The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multi...The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.展开更多
In this paper, we propose a method to construct an online/offiine batch verification signature scheme in a multi-signer setting. The length of the scheme is approximately 480 bits. Based on the Lysyanskaya, Rivest, Sa...In this paper, we propose a method to construct an online/offiine batch verification signature scheme in a multi-signer setting. The length of the scheme is approximately 480 bits. Based on the Lysyanskaya, Rivest, Sahai and Wolf （LRSW） assumption, this scheme is proved secure in a random oracle model, and it requires only three pairing operations for verifying n signatures from a multi-signer setting.展开更多
基金the Shanghai Pujiang Program (No.22PJD030),the National Natural Science Foundation of China (Nos.61603244 and 71904116)the National Natural Science Foundation of China-Shandong Joint Fund (No.U2006228)。
文摘The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.
基金Supported by the National Natural Science Foundation of China(61072080)the Foundation of Education Bureau of Fujian Province (JK2010012)Key Project of Services for Haixia Construction in Universities of Fujian Province
文摘In this paper, we propose a method to construct an online/offiine batch verification signature scheme in a multi-signer setting. The length of the scheme is approximately 480 bits. Based on the Lysyanskaya, Rivest, Sahai and Wolf （LRSW） assumption, this scheme is proved secure in a random oracle model, and it requires only three pairing operations for verifying n signatures from a multi-signer setting.
