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

Shlomi Hacohen, S. Shoval, N. Shvalb
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引用次数: 11

Abstract

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.
基于概率导航函数的不确定条件下多智能体多目标任务
本文研究了一群自主异构移动智能体的协同控制问题,这些智能体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,这类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、运动规划和运动控制。本文提出了一种基于概率导航函数(PNF)的并行求解方法。该方案考虑了目标和障碍物位置的不确定性。使得目标和障碍物的位置和几何形状由高斯概率分布给出。这些概率与代理、障碍物和目标的几何图形相结合,提供了一个全局概率导航函数φ。PNF提供了一个解析解决方案,并保证同时拦截所有目标,同时将代理的风险限制在给定值内。解决方案的复杂性与目标和代理的数量呈线性关系,因此不限于小问题。虽然PNF提供的解决方案不是最优的,但它提供了简单有效的解决方案,适合于大范围的实时应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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