未知动力学与混合观测下的风险感知运动规划与控制

中文摘要 AI 辅助

我们考虑在未知动力学和混合状态观测下的机器人运动规划与控制问题，其中状态测量仅在状态空间的某些部分可用。现有工作通过多面体状态空间划分上的局部仿射近似模型，在分层框架中结合系统辨识、预测可达性、图搜索和控制器综合，但该方法需要状态观测来进行辨识和反馈控制。基于该框架，我们通过选择标称动力学并在观测丢失前预计算开环控制序列来处理盲区。由于真实动力学可能与所选标称模型不同，机器人可能通过非预期的面离开盲多面体。我们量化了这一转移风险，并将可能的结果纳入随机转移系统。高层规划问题被建模为随机最短路径问题，其策略指导控制器综合。案例研究表明，该方法在平衡路线效率与穿越盲区相关风险的同时，引导机器人从初始状态到达目标。

英文摘要

We consider robotic motion planning and control under unknown dynamics with hybrid state observations, where state measurements are available only in parts of the state space. Existing work combines system identification, predicted reachability, graph search and controller synthesis in a hierarchical framework using local affine approximated models over polytopic state space partitioning, but requires state observations for identification and feedback control. Based on this framework, we address blind regions by selecting nominal dynamics and precomputing open-loop control sequences before observation is lost. Since the true dynamics may differ from the selected nominal model, the robot may exit a blind polytope through an unintended facet. We quantify this transition risk and incorporate the possible outcomes into a stochastic transition system. The high-level planning problem is formulated as a stochastic shortest path problem, whose policy guides controller synthesis. A case study demonstrates that the method guides the robot from an initial state to a target while balancing route efficiency and the risks associated with traversing blind regions.

发表机构

• University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）

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