Multi-Agent Autonomous Surveillance: A Framework Based on Stochastic Reachability and Hierarchical Task Allocation

Kariotoglou, Nikolaos; Raimondo, Davide M.; Summers, Sean; Lygeros, John

doi:10.1115/1.4028589

Cited by 11 publications

(7 citation statements)

References 32 publications

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“…We also believe that recursive structures appear naturally in multi-agent systems where the decisions of one agent depend on the decision of another; in such cases using different samples between agents can have a significant impact on the required communication bandwidth. In terms of applications, we intend to use the recursive scenario approach discussed here to address surveillance tasks that are posed as reach-avoid problems [36].…”

Section: Discussionmentioning

confidence: 99%

On the computational complexity and generalization properties of multi-stage and stage-wise coupled scenario programs

Kariotoglou

Margellos

Lygeros

2016

Systems & Control Letters

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We discuss the computational complexity and feasibility properties of scenario based techniques for uncertain optimization programs. We consider different solution alternatives ranging from the standard scenario approach to recursive variants, and compare feasibility as a function of the total computation burden. We identify trade-offs between the different methods depending on the problem structure and the desired probability of constraint satisfaction. Our motivation for this work stems from the applicability and complexity reduction when making decisions by means of recursive algorithms. We illustrate our results on an example from the area of approximate dynamic programming. Index TermsScenario approach, randomized optimization, uncertain systems, approximate dynamic programming.

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Section: Discussionmentioning

confidence: 99%

On the computational complexity and generalization properties of multi-stage and stage-wise coupled scenario programs

Kariotoglou

Margellos

Lygeros

2016

Systems & Control Letters

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“…However, these approaches suffer from the curse of dimensionality [15,26]. Researchers have proposed particle filters [8,27] and approximate dynamic programming [28] to improve the computational tractability. For stochastic rigid body obstacles with a discrete disturbance, a convolution-based formulation was proposed to quantify the collision probability [16].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Occupancy Function and Sets Using Forward Stochastic Reachability for Rigid-Body Dynamic Obstacles

Vinod¹,

Oishi²

2018

Preprint

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We present theory and algorithms for the computation of probability-weighted "keep-out" sets to assure probabilistically safe navigation in the presence of multiple rigid body obstacles with stochastic dynamics. Our forward stochastic reachability-based approach characterizes the stochasticity of the future obstacle states in a grid-free and recursion-free manner, using Fourier transforms and computational geometry. We consider discrete-time Markovian switched systems with affine parameter-varying stochastic subsystems (DMSP) as the obstacle dynamics, which includes Markov jump affine systems and discrete-time affine parameter-varying stochastic systems (DPV). We define a probabilistic occupancy function, to describe the probability that a given state is occupied by a rigid body obstacle with stochastic dynamics at a given time; keep-out sets are the super-level sets of this occupancy function. We provide sufficient conditions that ensure convexity and compactness of these keep-out sets for DPV obstacle dynamics. We also propose two computationally efficient algorithms to overapproximate the keep-out sets -a tight polytopic approximation using projections, and an overapproximation using Minkowski sum. For DMSP obstacle dynamics, we compute a union of convex and compact sets that covers the potentially non-convex keep-out set. Numerical simulations show the efficacy of the proposed algorithms for a modified version of the classical unicycle dynamics, modeled as a DMSP.

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“…This difficulty is evidenced by the problem of inverse reinforcement learning [44,3,4], a well-known problem in artificial intelligence where the objective is to learn a reward or cost function being optimized based on observed behavior of an agent/controller, in tasks where it is not entirely obvious what should be optimized. The stochastic reach-avoid framework has been applied to several problems including aircraft conflict detection under stochastic wind [57,23], feedback control of camera networks in the presence of an uncertain evader [33] and optimal feedback policies for building evacuation under a randomly evolving hazards [58].…”

Section: Introductionmentioning

confidence: 99%

The Linear Programming Approach to Reach-Avoid Problems for Markov Decision Processes

Kariotoglou¹,

Kamgarpour²,

Summers³

et al. 2017

jair

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One of the most fundamental problems in Markov decision processes is analysis and control synthesis for safety and reachability specifications. We consider the stochastic reach-avoid problem, in which the objective is to synthesize a control policy to maximize the probability of reaching a target set at a given time, while staying in a safe set at all prior times. We characterize the solution to this problem through an infinite dimensional linear program. We then develop a tractable approximation to the infinite dimensional linear program through finite dimensional approximations of the decision space and constraints. For a large class of Markov decision processes modeled by Gaussian mixtures kernels we show that through a proper selection of the finite dimensional space, one can further reduce the computational complexity of the resulting linear program. We validate the proposed method and analyze its potential with numerical case studies.

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Multi-Agent Autonomous Surveillance: A Framework Based on Stochastic Reachability and Hierarchical Task Allocation

Cited by 11 publications

References 32 publications

On the computational complexity and generalization properties of multi-stage and stage-wise coupled scenario programs

On the computational complexity and generalization properties of multi-stage and stage-wise coupled scenario programs

Probabilistic Occupancy Function and Sets Using Forward Stochastic Reachability for Rigid-Body Dynamic Obstacles

The Linear Programming Approach to Reach-Avoid Problems for Markov Decision Processes

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