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An Integrated Vision and Control Architecture for Agile Robotic Exploration

用于敏捷机器人探索的集成视觉和控制架构

基本信息

批准号：
EP/M019284/1
负责人：
Piotr Dudek
金额：
$ 109.37万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FM019284%2F1
关键词：
Integrated Vision Control Architecture Agile

项目摘要

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.

自主机器人能够在未知环境中独立和智能地导航，有可能大大提高人类的安全和保障。它们可以代替人们执行潜在危险的任务，例如在灾区进行搜索和救援行动，或调查核/化学设施。视觉是能够实现这种能力的主要感官之一，然而，视觉信息处理是非常困难的，特别是在快速移动机器人所需的速度下，特别是在系统的低重量、功耗和成本受到关注的情况下。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传感和处理硬件与视觉、导航和控制算法紧密集成，以实现下一代自主机器人。该系统的核心将是一种称为“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，包括每个像素的处理器。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为预处理图像，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。在像素级的特征提取导致非常有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中的“飞行时间”数据。这将允许提取到特征的距离并将其与图像平面数据相结合以进行视觉跟踪，从而简化和加速实时状态估计和映射能力。本项目将开发视觉算法，以最大限度地利用这一新的硬件技术。本项目不仅将开发独特的视觉处理系统，还将紧密结合控制系统设计。传统上，视觉和控制系统是独立开发的，下游的信息流从传感器到电机控制。在我们的系统中，信息流将是双向的。控制系统参数将被传递到图像传感器本身，指导计算工作并减少处理开销。例如，一个旋转指令传递到控制系统，不仅会导致车辆运动的控制驱动，而且会导致光学跟踪沿着同一路径。因此，该项目的一个关键组成部分将是跨所有三个层面的信息管理和控制：传感，视觉感知和控制。信息共享将以多种速率进行，并且可以是预定的或请求的。共享信息和分布式计算将为高度敏捷的机器人系统的控制能力提供突破。同时适用于非常广泛的学科，我们的系统将在要求苛刻的自主航空机器人领域进行测试。我们将在无人机（UAV）上集成新的视觉传感器，开发一种控制系统，以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，通过敏捷的机动和快速变化的轨迹来控制车辆。尽管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。任何目前受跟踪能力限制的应用，特别是当与快速、苛刻的控制挑战相结合时，都将受益于这项工作。我们将展示用于探索的无人机的敏捷，基于视觉的控制的一步变化，并在此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不同领域受益。