Scientists from the University of Lincoln and Newcastle University have created a computerised system which allows for autonomous navigation of mobile robots based on the locust’s unique visual system.
The work could provide the blueprint for the development of highly accurate vehicle collision sensors, surveillance technology and even aid video game programming according to the research published today.
Locusts have a distinctive way of processing information through electrical and chemical signals, giving them an extremely fast and accurate warning system for impending collisions.
The insect has incredibly powerful data processing systems built into its biology, which can in theory be recreated in robotics.
Inspired by the visual processing power built into these insects’ biology, Professor Shigang Yue from the University of Lincoln’s School of Computer Science and Dr Claire Rind from Newcastle University’s Institute of Neuroscience created the computerised system.
Their findings are published today in the International Journal of Advanced Mechatronic Systems.
The research started by understanding the anatomy, responses and development of the circuits in the locust brain that allow it to detect approaching objects and avoid them when in flight or on the ground.
A visually stimulated motor control (VSMC) system was then created which consists of two movement detector types and a simple motor command generator. Each detector processes images and extracts relevant visual clues which are then converted into motor commands.
Prof Yue said: “We were inspired by the way the locusts’ visual system works when interacting with the outside world and the potential to simulate such complex systems in software and hardware for various applications. We created a system inspired by the locusts’ motion sensitive interneuron – the lobula giant movement detector. This system was then used in a robot to enable it to explore paths or interact with objects, effectively using visual input only.”
Funded by the European Union’s Seventh Framework Programme (FP7), the research was carried out as part of a collaborative project with the University of Hamburg in Germany and Tsinghua University and Xi’an Jiaotong University, China.
The primary objective of the project, which started in 2011 and runs for four years, is to build international capacity and cooperation in the field of biologically inspired visual neural systems.
Prof Yue explained: “Effective computer vision is a major research challenge. Vision plays a critical role in the interaction of most animal species, and even relatively low order animals have remarkable visual processing capabilities. For example, insects can respond to approaching predators with remarkable speed. This research demonstrates that modelling biologically plausible artificial visual neural systems can provide new solutions for computer vision in dynamic environments. For example, it could be used to enable vehicles to understand what is happening on the road ahead and take swifter action.”
Dr Claire Rind has been working on the locust’s visual system for several years.
She said: “Developing robot neural network programmes, based on the locust brain, has allowed us to create a programme allowing a mobile robot to detect approaching objects and avoid them. It’s not the conventional approach as it avoids using radar or infrared detectors which require very heavy-duty computer processing. Instead it is modelled on the locust’s eyes and neurones as the basis of a collision avoidance system.
“Taking this work forward we want to apply it to collision avoidance systems in vehicles which is a major challenge for the automotive industry. While some collision-avoidance features are pricey options on luxury cars, their performance is not always as good as it could be – and they come at a high cost. This research offers us important insights into how we can develop a system for the car which could improve performance to such a level that we could take out the element of human error.”
The paper is entitled ‘Visually Stimulated Motor Control for a Robot with a Pair of LGMD Visual Neural Networks’. It can be downloaded in full from the website.
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Marie Daniels – PR Officer