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Bioinspired vision processing for autonomous terrestrial locomotion

用于自主地面运动的仿生视觉处理

基本信息

批准号：
EP/J012025/1
负责人：
J Burn
金额：
$ 69.92万
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FJ012025%2F1
关键词：
Bioinspired vision processing autonomous terrestrial

项目摘要

Land vehicles have been designed almost exclusively to use wheels whereas terrestrial animals almost exclusively use legs for locomotion. Wheeled systems can be fast and efficient on hard flat ground; leg based systems are more versatile and efficient on natural terrain. As we move towards a future of autonomous systems operating beyond the extent of the road network and on other planets it is likely that development of robust artificial leg-based locomotion will become increasingly important.At present, several limits of technology prevent the emergence of autonomous legged systems with biocomparable performance. Even if a system was to emerge that could walk, run, leap, and turn without falling over, the technology does not exist safely to guide it through complex terrain using vision. Typically research into using vision for autonomous locomotion is undertaken using available vehicle technology - suggesting that the emergence of high-performance, vision-guided legged systems might occur at some time following the emergence of a basic high performance legged vehicle platform. In a novel approach we will expedite the development of a vision control architecture for locomotion over complex terrain by using human subjects as high performance vehicle platforms.The visual scene captured using a head mounted camera will be processed to identify terrain characteristics known to be important for control of locomotion. A map of the terrain synthesised in 3D virtual space and updated in real-time is presented to the human using a virtual reality headset. The overall outcome measure will be the locomotion performance achieved by the humans using the system compared to that with no vision information available and with normal vision.There are many benefits of this approach: it will allow us to investigate how humans modulate gait paramters and limb mechanics to compensate for partial or unreliable inforamtion about the environment. It will provide insight into the integration of feedforward and feedback control of locomotion. It will allow us to determine the locomotion performance that is possible from a given amount and quality of visually derived information given a highly developed locomotor platform and thus to understand how these two components of a high performance locomotor sytem combine to determine overall performance.The basic principles and technologies establilsed during this project will be applicable to any land vehicle whether based on wheels or legs. Additionally, the processing of visual information for locomotion control is a special case of the more generalised task to search the ground for an object or visual feature. The technology developed in this project may be translated to other applications in which visually-guided autonomous function is required.

陆地交通工具的设计几乎完全使用轮子，而陆地动物几乎完全使用腿来移动。轮式系统在坚硬的平地上可以快速有效;基于腿的系统在自然地形上更加通用和有效。随着我们走向未来的自主系统运行的范围以外的道路网络和其他星球上很可能是强大的人工腿为基础的运动的发展将变得越来越重要。目前，技术的几个限制，防止出现自主腿系统的生物性能。即使出现了一个可以行走、奔跑、跳跃和转弯而不会摔倒的系统，也没有技术可以安全地使用视觉引导它通过复杂的地形。通常，使用视觉进行自主运动的研究是使用现有的车辆技术进行的-这表明高性能的视觉引导腿式系统的出现可能会在基本的高性能腿式车辆平台出现之后的某个时间出现。在一个新的方法中，我们将加快发展的视觉控制架构的复杂地形上的运动，使用人体作为高性能vehicle platforms.The视觉场景捕获使用头戴式摄像机将被处理，以确定已知的地形特征是重要的运动控制。在3D虚拟空间中合成并实时更新的地形地图使用虚拟现实耳机呈现给人类。总体结果的衡量将是人类使用该系统所实现的运动性能相比，没有视觉信息可用，并与正常view.There有很多好处，这种方法：它将使我们能够研究人类如何调节步态参数和肢体力学，以补偿部分或不可靠的信息有关的环境。它将提供深入了解整合的前馈和反馈控制的运动。它将使我们能够确定的运动性能，是可能的，从给定的数量和质量的视觉衍生信息给定一个高度发达的运动平台，从而了解如何这两个组成部分的高性能运动系统联合收割机结合起来，以确定整体performance.The的基本原则和技术establiled在这个项目将适用于任何陆地车辆，无论是基于车轮或腿。此外，用于运动控制的视觉信息的处理是在地面上搜索物体或视觉特征的更一般化任务的特殊情况。该项目中开发的技术可能会转化为其他需要视觉引导自主功能的应用。