基于事件驱动的多智能体系统数据采集轨迹优化
Event-driven Trajectory Optimization for Data Harvesting in Multi-Agent Systems
AI总结：
本文提出一种基于事件驱动的在线轨迹优化方法，用于多智能体系统中的数据采集问题，通过优化轨迹参数化和IPA算法降低预期采集与交付延迟。
摘要：
我们提出了一种新的基于事件驱动的方法用于在线轨迹优化，以解决数据采集问题：在二维任务空间中，N个移动智能体被赋予从M个静止源收集数据并将其送至基地的任务，目标是最小化预期的采集和交付延迟。我们定义了一个新的性能度量标准，以解决事件驱动控制器中的事件激发问题，并制定了一个最优控制问题。该问题的解提供了一些结构上的见解，但计算上是不可行的，特别是当数据生成过程是随机的时。我们提出了一种将智能体轨迹参数化为通用函数族的方法，这些参数化可以随后通过Infinitesimal Perturbation Analysis (IPA)在线优化。识别了解的性质，包括对随机数据生成过程的鲁棒性和底层混合动态系统的事件集大小的可扩展性。提供了椭圆和傅里叶级数轨迹的显式结果，并给出了与最新状态的图基算法的比较。
英文摘要：
We propose a new event-driven method for on-line trajectory optimization to solve the data harvesting problem: in a two-dimensional mission space, N mobile agents are tasked with the collection of data generated at M stationary sources and delivery to a base with the goal of minimizing expected collection and delivery delays. We define a new performance measure that addresses the event excitation problem in event-driven controllers and formulate an optimal control problem. The solution of this problem provides some insights on its structure, but it is computationally intractable, especially in the case where the data generating processes are stochastic. We propose an agent trajectory parameterization in terms of general function families which can be subsequently optimized on line through the use of Infinitesimal Perturbation Analysis (IPA). Properties of the solutions are identified, including robustness with respect to the stochastic data generation process and scalability in the size of the event set characterizing the underlying hybrid dynamical system. Explicit results are provided for the case of elliptical and Fourier series trajectories and comparisons with a state-of-the-art graph-based algorithm are given.
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