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An energy-optimal framework for assignment and trajectory generation in teams of autonomous agents

机译:自治工人团队中的分配和轨迹生成的能量最优框架

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In this paper, we present an approach for solving the problem of moving N homogeneous agents into M >= N goal locations along energy-minimizing trajectories. We propose a decentralized framework that only requires knowledge of the goal locations and partial observations of the global state by each agent. The framework includes guarantees on safety through dynamic constraints, and a method to impose a dynamic, global priority ordering on the agents. A solution to the goal assignment and trajectory generation problems is derived in the form of a binary program and a nonlinear system of equations. Then, we present the conditions for optimality and characterize the conditions under which our algorithm is guaranteed to converge to a unique assignment of agents to goals. We also solve the fully constrained decentralized trajectory generation problem considering the state, control, and safety constraints. Finally, we validate the efficacy of our approach through a numerical simulation in MATLAB. (C) 2020 Elsevier B.V. All rights reserved.
机译:在本文中,我们提出了一种方法,用于求解沿能量最小化轨迹将n个均匀试剂移动到M> = n射门位置的问题。我们提出了一个分散的框架,只需要每个特工了解目标位置和全球州的部分观察。该框架包括通过动态约束的安全保证,以及一种在代理上强加动态,全局优先级排序的方法。目标分配和轨迹生成问题的解决方案以二进制程序和方程的非线性系统的形式导出。然后,我们展示了最优性的条件,并表征了我们算法保证的条件,以融合到目标的独特分配目标。考虑到国家,控制和安全限制,我们还解决了完全约束的分散轨迹生成问题。最后,我们通过Matlab中的数值模拟验证了我们方法的功效。 (c)2020 Elsevier B.V.保留所有权利。

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