An assembly robot needs to be capable of executing an assembly task robustly under various uncertainties.To attain this goal,we use a task sequence tree model originally proposed for manual assembly.This model regards...An assembly robot needs to be capable of executing an assembly task robustly under various uncertainties.To attain this goal,we use a task sequence tree model originally proposed for manual assembly.This model regards an assembly task under uncertainties as a transformation of the contact state concept.The concept may contain several contact states with probabilities but these are transformed through a series of task elements into the contact state concept having only the goal state at the end.The transformed contact state concept can be classified according to the terminal condition of each task element.Thus,the whole assembly task can be designed as a tree-shaped contingent strategy called a task sequence tree.This paper proposes a systematic approach for reconfiguring a task sequence tree model for application to a robotic assembly task.In addition,by taking a 2D peg-in-hole insertion task to be performed by a robot equipped with a force sensor as an example,we confirm that the proposed approach can provide a robust motion strategy for the task and that the robot can actually execute the task robustly under bounded uncertainty according to the strategy.展开更多
Workflow scheduling is a key issue and remains a challenging problem in cloud computing.Faced with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriat...Workflow scheduling is a key issue and remains a challenging problem in cloud computing.Faced with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each task.Multiple task scheduling sequences exist in a workflow application.Different task scheduling sequences have a significant impact on the scheduling performance.It is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling sequence.Besides,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a workflow.This paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of tasks.An idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling solution.To handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling sequences.The proposed approach is compared with state-of-the-art cloud workflow scheduling algorithms.Experiments show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.展开更多
Existing approaches to automatic assembly planning often lead to combinatorial explo- sion. When the parts composing the assembly increase in number, computer-aided planning be- comes much slower than manual planning....Existing approaches to automatic assembly planning often lead to combinatorial explo- sion. When the parts composing the assembly increase in number, computer-aided planning be- comes much slower than manual planning. Efforts to reduce the computing time by taking into ac- count various constraints and criteria to guide the search for the optimal plan requires too much input information, so as to offset the convenience of automatic assembly planning. In addition, as the planner becomes more complicated, such efforts often fail to reach the objective. This paper presents a new concep── unit , asserting that the intemal structure of an assembly is hierachical. Every disassembly operation only handles several units, no matter how many parts are involved. Furthermore, the scenario of disassembly is brought to light. It relates to only two key data──the liaison type and the assembly direction. The computational cast of this approach is roughly propor. tional to the number of parts. A planner, implementing these principlcs can generate the optimal as- sembly plans dramatically faster than the known approaches.展开更多
基金Project (No.19GS0208) supported by the Grant-in-Aid for Creative Scientific Research 2007–2011 funded by the Ministry of Education,Culture,Sports,Science and Technology,Japan
文摘An assembly robot needs to be capable of executing an assembly task robustly under various uncertainties.To attain this goal,we use a task sequence tree model originally proposed for manual assembly.This model regards an assembly task under uncertainties as a transformation of the contact state concept.The concept may contain several contact states with probabilities but these are transformed through a series of task elements into the contact state concept having only the goal state at the end.The transformed contact state concept can be classified according to the terminal condition of each task element.Thus,the whole assembly task can be designed as a tree-shaped contingent strategy called a task sequence tree.This paper proposes a systematic approach for reconfiguring a task sequence tree model for application to a robotic assembly task.In addition,by taking a 2D peg-in-hole insertion task to be performed by a robot equipped with a force sensor as an example,we confirm that the proposed approach can provide a robust motion strategy for the task and that the robot can actually execute the task robustly under bounded uncertainty according to the strategy.
基金is with the School of Computing Science,Beijing University of Posts and Telecommunications,Beijing 100876,and also with the Key Laboratory of Trustworthy Distributed Computing and Service(BUPT),Ministry of Education,Beijing 100876,China(e-mail:zuoxq@bupt.edu.cn).supported in part by the National Natural Science Foundation of China(61874204,61663028,61703199)the Science and Technology Plan Project of Jiangxi Provincial Education Department(GJJ190959)。
文摘Workflow scheduling is a key issue and remains a challenging problem in cloud computing.Faced with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each task.Multiple task scheduling sequences exist in a workflow application.Different task scheduling sequences have a significant impact on the scheduling performance.It is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling sequence.Besides,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a workflow.This paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of tasks.An idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling solution.To handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling sequences.The proposed approach is compared with state-of-the-art cloud workflow scheduling algorithms.Experiments show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.
文摘Existing approaches to automatic assembly planning often lead to combinatorial explo- sion. When the parts composing the assembly increase in number, computer-aided planning be- comes much slower than manual planning. Efforts to reduce the computing time by taking into ac- count various constraints and criteria to guide the search for the optimal plan requires too much input information, so as to offset the convenience of automatic assembly planning. In addition, as the planner becomes more complicated, such efforts often fail to reach the objective. This paper presents a new concep── unit , asserting that the intemal structure of an assembly is hierachical. Every disassembly operation only handles several units, no matter how many parts are involved. Furthermore, the scenario of disassembly is brought to light. It relates to only two key data──the liaison type and the assembly direction. The computational cast of this approach is roughly propor. tional to the number of parts. A planner, implementing these principlcs can generate the optimal as- sembly plans dramatically faster than the known approaches.
