Bioinspired vision processing for autonomous terrestrial locomotion

陆地上的交通工具几乎都是用轮子的，而陆地上的动物几乎都是用腿来移动的。轮式系统可以在坚硬的平地上快速高效；基于腿的系统在自然地形上更加通用和高效。随着我们走向未来的自主系统，这些系统将超越道路网络的范围，在其他星球上运行，基于假肢的强大运动的发展可能会变得越来越重要。目前，一些技术限制阻碍了具有生物可比性能的自主腿系统的出现。即使出现了一个能走、能跑、能跳、能转弯而不会摔倒的系统，这种技术也无法安全地通过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利用现有的车辆技术进行的，这表明在基本的高性能腿式车辆平台出现后，高性能视觉引导腿式系统可能会在某个时候出现。在一种新颖的方法中，我们将通过使用人类受试者作为高性能车辆平台，加快在复杂地形上运动的视觉控制体系结构的开发。使用头戴式摄像机捕获的视觉场景将被处理以识别已知的对运动控制重要的地形特征。在三维虚拟空间中合成并实时更新的地形图通过虚拟现实耳机呈现给人类。总体结果衡量将是使用该系统的人与没有视觉信息和正常视觉的人相比所取得的运动表现。这种方法有很多好处：它将允许我们研究人类如何调节步态参数和肢体力学来补偿部分或不可靠的环境信息。它将提供洞察运动的前馈和反馈控制的集成。这将使我们能够在高度发达的运动平台上，从给定数量和质量的视觉衍生信息中确定可能的运动性能，从而了解高性能运动系统的这两个组成部分如何结合起来决定整体性能。在这个项目中建立的基本原理和技术将适用于任何基于车轮或腿的陆地车辆。此外，用于运动控制的视觉信息处理是在地面上搜索物体或视觉特征的更广义任务的特殊情况。在这个项目中开发的技术可能会被转化为其他应用，其中需要视觉引导的自主功能。

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.