Скачать или смотреть C-OPT: Coverage-Aware Trajectory Optimization Under Uncertainty

We introduce a new problem of continuous,
coverage-aware trajectory optimization under localization and
sensing uncertainty. In this problem, the goal is to plan a
path from a start state to a goal state that maximizes the
coverage of a user-specified region while minimizing the control
costs of the robot and the probability of collision with the
environment. We present a principled method for quantifying the
coverage sensing uncertainty of the robot. We use this sensing
uncertainty along with the uncertainty in robot localization
to develop C-OPT, a coverage-optimization algorithm which
optimizes trajectories over belief-space to find locally optimal
coverage paths. We highlight the applicability of our approach
in multiple simulated scenarios inspired by surveillance, UAV
crop analysis, and search-and-rescue tasks. We also present a
case study on a physical, differential-drive robot. We also provide
quantitative and qualitative analysis of the paths generated by
our approach.

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Applied Motion Lab, UMN
http://motion.cs.umn.edu