RI: Small: Robust motion planning for multirobot target tracking

自主的移动的机器人最近已经被用于诸如监视和入侵者检测的任务。然而，协调这样的机器人网络是一项艰巨的任务，特别是如果人们试图避免被发现。这项研究旨在开发一个可以检测和跟踪农田入侵者的自主飞行器网络。面对最近与农业间谍和开放农场生物技术盗版有关的事件，机器人入侵检测系统将对农民有所帮助。所开发的技术也将适用于空中交通管制、运输网络监测和沿海地区巡逻等领域。通过教育和推广活动，该项目旨在为机器人和网络安全等日益增长的领域创造一支高技能的劳动力队伍。 所提出的研究解决了多机器人系统部署用于跟踪不可预测和敌对目标的鲁棒运动规划设计的问题。在这些问题上的一个严重挑战是缺乏关于目标未来行动的信息。基于微分对策理论的工具，本研究开发的目标分配和轨迹生成算法的一组观察员机器人跟踪多个入侵者，与跟踪性能的保证。使用自下而上的方法，提出了一个运动规划器的多机器人目标跟踪，可以处理的传感和运动的限制所造成的障碍物在环境中，可变数量的目标，和嘈杂的传感器和执行器的测量。拟议的研究包括在PI实验室的多四旋翼试验台上测试和验证跟踪规划器，以检测农业农田中未经授权的入侵。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Autonomous mobile robots have recently been used for tasks such as surveillance and intruder detection. Coordinating a network of such robots, though, is a difficult task, especially if the people are trying to avoid detection. This research aims to develop a network of autonomous aerial vehicles that can detect and track intruders in farmlands. In the face of recent incidents related to agricultural espionage and biotech piracy in open farms, a robotic intrusion detection system would be helpful to farmers. The techniques developed would also be applicable in areas such as air traffic control, surveillance of transportation networks, and patrol of coastal areas. Through educational and outreach activities, the project aims to create a highly skilled workforce in the growing areas robotics and cyber-security. The proposed research tackles the problem of designing robust motion plans for multirobot systems deployed for tracking unpredictable and adversarial targets. A serious challenge in such problems is the lack of information about the targets' future actions. Based on tools from differential game theory, this research develops target allocation and trajectory generation algorithms for a team of observer robots to track multiple intruders, with guarantees on tracking performance. Using a bottom-up approach, a motion planner is proposed for multirobot target tracking that can handle sensing and motion constraints posed by obstacles in the environment, variable number of targets, and noisy sensor and actuator measurements. The proposed research includes testing and validation of the tracking planner on a multi-quadrotor testbed in the PI's lab to detect unauthorized intrusion in agricultural farmlands.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.