790 research outputs found
Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions
A multi-agent partially observable Markov decision process (MPOMDP) is a
modeling paradigm used for high-level planning of heterogeneous autonomous
agents subject to uncertainty and partial observation. Despite their modeling
efficiency, MPOMDPs have not received significant attention in safety-critical
settings. In this paper, we use barrier functions to design policies for
MPOMDPs that ensure safety. Notably, our method does not rely on discretization
of the belief space, or finite memory. To this end, we formulate sufficient and
necessary conditions for the safety of a given set based on discrete-time
barrier functions (DTBFs) and we demonstrate that our formulation also allows
for Boolean compositions of DTBFs for representing more complicated safe sets.
We show that the proposed method can be implemented online by a sequence of
one-step greedy algorithms as a standalone safe controller or as a
safety-filter given a nominal planning policy. We illustrate the efficiency of
the proposed methodology based on DTBFs using a high-fidelity simulation of
heterogeneous robots.Comment: 8 pages and 4 figure
Safety Verification of Fault Tolerant Goal-based Control Programs with Estimation Uncertainty
Fault tolerance and safety verification of control systems that have state variable estimation uncertainty are essential for the success of autonomous robotic systems. A software control architecture called mission data system, developed at the Jet Propulsion Laboratory, uses goal networks as the control program for autonomous systems. Certain types of goal networks can be converted into linear hybrid systems and verified for safety using existing symbolic model checking software. A process for calculating the probability of failure of certain classes of verifiable goal networks due to state estimation uncertainty is presented. A verifiable example task is presented and the failure probability of the control program based on estimation uncertainty is found
Safety Barrier Certificates for Stochastic Control Systems with Wireless Communication Networks
This work is concerned with a formal approach for safety controller synthesis
of stochastic control systems with both process and measurement noises while
considering wireless communication networks between sensors, controllers, and
actuators. The proposed scheme is based on control barrier certificates (CBC),
which allows us to provide safety certifications for wirelessly-connected
stochastic control systems. Despite the available literature on designing
control barrier certificates, there has been unfortunately no consideration of
wireless communication networks to capture potential packet losses and
end-to-end delays, which is absolutely crucial in safety-critical real-world
applications. In our proposed setting, the key objective is to construct a
control barrier certificate together with a safety controller while providing a
lower bound on the satisfaction probability of the safety property over a
finite time horizon. We propose a systematic approach in the form of
sum-of-squares optimization and matrix inequalities for the synthesis of CBC
and its associated controller. We demonstrate the efficacy of our approach on a
permanent magnet synchronous motor. For the application of automotive electric
steering under a wireless communication network, we design a CBC together with
a safety controller to maintain the electrical current of the motor in a safe
set within a finite time horizon while providing a formal probabilistic
guarantee
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