12,122 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
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
Age Optimal Information Gathering and Dissemination on Graphs
We consider the problem of timely exchange of updates between a central
station and a set of ground terminals , via a mobile agent that traverses
across the ground terminals along a mobility graph . We design the
trajectory of the mobile agent to minimize peak and average age of information
(AoI), two newly proposed metrics for measuring timeliness of information. We
consider randomized trajectories, in which the mobile agent travels from
terminal to terminal with probability . For the information
gathering problem, we show that a randomized trajectory is peak age optimal and
factor- average age optimal, where is the mixing
time of the randomized trajectory on the mobility graph . We also show that
the average age minimization problem is NP-hard. For the information
dissemination problem, we prove that the same randomized trajectory is
factor- peak and average age optimal. Moreover, we propose an
age-based trajectory, which utilizes information about current age at
terminals, and show that it is factor- average age optimal in a symmetric
setting
Applications of stochastic modeling in air traffic management:Methods, challenges and opportunities for solving air traffic problems under uncertainty
In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management
A Bioeconomic Analysis of the Duration of Conservation Contracts
Conservation and restoration of native vegetation is often a gradual process which may require many years to transform an ecosystem from one vegetative state to a target ecosystem. This process is stochastic, with some changes potentially irreversible. In contrast, contracts with landholders to undertake conservation measures on their property are typically for less than ten years and often make no contingencies for re-contracting at the end of the contract period. The risk to land holders and conservation agencies of contracts not being renewed and the consequent potential loss of previous investment means including covenants in conservation contracts may be attractive to both parties. A model is developed to empirically examine the optimal dynamic conservation contract and the possible role of covenants in the costs and benefits of contracts.POMDP, biodiversity, contracts, monitoring, Resource /Energy Economics and Policy,
- …