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

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Document pages: 9 pages

Abstract: We present a motion planning algorithm with probabilistic guarantees forlimbed robots with stochastic gripping forces. Planners based on deterministicmodels with a worst-case uncertainty can be conservative and inflexible toconsider the stochastic behavior of the contact, especially when a gripper isinstalled. Our proposed planner enables the robot to simultaneously plan itspose and contact force trajectories while considering the risk associated withthe gripping forces. Our planner is formulated as a nonlinear programmingproblem with chance constraints, which allows the robot to generate a varietyof motions based on different risk bounds. To model the gripping forces asrandom variables, we employ Gaussian Process regression. We validate ourproposed motion planning algorithm on an 11.5 kg six-limbed robot for two-wallclimbing. Our results show that our proposed planner generates varioustrajectories (e.g., avoiding low friction terrain under the low risk bound,choosing an unstable but faster gait under the high risk bound) by changing theprobability of risk based on various specifications.

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