Multi-Agent Path Planning Under Observation Schedule Constraints
Multi-Agent Path Planning Under Observation Schedule Constraints
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DOI：
10.1109/iros45743.2020.9340747
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Ziqi Yang;Roberto Tron
中科院分区：
文献类型：
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作者：
Ziqi Yang;Roberto Tron
我们认为，多机器人系统的增强安全性的问题，以防止网络攻击者采取控制一个或多个机器人组。我们建立在最近提出的解决方案上，该解决方案利用机器人的物理测量能力来执行内省，即，使用组的其他成员检测受损代理的恶意操作。特别是，所提出的解决方案发现多智能体路径上的离散空间相结合的一组相互观察在特定locations检测机器人与预定routes.In显着偏差，在本文中，我们开发了一个计划器，工作在连续的配置空间，同时也考虑到类似的时空约束。此外，规划器允许更一般的任务，可以制定为任意光滑的成本函数被指定。本文中考虑的约束和目标的组合不容易由流行的路径规划算法（例如，基于采样的方法），因此我们提出了一种基于交替方向乘法（ADMM）的方法。ADMM是能够找到局部最优解的问题，涉及不同类型的目标和非凸的时间和空间约束，并允许不可行的初始化。我们基准我们提出的方法多智能体地图探索最小的不确定性成本函数，障碍和观察时间表的限制。
We consider the problem of enhanced security of multi-robot systems to prevent cyber-attackers from taking control of one or more robots in the group. We build upon a recently proposed solution that utilizes the physical measurement capabilities of the robots to perform introspection, i.e., detect the malicious actions of compromised agents using other members of the group. In particular, the proposed solution finds multi-agent paths on discrete spaces combined with a set of mutual observations at specific locations to detect robots with significant deviations from the preordained routes.In this paper, we develop a planner that works on continuous configuration spaces while also taking into account similar spatio-temporal constraints. In addition, the planner allows for more general tasks that can be formulated as arbitrary smooth cost functions to be specified. The combination of constraints and objectives considered in this paper are not easily handled by popular path planning algorithms (e.g., sampling-based methods), thus we propose a method based on the Alternating Direction Method of Multipliers (ADMM). ADMM is capable of finding locally optimal solutions to problems involving different kinds of objectives and non-convex temporal and spatial constraints, and allows for infeasible initialization. We benchmark our proposed method on multi-agent map exploration with minimum-uncertainty cost function, obstacles, and observation schedule constraints.