Robust Motion Planning with Accuracy Optimization based on Learned Sensitivity Metrics
Abstract
This work addresses the problem of generating
robust and accurate trajectories taking into account uncertainties
in the robot dynamic model. Based on the notion of closedloop
sensitivity, which quantifies deviations in the closed-loop
trajectories of any robot/controller pair against uncertainties in
the robot model parameters, uncertainty tubes can be derived
for bounded parameter variations. In our prior work, such
tubes were integrated within a motion planner named SAMP to
produce robust global plans, emphasizing the generation of trajectories
with low sensitivity to model uncertainty. However, the
high computational cost of the uncertainty tubes is a bottleneck
for this method. Here, we solve this problem by proposing a
novel framework that first incorporates a Gated Recurrent Unit
(GRU) neural network to provide fast and accurate estimation
of uncertainty tubes and then minimizes these tubes at given
points along the trajectory. We experimentally validate our
framework on a 3D quadrotor in two challenging scenarios:
a navigation through a narrow window, and an in-flight “ring
catching” task that requires high accuracy.
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