Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor systems.However,it still faces challenges due to the system comp...Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor systems.However,it still faces challenges due to the system complexity,the execution order constraints,and the dynamic environment uncertainty.To address it,a coordinated dynamic mission planning scheme is proposed utilizing the method of the weighted AND/OR tree and the AOE-Network.In the scheme,the mission is decomposed into a time-constraint weighted AND/OR tree,which is converted into an AOE-Network for mission planning.Then,a dynamic planning algorithm is designed which uses task subcontracting and dynamic re-decomposition to coordinate conflicts.The scheme can reduce the task complexity and its execution time by implementing real-time dynamic re-planning.The simulation proves the effectiveness of this approach.展开更多
The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat agents.This study aims to address the problem of dynamic construction of mission grou...The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat agents.This study aims to address the problem of dynamic construction of mission groups under new requirements.Agents are heterogeneous,and a group formation method must dynamically form new groups in circumstances where missions are constantly being explored.In our method,a group formation strategy that combines heuristic rules and response threshold models is proposed to dynamically adjust the members of the mission group and adapt to the needs of new missions.The degree of matching between the mission requirements and the group’s capabilities,and the communication cost of group formation are used as indicators to evaluate the quality of the group.The response threshold method and the ant colony algorithm are selected as the comparison algorithms in the simulations.The results show that the grouping scheme obtained by the proposed method is superior to those of the comparison methods.展开更多
基金Projects(61071096,61003233,61073103)supported by the National Natural Science Foundation of ChinaProjects(20100162110012,20110162110042)supported by the Research Fund for the Doctoral Program of Higher Education of China
文摘Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor systems.However,it still faces challenges due to the system complexity,the execution order constraints,and the dynamic environment uncertainty.To address it,a coordinated dynamic mission planning scheme is proposed utilizing the method of the weighted AND/OR tree and the AOE-Network.In the scheme,the mission is decomposed into a time-constraint weighted AND/OR tree,which is converted into an AOE-Network for mission planning.Then,a dynamic planning algorithm is designed which uses task subcontracting and dynamic re-decomposition to coordinate conflicts.The scheme can reduce the task complexity and its execution time by implementing real-time dynamic re-planning.The simulation proves the effectiveness of this approach.
基金Project supported by the National Natural Science Foundation of China(No.61773066)the Foundation of China Academy of Railway Sciences Corporation Limited(No.2019YJ071)。
文摘The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat agents.This study aims to address the problem of dynamic construction of mission groups under new requirements.Agents are heterogeneous,and a group formation method must dynamically form new groups in circumstances where missions are constantly being explored.In our method,a group formation strategy that combines heuristic rules and response threshold models is proposed to dynamically adjust the members of the mission group and adapt to the needs of new missions.The degree of matching between the mission requirements and the group’s capabilities,and the communication cost of group formation are used as indicators to evaluate the quality of the group.The response threshold method and the ant colony algorithm are selected as the comparison algorithms in the simulations.The results show that the grouping scheme obtained by the proposed method is superior to those of the comparison methods.
