Problem
Research questions and friction points this paper is trying to address.
unknown dynamics
hybrid observations
motion planning
control
blind regions
Risk-Aware Motion Planning and Control under Unknown Dynamics with Hybrid Observations
🤖 AI Summary
研究解决了未知动态下机器人运动规划与控制问题，通过预计算开环控制序列和量化转换风险的方法处理观测盲区。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
risk-aware motion planning
unknown dynamics
hybrid observations
stochastic transition system
precomputed open-loop control sequences
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