Risk-constrained Motion Planning for Robot Locomotion: Formulation and Running Robot Demonstration
Jacob Hackett, Wei Gao, Monica Daley, Jonathan Clark, Christian Hubicki
Abstract
Robots encounter many risks that threaten the success of practical locomotion tasks. Legs break, electrical components overheat, and feet can unexpectedly slip. When all risks cannot be completely avoided, how does a robot decide its best action? We present a method for planning robot motions by reasoning about risk-of-failure probabilities instead of applying cost-penalty functions or inflexible path constraints. This work develops a risk-constrained formulation that can be straightforwardly included in existing motion planning optimizations. The risk constraints scale tractably with many risk sources, and in some cases, only add linear constraints to the optimization problem and are therefore compatible with model-predictive control techniques. We present a toy "Puck World" proof-of-concept example and a practical implementation on a planar monopod robot that runs at 3.2 m/s when permitted to take high-risk maneuvers. We believe this risk approach can be used to optimize robot behaviors under numerous conflicting task pressures and model risk-conscious behaviors in animals.
BibTeX
@inproceedings{iros2020_riskconstrainedm,
title = {Risk-constrained Motion Planning for Robot Locomotion: Formulation and Running Robot Demonstration},
author = {Jacob Hackett and Wei Gao and Monica Daley and Jonathan Clark and Christian Hubicki},
booktitle = {IROS 2020},
year = {2020}
}