以中继卫星(Racking and Data Relay Satellite,TDRS)为研究对象,以有色Petri网(Colored Petri Net,CPN)为数学工具,根据自顶向下的原则和层次化建模思想,提出一种基于CPN的TDRS操作规划模型,该模型分为顶层模型、控制模型、前向链路数...以中继卫星(Racking and Data Relay Satellite,TDRS)为研究对象,以有色Petri网(Colored Petri Net,CPN)为数学工具,根据自顶向下的原则和层次化建模思想,提出一种基于CPN的TDRS操作规划模型,该模型分为顶层模型、控制模型、前向链路数据接收任务与发送任务的操作规划模型和返向链路数据接收任务与发送任务的操作规划模型,有效地描述了TDRS的动态行为特性。最后,通过仿真实验得到了TDRS操作规划方案,验证了所建模型的有效性。与PDDL模型比较分析表明:所建模型可以有效引入TDRS的领域知识,能够有效提高求解效率。所建模型可以为TDRS操作规划方案的制定提供理论参考。展开更多
在地外天体执行遥操作任务时,在复杂约束条件下会出现多分支作业选择困难、事件属性设置复杂等现实难题。提出了一种通用型任务智能规划方法——分层规划对象模型(Hierarchical Plan Object Model,HPOM),巡视器在地外天体作业时,其分解...在地外天体执行遥操作任务时,在复杂约束条件下会出现多分支作业选择困难、事件属性设置复杂等现实难题。提出了一种通用型任务智能规划方法——分层规划对象模型(Hierarchical Plan Object Model,HPOM),巡视器在地外天体作业时,其分解为多选项作业、带约束行为、多分支指令序列、参数化虚拟指令4个层次,将带约束行为表示的计划转化为行为规划问题进行求解,获得求解方法集合。采用“人机协同迭代求解”(Human-In-The-Loop,HITL)的处理流程,生成指令序列以期实现对不同规划粒度方案的一致性验证。该方法已成功应用于“嫦娥四号”(Chang'E-4,CE-4)任务,为任务圆满成功提供了技术支撑。展开更多
Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solv...Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However, many different planners exist, each with different strengths and weaknesses,and there are no general rules for which planner would be best to apply to a given problem. In this study, we empirically compare the performance of state-of-the-art planners that use either the planning domain description language(PDDL) or answer set programming(ASP) as the underlying action language. PDDL is designed for task planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used to solve task planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions,and ASP-based planners are better on tasks with a large number of objects or tasks in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general-purpose planning systems for particular robot task planning domains.展开更多
文摘以中继卫星(Racking and Data Relay Satellite,TDRS)为研究对象,以有色Petri网(Colored Petri Net,CPN)为数学工具,根据自顶向下的原则和层次化建模思想,提出一种基于CPN的TDRS操作规划模型,该模型分为顶层模型、控制模型、前向链路数据接收任务与发送任务的操作规划模型和返向链路数据接收任务与发送任务的操作规划模型,有效地描述了TDRS的动态行为特性。最后,通过仿真实验得到了TDRS操作规划方案,验证了所建模型的有效性。与PDDL模型比较分析表明:所建模型可以有效引入TDRS的领域知识,能够有效提高求解效率。所建模型可以为TDRS操作规划方案的制定提供理论参考。
文摘在地外天体执行遥操作任务时,在复杂约束条件下会出现多分支作业选择困难、事件属性设置复杂等现实难题。提出了一种通用型任务智能规划方法——分层规划对象模型(Hierarchical Plan Object Model,HPOM),巡视器在地外天体作业时,其分解为多选项作业、带约束行为、多分支指令序列、参数化虚拟指令4个层次,将带约束行为表示的计划转化为行为规划问题进行求解,获得求解方法集合。采用“人机协同迭代求解”(Human-In-The-Loop,HITL)的处理流程,生成指令序列以期实现对不同规划粒度方案的一致性验证。该方法已成功应用于“嫦娥四号”(Chang'E-4,CE-4)任务,为任务圆满成功提供了技术支撑。
基金supported in part by NSF (IIS1637736, IIS-1651089, IIS-1724157)ONR (N00014-182243)+2 种基金FLI (RFP2-000)Intel, RaytheonLockheed Martin
文摘Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However, many different planners exist, each with different strengths and weaknesses,and there are no general rules for which planner would be best to apply to a given problem. In this study, we empirically compare the performance of state-of-the-art planners that use either the planning domain description language(PDDL) or answer set programming(ASP) as the underlying action language. PDDL is designed for task planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used to solve task planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions,and ASP-based planners are better on tasks with a large number of objects or tasks in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general-purpose planning systems for particular robot task planning domains.
