Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Provably Safe Robot Navigation with Obstacle Uncertainty   As drones and autonomous cars become more widespread it is becoming
increasingly important that robots can operate safely under realistic
conditions. The noisy information fed into real systems means that robots must
use estimates of the environment to plan navigation. Efficiently guaranteeing
that the resulting motion plans are safe under these circumstances has proved
difficult. We examine how to guarantee that a trajectory or policy is safe with
only imperfect observations of the environment. We examine the implications of
various mathematical formalisms of safety and arrive at a mathematical notion
of safety of a long-term execution, even when conditioned on observational
information. We present efficient algorithms that can prove that trajectories
or policies are safe with much tighter bounds than in previous work. Notably,
the complexity of the environment does not affect our methods ability to
evaluate if a trajectory or policy is safe. We then use these safety checking
methods to design a safe variant of the RRT planning algorithm.

4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom