Robot motion planning manages unknown dynamics with partial observations
Risk-Aware Motion Planning and Control under Unknown Dynamics with Hybrid Observations
Robotics
Summary
Robots sometimes cannot see their full surroundings or know exactly how they will move, which makes planning a safe and efficient path difficult. The authors study how to plan and control a robot’s movements when it only gets limited information about its state and doesn’t know precisely how its controls affect its motion. They create a method that picks a likely model of the robot’s behavior before entering unseen areas and plans for the risks of unexpected movements. This helps guide the robot safely to its target while accounting for uncertainty in both what it sees and how it moves.
What this means in practice
- •For robotics control engineers: Plan robot paths that balance efficiency and safety when sensor data is missing and dynamics are uncertain.
- •For autonomous vehicle developers: Improve route planning under sensor blind spots and unknown vehicle dynamics by modeling probable risks of unintended movements.
Authors
Zhiquan Zhang, Melkior Ornik
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.