基于共识束算法的危险环境下无人驾驶车辆任务分配

Zhiyu Duan, Xiaolin Chen, Ziyuan Xie
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摘要

本文采用基于共识的束算法(Consensus-Based Bundle algorithm, CBBA)解决了基于危险广播算法的无人驾驶车辆分布式任务分配问题。通过定义飞行无人机发现的危险区域,将传统的基于共识的束算法扩展为危险广播,称为危险广播共识束算法(DBCBBA)。在未知任务区域,使用时间折扣和距离来描述任务的收益函数。与传统的CBBA算法相比,该算法可以应用于真实复杂的环境,通过分布式危险广播架构节省了更多的无人驾驶车辆。最后,通过仿真实验验证了该算法在未知危险区域下的性能。
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Consensus-Based Bundle Algorithm-Based Task Allocation for Unmanned Vehicles in Dangerous Environment
This paper solves the distributed task allocation problem based on the danger-broadcast algorithm for unmanned vehicles with the Consensus-Based Bundle Algorithm (CBBA). By defining the dangerous region that is found by the flying unmanned vehicle, the traditional Consensus-Based Bundle Algorithm is extended with the danger broadcast, which is named as Danger-broadcast Consensus-Based Bundle Algorithm (DBCBBA). In the unknown task areas, the time discount and distance are used to describe the task's revenue function. Compared with the traditional CBBA, the proposed algorithm can be adopted to a real and complex environment, and more unmanned vehicles are saved by distributed danger-broadcast architecture. In the end, simulation experiments demonstrate the performance of the proposed algorithm for the unknown danger zone.
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