Intelligent coordination of multiple unmanned ground vehicles and unmanned aerial vehicles

未来，自主无人机（uav）需要与无人地面车辆（ugv）合作，共享信息和协调活动。私营部门和政府机构已经将无人机和ugv用于国土安全、侦察、监视、数据收集、城市规划和地理信息工程。支持由多个ugv和uav组成的多智能体系统（MAS）决策过程的重要研究正在进行中。拟议的五年研究计划将通过研究智能控制（MASs）的基本问题，包括合作、协调、传感器融合、无碰撞导航和多ugv和无人机的远程操作，为这些努力做出贡献。具有多个ugv和uav的MASs是典型的分布式系统。我们将使用人工智能方法开发智能控制算法，用于自动驾驶车辆的合作和协调。我们还将开发传感器融合和无碰撞导航技术，因为它们是动态和未知环境中系统的关键问题。当在危险或未知环境中使用ugv和无人机时，通常需要人类操作员能够远程操作系统。因此，我们将为一支无人机和ugv团队开发远程操作机制。仿真技术对航天飞行器的发展具有重要意义。在这项研究中，我们将把一个高保真的无人机模型与移动机器人和网络计算机集成在一起，作为多个无人机和ugv控制的测试平台。所提出的MAS控制技术将提高分布式系统控制的可靠性和效率。研究结果将广泛应用于商业部门和国防工业。此外，该研究将为加拿大雇主高度要求的智能系统、控制理论、机器人和航空航天领域的高素质人才提供培训。

In the future, autonomous Unmanned Aerial Vehicles (UAVs) need to work in teams with Unmanned Ground Vehicles (UGVs) to share information and coordinate activities. The private sector and government agencies have implemented UAVs and UGVs for homeland security, reconnaissance, surveillance, data collection, urban planning, and geomatics engineering. Significant research is in progress to support the decision-making process for a Multi-Agent System (MAS) consisting of multiple UGVs and UAVs. The proposed five-year research program will contribute to these efforts by investigating fundamental issues in intelligent control of (MASs, including cooperation, coordination, sensor fusion, collision-free navigation and teleoperation of multiple UGVs and UAVs. MASs with multiple UGVs and UAVs are typical distributed systems. We will use artificial intelligence approaches to develop intelligent control algorithms for the cooperation and coordination of autonomous vehicles. We will also develop technologies for sensor fusion and collision-free navigation because they are critical issues for systems in a dynamic and unknown environment. When using UGVs and UAVs in hazardous or unknown environments, it is often desirable for a human operator to be able to teleoperate the system. Therefore, we will develop teleoperation mechanisms for a team of UAVs and UGVs. Simulation technologies have become important to the development of aerospace vehicles. In this research, we will integrate a high-fidelity UAV model with mobile robots and networked computers as a test bed for the control of multiple UAVs and UGVs. The proposed MAS control technologies will improve the reliability and efficiency of the control of distributed systems. The results will have wide applications in both commercial sectors and the defence industry. Furthermore, the research will provide training for highly qualified personnel in the areas of intelligent systems, control theories, robotics and aerospace, which are highly demanded by Canadian employers.