Off-Road Multivehicle Autonomy for Challenging Outdoor Environments

该项目是关于研究和开发可演示的实用方法和算法，用于使用多个机器人车辆进行侦察活动，特别关注特别恶劣和不确定的地形（例如，岩石，森林，非结构化，越野），以及在此类行动中对潜在入侵者保持保密的问题。拟议的研究包括车辆导航、机器人团队、基于学习的控制、地形评估，以及不透明和不可观察性的概念，以保持保密性，其中一些车辆可能只有半自主、远程驾驶和/或由人类操作员监督。除了这项基础研究外，项目计划还包括实际移动机器人的集成和代表性环境的现场测试。虽然侦察是与该项目有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其他“非公路”情况下得到应用，包括采矿自动化、林业、农业以及行星探测和科学。拟议的项目还旨在在多个方面提供独特的HQP培训经验。学生研究人员将有机会不仅在多个学科之间进行合作，而且通过访问最先进的移动机器人和现场测试环境，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.