumption simultaneously for each robot while avoiding collisions between these robots.For this routing problem,we propose a new flexible time-space network model that allows us to reduce energy consumption while minimizing the cycle time.The corresponding optimization problem is mixed-integer nonlinear programming(MINLP).For addressing its computational complexity,this paper further designs a meta-heuristic algorithm tailored to the proposed new model,and proposes a bi-objective ?-constraint algorithm to analyze the Pareto frontiers.We conduct industrially relevant simulation experiments of case studies to show its effectiveness,in comparison to a state-of-the-art method and two commonly used solvers.The results show that the proposed methodology can reduce energy consumption by at least 30% without compromising the cycle time.Meanwhile,the proposed algorithm can provide efficient solutions within reasonable computational time.At last,the research work of this thesis is summarized briefly and the prospect and planning research for the next step of the research are put forward.