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Details of Grant 

EPSRC Reference: EP/J012025/1
Title: Bioinspired vision processing for autonomous terrestrial locomotion
Principal Investigator: Burn, Dr J
Other Investigators:
Bull, Professor D Gilchrist, Professor ID Mayol-Cuevas, Professor WW
Researcher Co-Investigators:
Project Partners:
Department: Mechanical Engineering
Organisation: University of Bristol
Scheme: Standard Research
Starts: 24 August 2012 Ends: 23 August 2015 Value (£): 548,721
EPSRC Research Topic Classifications:
Digital Signal Processing Image & Vision Computing
Robotics & Autonomy
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
15 Dec 2011 Autonomous and Intelligent Systems Meeting Announced
18 Aug 2011 Autonomous and Intelligent Systems Sift Announced
Summary on Grant Application Form
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.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.bris.ac.uk