Stochastic Model-Based Control of Multi-Robot Systems

机译：基于随机模型的多机器人系统控制

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In this report we consider control of single- and multi-robot systems as an optimal control problem. Solution of this problem may be of enormous complexity because of a large-number of robots, a large number of redundant states, and environment uncertainties. Motivated by estimation methods based on statistical sampling employed for solving complex estimation problems, we explore the possibility of using stochastic process samples for computing the optimal control. This approach can ultimately provide small-size, low-cost and efficient computational hardware for solving complex multi-robot control problems and in which computations are driven by laws of statistical physics.

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Milutinovic, D.; Garg, D. P.;
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年度 2009
页码 1-36
总页数 36
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Robots ; Statistical samples ; Stochastic processes ; Optimization ; Computations ; Models ; Statistics ; Efficiency ; Problem solving ; Estimates ; Variables ; Physics ; Sampling ; Hybrid systems ; Equations ; Hamiltonian functions ; Methodology ; Multiple operation ; Test and evaluation ; Control;

机译：机器人;统计样本;随机过程;优化;计算;模型;统计;效率;问题解决;估计;变量;物理;抽样;混合系统;方程;哈密顿函数;方法;多操作;测试和评估;控制;

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3. A NARMAX model-based state-space self-tuning control for nonlinear stochastic hybrid systems [J] . Jason Sheng-Hong Tsai, Chu-Tong Wang, Chi-Chieh Kuang, Applied Mathematical Modelling . 2010,第10期

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Stochastic Model-Based Control of Multi-Robot Systems

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