This paper aims to solve large-scale and complex isogeometric topology optimization problems that consumesignificant computational resources. A novel isogeometric topology optimization method with a hybrid parallelstr...This paper aims to solve large-scale and complex isogeometric topology optimization problems that consumesignificant computational resources. A novel isogeometric topology optimization method with a hybrid parallelstrategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equationsolving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency ofCPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload betweenCPU and GPU. To illustrate the advantages of the proposedmethod, three benchmark examples are tested to verifythe hybrid parallel strategy in this paper. The results show that the efficiency of the hybrid method is faster thanserial CPU and parallel GPU, while the speedups can be up to two orders of magnitude.展开更多
Task allocation is a fundamental problem in multi-robot systems where heterogeneous robots cooperate to perform a complex mission.A general requirement in a task allocation algorithm is to find an optimal set of robot...Task allocation is a fundamental problem in multi-robot systems where heterogeneous robots cooperate to perform a complex mission.A general requirement in a task allocation algorithm is to find an optimal set of robots to execute a certain task.This paper presents the work that harnesses an area decomposition algorithm,and a space-based middleware to facilitate task allocation process in unstructured and dynamic environments.To reduce spatial interference between robots,area decomposition algorithm divides a working area into cells which are then dynamically assigned to robots.In addition,coordination and collaboration among distributed robots are realized through a space-based middleware.For this purpose,the space-based middleware is extended with a semantic model of robot capabilities to improve task selection in terms of flexibility,scalability,and reduced communication overhead during task allocation.In this way a framework which exploits the synergy of area decomposition and semantically enriched space-based approach is created.We conducted performance tests in a specific precision agriculture use case focusing on the utilization of a robotic fleet for weed control introduced in the European Project RHEA–Robot Fleets for Highly Effective Agriculture and Forestry Management.展开更多
基金the National Key R&D Program of China(2020YFB1708300)the National Natural Science Foundation of China(52005192)the Project of Ministry of Industry and Information Technology(TC210804R-3).
文摘This paper aims to solve large-scale and complex isogeometric topology optimization problems that consumesignificant computational resources. A novel isogeometric topology optimization method with a hybrid parallelstrategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equationsolving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency ofCPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload betweenCPU and GPU. To illustrate the advantages of the proposedmethod, three benchmark examples are tested to verifythe hybrid parallel strategy in this paper. The results show that the efficiency of the hybrid method is faster thanserial CPU and parallel GPU, while the speedups can be up to two orders of magnitude.
基金This paper is partially supported within the European Research Project RHEAThe Telecommunications Research Center Vienna(FTW)is supported by the Austrian government and the City of Vienna within the competence center program COMET.
文摘Task allocation is a fundamental problem in multi-robot systems where heterogeneous robots cooperate to perform a complex mission.A general requirement in a task allocation algorithm is to find an optimal set of robots to execute a certain task.This paper presents the work that harnesses an area decomposition algorithm,and a space-based middleware to facilitate task allocation process in unstructured and dynamic environments.To reduce spatial interference between robots,area decomposition algorithm divides a working area into cells which are then dynamically assigned to robots.In addition,coordination and collaboration among distributed robots are realized through a space-based middleware.For this purpose,the space-based middleware is extended with a semantic model of robot capabilities to improve task selection in terms of flexibility,scalability,and reduced communication overhead during task allocation.In this way a framework which exploits the synergy of area decomposition and semantically enriched space-based approach is created.We conducted performance tests in a specific precision agriculture use case focusing on the utilization of a robotic fleet for weed control introduced in the European Project RHEA–Robot Fleets for Highly Effective Agriculture and Forestry Management.
