Abstract
Motivated by the success of stochastic process sampling methods in solving complex estimation problems, we explore the possibility to utilize stochastic processes for computing optimal control for a large-size robot population. We assume that the individual robot state is composed of discrete and continuous components, while the population is controlled in a probability space. The optimal control solution is based on an infinite dimensional Pontryagin-like minimum principle, which involves an evaluation of systems of partial differential equations. The paper shows that these equations can be evaluated with computations involving stochastic process samples. This is an important result because generating stochastic process multi-dimensional trajectories is much easier than solving corresponding multi-dimensional partial differential equations. The proposed evaluations are illustrated and verified by an example of the centralized optimal control for a large-size robot population.
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Milutinović, D. (2013). Utilizing Stochastic Processes for Computing Distributions of Large-Size Robot Population Optimal Centralized Control. In: Martinoli, A., et al. Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32723-0_26
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DOI: https://doi.org/10.1007/978-3-642-32723-0_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32722-3
Online ISBN: 978-3-642-32723-0
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