Intelligent Workspace Acquisition, Comprehension and Exploitation for Mobile Autonomy in Infrastructure Denied Environments

汽车只会变得更智能。人们总是渴望更多的机器智能和自主性。我们的需求和期望在不断增加。因此，我们继续将更多的传感器和更多的计算放入机器人中，这些机器人为我们运送、运输、劳动和保卫我们。在这里，我们将自主性解释为机器人在其操作环境中感知、理解并最终自主行动的能力。这项提议是为了让自动驾驶汽车能够在困难的条件下长时间导航。当GPS被拒绝或仅断断续续地可用时，当对环境知之甚少时，当通信零星且不可靠时，或者当照明等运行条件发生不可预测的变化时，条件变得“困难”。然而，有点反常的是，往往正是在这些情况下，我们最需要导航：例如，对福岛等受损核设施的建筑物进行勘测，或者在GPS覆盖较差的城市夜间自动驾驶汽车。智能导航是移动机器人研究的核心。它在远程检查、自动城市驾驶、国防、物流、安全和空间机器人中得到了应用。我们将考虑机器如何获取和管理它们在我们选择的工作空间中持续运行所需的信息。我们的目标是证明性能通过使用和随着时间的推移而提高-这是人类天生的东西，在机器中非常有价值。这个目标提出了一个问题，即控制机器人的计算机应该如何用塑料方式来表示它们的环境--随着时间的推移，这种塑料可以拉伸和拉成不同的形状。我们还需要考虑如何让机器决定如何行动，以提高它们对世界的理解--单独行动，以及与其他车辆协同行动，每一辆车都有不同的传感器和能力。如何透明、连续地校准车辆传感器？如何规划动议，以最大限度地扩大检查的覆盖面和工作空间评估的准确性？在存在不可靠或短距离通信的情况下，如何确保成功的操作？导航、规划和通信管理方面的最先进技术交织在一起是不寻常的，将使我们能够提出并回答具有挑战性的机器人科学问题，这些问题一旦被利用，将对未来变得不可或缺的机器人产生巨大影响。

Vehicles will only get smarter. There will always be a desire for more machine intelligence and autonomy. Our needs and expectations are ever increasing. As a result, we continue to pack more sensors and more computation into the robots that carry, transport, labour for and defend us. Here we interpret autonomy as a robot's ability to sense, understand and ultimately act of its own accord in its operating environment. This proposal is about giving autonomous vehicles the ability to navigate in difficult conditions over long periods of time. Conditions become "difficult" when GPS is denied or only intermittently available, when little, if anything, is known about the environment, when communications are sporadic and unreliable or when operating conditions like lighting change unpredictably. And yet, somewhat perversely, it is often in just these conditions that our need to navigate is greatest: consider, for example, the surveying of buildings in a stricken nuclear facility such as Fukushima, or the autonomous driving of cars at night in cities where GPS coverage is poor. Intelligent navigation lies at the heart of much of mobile robotics research. It finds application in remote inspection, autonomous urban driving, defence, logistics, security and space robotics.We shall consider how machines can acquire and manage the information they need to operate persistently in workspaces of our choosing. The goal is to demonstrate that performance improves through use and over time - something that comes naturally to humans and is immensely valuable in a machine. This goal poses questions about how the computers that control robots should represent their environment in a plastic fashion - one which can be stretched and pulled into different shapes over time. We also need to consider how to enable machines to decide how to act to improve their understanding of the world - alone and in concert with other vehicles, each with different sensors and capabilities. How can vehicle sensors be calibrated transparently and continuously? How can motion be planned to maximise both the coverage of inspections and the accuracy of workspace assessments? How can successful operation be guaranteed in the presence of unreliable or short-range communications?This interweaving of the state of the art in navigation, planning and communications management is unusual and will allow us to ask and provide answers to challenging robotics science questions which, when exploited, will have a dramatic impact on the robots that will become indispensable in the future.