Risk-Aware Motion Planning for a Limbed Robot with Stochastic Gripping Forces Using Nonlinear Programming

TL;DR

This paper introduces a probabilistic motion planning algorithm for limbed robots with stochastic gripping forces, enabling risk-aware trajectory generation through nonlinear programming and Gaussian Process modeling.

Contribution

It presents a novel nonlinear programming approach with chance constraints for risk-aware motion planning considering stochastic gripping forces.

Findings

Planner generates diverse trajectories based on risk bounds.

Successfully validated on a six-limbed robot for wall climbing.

Demonstrates flexibility in motion planning under different risk levels.

Abstract

We present a motion planning algorithm with probabilistic guarantees for limbed robots with stochastic gripping forces. Planners based on deterministic models with a worst-case uncertainty can be conservative and inflexible to consider the stochastic behavior of the contact, especially when a gripper is installed. Our proposed planner enables the robot to simultaneously plan its pose and contact force trajectories while considering the risk associated with the gripping forces. Our planner is formulated as a nonlinear programming problem with chance constraints, which allows the robot to generate a variety of motions based on different risk bounds. To model the gripping forces as random variables, we employ Gaussian Process regression. We validate our proposed motion planning algorithm on an 11.5 kg six-limbed robot for two-wall climbing. Our results show that our proposed planner generates various trajectories (e.g., avoiding low friction terrain under the low risk bound, choosing an unstable but faster gait under the high risk bound) by changing the probability of risk based on various specifications.