Off-Road Multivehicle Autonomy for Challenging Outdoor Environments

这个项目是关于研究和开发使用多个机器人车辆进行侦察活动的明显实用的方法和算法，特别侧重于特别恶劣和不确定的地形(例如，岩石、森林、无结构、越野)以及在这类行动中对潜在入侵者保密的问题。拟议的研究包括车辆导航、机器人组合、基于学习的控制、地形评估，以及用于保密的不透明和不可观察的概念，其中一些车辆可能只是半自动、远程驾驶和/或由操作员监督。除了这项基础性研究外，该项目计划还包括在实际移动机器人上进行集成和在典型环境中进行现场测试。虽然侦察是与该项目相关的研究伙伴的主要兴趣，但预计这项研究也可能在加拿大的其他“越野”方案中得到应用，包括采矿自动化、林业、农业以及行星探索和科学。拟议的项目还旨在提供多个方面的独特HQP培训经验。学生研究人员将有机会不仅在几个学科之间进行合作，而且还可以通过访问最先进的移动机器人和现场测试环境的方式将理论付诸实践。

This project is about studying and developing demonstrably practical methods and algorithms for reconnaissance activities using multiple robotic vehicles, with a particular focus on especially harsh and uncertain terrains (e.g., rocky, forested, unstructured, off-road) as well as on the problem of maintaining secrecy from potential intruders in such operations. The proposed research includes work on vehicle navigation, robot teaming, learning-based control, terrain assessment, as well as the concepts of opacity and unobservability for maintaining secrecy, where some vehicles may be only semi-autonomous, remotely driven, and/or supervised by human operators. In addition to this fundamental research, the project plan includes integration on actual mobile robots and field testing in representative environments. Although reconnaissance is the primary interest of the research partners associated with this project, it is anticipated that this research may also find application in other Canadian "off-road" scenarios, including mining automation, forestry, agriculture, as well is in planetary exploration and science. The proposed project also aims to provide unique HQP training experiences on multiple fronts. Student researchers will be given the opportunity to not only collaborate across several disciplines, but also take theory into practice by way of access to state-of-the-art mobile robots and field testing environments.