2,092 research outputs found
Verification of Uncertain POMDPs Using Barrier Certificates
We consider a class of partially observable Markov decision processes
(POMDPs) with uncertain transition and/or observation probabilities. The
uncertainty takes the form of probability intervals. Such uncertain POMDPs can
be used, for example, to model autonomous agents with sensors with limited
accuracy, or agents undergoing a sudden component failure, or structural damage
[1]. Given an uncertain POMDP representation of the autonomous agent, our goal
is to propose a method for checking whether the system will satisfy an optimal
performance, while not violating a safety requirement (e.g. fuel level,
velocity, and etc.). To this end, we cast the POMDP problem into a switched
system scenario. We then take advantage of this switched system
characterization and propose a method based on barrier certificates for
optimality and/or safety verification. We then show that the verification task
can be carried out computationally by sum-of-squares programming. We illustrate
the efficacy of our method by applying it to a Mars rover exploration example.Comment: 8 pages, 4 figure
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.Comment: RSS 201
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