An Integrated Vision and Control Architecture for Agile Robotic Exploration

自主机器人能够在未知环境中独立而智能地导航，具有显著提高人类安全和保障的潜力。他们可以在潜在危险的任务中取代人，例如在灾区的搜索和救援行动，或核/化学设施的勘测。视觉是实现这种能力的主要感官之一，然而，视觉信息处理是出了名的困难，特别是在快速移动的机器人所需的速度下，特别是在系统的低重量、功率消耗和成本令人担忧的情况下。传统的硬件和算法无法胜任这项任务。这里的提议是将新型的传感和处理硬件与视觉、导航和控制算法紧密结合，以实现下一代自主机器人。系统的核心将是一种被称为“视觉芯片”的设备。这种定制的集成电路不同于传统的图像传感器，每个像素都有一个处理器。这将提供前所未有的性能。大规模并行处理器阵列将被编程对图像进行预处理，将更高级别的特征信息上游传递给视觉跟踪算法和控制系统。像素级的特征提取产生了极其高效和高速的信息吞吐量。新视觉芯片的另一个功能是测量每个像素的飞行时间数据。这将允许提取到特征的距离并与图像平面数据组合用于视觉跟踪，从而简化和加快实时状态估计和映射能力。视觉算法的开发将最大限度地利用这一新的硬件技术。该项目不仅将开发出一种独特的视觉处理系统，而且将紧密结合控制系统的设计。视觉和控制系统传统上是独立开发的，从传感器到电机控制的下游信息流。在我们的系统中，信息流动将是双向的。控制系统参数将传递给图像传感器本身，指导计算工作并减少处理开销。例如，传递到控制系统的旋转需求不仅将导致对车辆运动的控制驱动，而且还将导致沿相同路径的光学跟踪。因此，该项目的一个关键组成部分将是管理和控制所有三个层面的信息：感知、视觉感知和控制。信息共享将以多个速率进行，可以是计划的或请求的。信息共享和分布式计算将为高度敏捷的机器人系统提供控制能力的突破。虽然我们的系统适用于非常广泛的学科，但我们的系统将在要求苛刻的自主空中机器人领域进行测试。我们将把新的视觉传感器集成到无人机(UAV)上，开发一种控制系统，充分利用新的跟踪能力。这将作为完整视觉系统的演示平台，结合非线性算法通过灵活的操作和快速变化的轨迹来控制车辆。虽然该项目将使用特定的视觉跟踪和控制算法，但硬件本身和系统架构将适用于非常广泛的任务。目前受到跟踪能力限制的任何应用程序，特别是在与快速、苛刻的控制挑战相结合时，都将从这项工作中受益。我们将展示对用于探索的无人机的灵活、基于视觉的控制的一步改变，并在这样做的过程中开发一种架构，该架构将在医疗机器人和工业生产等多个领域产生好处。

Autonomous robots, capable of independent and intelligent navigation through unknown environments, have the potential to significantly increase human safety and security. They could replace people in potentially hazardous tasks, for instance search and rescue operations in disaster zones, or surveys of nuclear/chemical installations. Vision is one of the primary senses that can enable this capability, however, visual information processing is notoriously difficult, especially at speeds required for fast moving robots, and in particular where low weight, power dissipation and cost of the system are of concern. Conventional hardware and algorithms are not up to the task. The proposal here is to tightly integrate novel sensing and processing hardware, together with vision, navigation and control algorithms, to enable the next generation of autonomous robots.At the heart of the system will be a device known as a 'vision chip'. This bespoke integrated circuit differs from a conventional image sensor, including a processor with each pixel. This will offer unprecedented performance. The massively parallel processor array will be programmed to pre-process images, passing higher-level feature information upstream to vision tracking algorithms and the control system. Feature extraction at pixel level results in an extremely efficient and high speed throughput of information. Another feature of the new vision chip will be the measurement of 'time of flight' data in each pixel. This will allow the distance to a feature to be extracted and combined with the image plane data for vision tracking, simplifying and speeding up the real-time state estimation and mapping capabilities. Vision algorithms will be developed to make the most optimal use of this novel hardware technology.This project will not only develop a unique vision processing system, but will also tightly integrate the control system design. Vision and control systems have been traditionally developed independently, with the downstream flow of information from sensor through to motor control. In our system, information flow will be bidirectional. Control system parameters will be passed to the image sensor itself, guiding computational effort and reducing processing overheads. For example a rotational demand passed into the control system, will not only result in control actuation for vehicle movement, but will also result in optic tracking along the same path. A key component of the project will therefore be the management and control of information across all three layers: sensing, visual perception and control. Information share will occur at multiple rates and may either be scheduled or requested. Shared information and distributed computation will provide a breakthrough in control capabilities for highly agile robotic systems.Whilst applicable to a very wide range of disciplines, our system will be tested in the demanding field of autonomous aerial robotics. We will integrate the new vision sensors onboard an unmanned air vehicle (UAV), developing a control system that will fully exploit the new tracking capabilities. This will serve as a demonstration platform for the complete vision system, incorporating nonlinear algorithms to control the vehicle through agile manoeuvres and rapidly changing trajectories. Although specific vision tracking and control algorithms will be used for the project, the hardware itself and system architecture will be applicable to a very wide range of tasks. Any application that is currently limited by tracking capabilities, in particular when combined with a rapid, demanding control challenge would benefit from this work. We will demonstrate a step change in agile, vision-based control of UAVs for exploration, and in doing so develop an architecture which will have benefits in fields as diverse as medical robotics and industrial production.