Detector makes sweeping improvements
When you take the bus to work, or drive to the shops, you expect the road to be clean, not cluttered with litter or scattered with stones. Street sweeping is something that’s easy to take for granted, but there’s more to the job that a quick flick of a broom. Efficient cleaning relies on vehicle drivers selecting and controlling brushes according to the debris to be cleared.
While operators concentrate on following the kerb safely they have little time for fine tuning the sweeping process. So researchers from the University of Surrey have decided to give street sweeping a scientific fillip. By identifying the rubbish ahead, a computer can automatically choose the best brush or stroke for maximum efficiency.
Graham Parker and his team from the School of Mechanical and Materials Engineering have closely investigated the sweeping action of rotary brushes found on sweeping vehicles. He categorises two forms of brush. ‘Cutters’ are stiff when forced down onto the surface. They are best used for compacted material like sand on the road. The tines on ‘flicking’ brushes, by contrast, are better designed to throw loose debris into the path of the vacuum hose underneath the sweeper truck.
“Although the operation of the road sweeping vehicle is straightforward,” says Professor Parker, “the choice of which brush to fit and its angle to the ground clearly affects the overall brushing performance when the debris types vary. A sensor system that could determine the most appropriate brush would make a better and more efficient machine.”
The team’s debris detector comprises a digital camera and a laser. Image processing of the camera image allows computers to pinpoint debris and calculate its size and shape.
“Image processing usually requires a lot of computing time, but in a sweeper vehicle you need real-time calculations,” explains Professor Parker. “By using the laser we can build up a 3-D profile of the road without intense processing, just identifying bright pixels in a relatively dark background. When, and only when more than this information is needed to analyse a scene more computer intensive processing techniques are used.”
Tests so far show than this laser striping system can locate and calculate the shape of most types of litter, from large objects that would damage the machine to small items that like bolts and nuts. For debris, such as sand, gravel and leaves that spread over a wide area, extra image processing is important: sand and leaves require quite different brushing styles.
Once the boundaries of the debris have been identified by laser striping, the computer then compared the pixel intensity over the surface. The varying intensity provides an indication of surface roughness, and therefore a guide to the type of debris. “We found that our smooth lab floor was easily distinguished from wood chip, for instance. This method should be able to distinguish gravel from leaves from sand.”
“We now hope to integrate our understanding of brushing with our sensor and predictive analysis,” concludes Professor Parker. “Hopefully we can improve street cleaning and make life easier for the vehicle operators.”