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Multi agents' multi targets mission under uncertainty using probability navigation function

机译:使用概率导航功能的不确定性下的多主体多目标任务

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In this paper we consider the problem of cooperative control of a swarm of autonomous heterogeneous mobile agents that are required to intercept a group of moving targets while avoiding contacts with dynamic obstacles. Traditionally these type of problems are solved by decomposing the solution into several sub problems: targets assignments, coordinated interception control, motion planning and motion control. In this paper we present a simultaneous solution to these problems based on the Probabilistic Navigation Function (PNF). The proposed solution considers uncertainties in the targets and obstacles locations. such that the locations and geometries of the targets and obstacles are given by Gaussian probability distributions. These probabilities are convoluted with the agents', obstacles' and targets' geometries to provide a Global Probability Navigation Function - φ. The PNF provides an analytic solution, and guarantees a simultaneous interception of all targets while limiting the risk of the agents to a given value. The complexity of the solution is linear with the number of targets and agents, and therefore is not limited to small problems. Although the solution provided by the PNF is not optimal, it provides simple and efficient solution, making it suitable for a large range of real time applications.
机译:在本文中,我们考虑了一群自治的异构移动代理的协同控制问题,这些代理需要拦截一组移动目标同时避免与动态障碍物接触。传统上,这些类型的问题是通过将解决方案分解为几个子问题来解决的:目标分配,协调拦截控制,运动计划和运动控制。在本文中,我们基于概率导航功能(PNF)提出了针对这些问题的同时解决方案。提出的解决方案考虑了目标和障碍物位置的不确定性。这样目标和障碍物的位置和几何形状就由高斯概率分布给出。将这些概率与代理,障碍物和目标的几何形状进行卷积,以提供全局概率导航功能-。 PNF提供了一种分析解决方案,可确保同时拦截所有目标,同时将试剂的风险限制在给定值。解决方案的复杂度与目标和代理的数量成线性关系,因此不限于小问题。尽管PNF提供的解决方案不是最佳解决方案,但它提供了简单有效的解决方案,使其适用于各种实时应用。

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