Safe and efficient robot motion planning is critical to ensure desired human-robot interactions. However, there are very few methods that can comprehensively address uncertainties in human behaviors, robot model mismatch, robot computation limits, and measurement and actuation noises in an integrated planning theme. This proposal targets to develop a planning method that addresses all these challenges using hierarchical motion planning. A hierarchical planning theme has multiple planners running in parallel that use different dynamic models of the system and have different planning horizons, sampling frequencies, and replanning frequencies. It can generate trajectory plans from coarse to fine, and efficiently separate time-sensitive computation (e.g., real-time safety response) from time-insensitive computation (e.g., figuring out the reference trajectory towards the goal). A set of design principles will be developed to ensure sound safety guarantees as well as the performance of the hierarchical planner (e.g., stable and dead-lock free).

Sponsor: Amazon Research Award

Period of Performance: 2020 ~ 2021

Point of Contact: Weiye Zhao