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.

尽管无人机协同规划的现有方法已经证明了性能，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有效地为正确的无人机确定正确的任务，同时最大限度地从无人机获得的总收益行动。这些新解决方案应该在智能交通系统的背景下进行特别测试，以评估它们对移动性和交通流量的影响。为了解决这些问题，我们在本文中提出了一种新的无人机协同规划五步解决方案。我们的解决方案使用多代理系统和萤火虫算法解决方案，通过考虑信任因素和成本/收益分析，使无人机能够共同抵消正在进行的事件。解决方案，它还能够向车辆发出适当的警告，以防止它们因正在进行的事件而遭受任何不良/危险的影响，正在使用奖励驱动的竞赛来鼓励无人机加入协作团队。与其他两种规划方法相比，我们的模拟在处理时间、能源消耗和获得的总奖励方面显示出有希望的结果，这些方法分别基于随机和基于优先级选择无人机将访问的下一个位置。