Game-theoretic learning algorithm for a spatial coverage problem

Savla, Ketan

doi:10.1109/allerton.2009.5394888

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…The use of probability distributions also provides an ability to react to uncertainties in environment and "learn" appropriate control policies in dynamic environments. This particular problem has become an interesting avenue of research, especially in the game theoretic learning community [159][160][161][162]. We have also applied PC to a dynamic replanning problem for a persistent surveillance mission-a complete discussion of this application can be found in [136].…”

Section: Collective Intelligence and Probability Collectivesmentioning

confidence: 99%

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The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review

Nigam

2014

Machines

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Control of autonomous vehicles for applications such as surveillance, search, and exploration has been a topic of great interest over the past two decades. In particular, there has been a rising interest in control of multiple vehicles for reasons such as increase in system reliability, robustness, and efficiency, with a possible reduction in cost. The exploration problem is NP hard even for a single vehicle/agent, and the use of multiple vehicles brings forth a whole new suite of problems associated with communication and cooperation between vehicles. The persistent surveillance problem differs from exploration since it involves continuous/repeated coverage of the target space, minimizing time between re-visits. The use of aerial vehicles demands consideration of vehicle dynamic and endurance constraints as well. Another aspect of the problem that has been investigated to a lesser extent is the design of the vehicles for particular missions. The intent of this paper is to thoroughly review the persistent surveillance problem, with particular focus on multiple Unmanned Air Vehicles (UAVs), and present some of our own work in this area. We investigate the different aspects of the problem and slightly digress into techniques that have been applied to exploration and coverage, but a comprehensive survey of all the work in multiple vehicle control for search, exploration, and coverage is beyond the scope of this paper.

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Section: Collective Intelligence and Probability Collectivesmentioning

confidence: 99%

“…So there is a need to improve those methods. The application of game theoretic learning for dynamic replanning [136,159,160] in surveillance and other applications is a lucrative area for further research, with several advantages and complexities associated with it.…”

Section: Approachesmentioning

confidence: 99%

The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review

Nigam

2014

Machines

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“…However, the policy is unstable for higher arrival rates. Different algorithms for the same problem have been derived with game theoretic formulations, e.g., in Savla and Frazzoli (2010). An important result covering many load cases is presented in Frazzoli and Bullo (2004).…”

Section: Introductionmentioning

confidence: 99%

The +1 Method: Model-Free Adaptive Repositioning Policies for Robotic Multi-Agent Systems

Ruch

Gachter

Hakenberg

2020

IEEE Trans. Netw. Sci. Eng.

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Robotic multi-agent systems can efficiently handle spatially distributed tasks in dynamic environments. Problem instances of particular interest and generality are the dynamic vehicle routing problem and the dynamic traveling repairman problem. Operational policies for robotic fleets solving these two problems take decisions in an online setting with continuously arriving dynamic demands to optimize system time and efficiency. They can be classified along several lines. First, some require a model of the demand, e.g., based on historical information, while others work model-free. Second, they are designed for different operating conditions from light to heavy system load. Third, they work in a time-invariant or time-varying setting. We present a novel class of model-free operational policies for time-varying demands, with performance independent of the load factor and applicable to any number of dimensions, a combination of properties not achieved by any other operational policy in the literature. The underlying principle of the introduced policies is to send available robots to recent realizations of the stochastic process that generates service requests. In simple terms, the strategies rely on sending more than one robot for every service request arriving to the system. This leads to an advantage in scenarios where demand is non-uniformly distributed and correlated in space an time. We provide theoretical stability and performance guarantees for both the time-invariant and the time-varying cases as well as for correlated demand. We verify our theoretical results numerically. Finally, we apply our operational policy to the problem of mobility-on-demand fleet operation and demonstrate that it outperforms model-based and complex algorithms across all load ranges despite of its simplicity.

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