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A five-step drone collaborative planning approach for the management of distributed spatial events and vehicle notification using multi-agent systems and firefly algorithms

机译:使用多智能体系和萤火虫算法管理分布式空间事件和车辆通知的五步无人机协作规划方法

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In spite of the performance that existing approaches for drone collaborative planning have demonstrated, there is still a need for new solutions which are capable of effectively identifying the right tasks for the right drones at the right times while maximizing the total benefits obtained from the drones' actions. These new solutions should be particularly tested within the context of intelligent transportation systems to assess their impact on mobility and traffic flow. In order to address these issues, we present in this paper a new a five-step solution for drone collaborative planning. Our solution uses a Multi-Agent System as well as a Firefly Algorithm solution to enable drones jointly neutralize ongoing events by considering trust factors and cost/benefit analysis. The solution, which is also capable of issuing appropriate warnings to vehicles to prevent them from incurring any undesirable/dangerous impact due to ongoing events, is using a reward-driven competition to encourage drones to join collaborating teams. Our simulations are showing promising results in terms of processing time, energy consumption, and total reward obtained compared to two other planning approaches relaying on random and priority-based selection of the next locations that drones will visit respectively.
机译:尽管已经表现出了无人机协作计划现有方法的性能,但仍然需要新的解决方案,这些解决方案能够在正确的时间内有效地识别正确的无人机的正确任务,同时最大化从无人机获得的总益处行动。这些新解决方案应在智能交通系统的背景下特别测试,以评估其对移动性和交通流量的影响。为了解决这些问题,我们在本文中展示了一个新的无人机协作计划的五步解决方案。我们的解决方案采用多种代理系统以及萤火虫算法解决方案,使无人机通过考虑信任因素和成本/效益分析来联合中和持续的事件。该解决方案,该解决方案还能够向车辆发出适当的警告,以防止由于持续的事件产生任何不良/危险的影响,是使用奖励驱动的竞争,鼓励无人机加入合作团队。我们的模拟在加工时间,能耗和总奖励方面表现出有前途的结果,而另外两种规划方法在随机和优先级选择的另一个地方的选择中分别访问过的下一个地点。

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