Abstract

To minimize the operation time, mobile manipulators need to pick-up parts while the mobile base and the gripper are moving. The gripper speed needs to be selected to ensure that the pick-up operation does not fail due to uncertainties in part pose estimation. This, in turn, affects the mobile base trajectory. This paper presents an active learning based approach to construct a meta-model to estimate the probability of successful part pick-up for a given level of uncertainty in the part pose estimate. Using this model, we present an optimization-based framework to generate time-optimal trajectories that satisfy the given level of success probability threshold for picking-up the part.

S. Thakar, P. Rajendran, V. Annem, A. Kabir, and S. Gupta, “Accounting for part pose estimation uncertainties during trajectory generation for part pick-up using mobile manipulators,” in IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 2019

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