Researchers from UOW have advanced our understanding of the most effective echolocation techniques, which could be used to develop better navigation aids for the blind and to create high-tech robots to rescue people from disaster scenes.
Robotics engineer Dr Shérine Antoun led the study, which will be published in the December issue of IEEE Sensors Journal. By setting up a navigation experiment using techniques learnt form observing how blind people use ultrasonic mobility aids to navigate corridors (whether they track the wall of the corridor, the free space or a combination of the two), the researchers have discovered a wealth of previously unknown data contained in the backscattered ultrasonic echo and have developed an optimal sensor scan motion.
“When we considered sensing and sensor positioning for corridor navigation we concluded that one sensor is sufficient and an appropriate scan motion appears to be: left until a wall is detected, right and down until the floor is detected, up to free space, repeat,” Dr Antoun, from the School of Computer Science and Software Engineering, said.
“These insights may lead to better navigation aids, improved techniques for the use of these navigation aids, and new guidelines to improve the navigability of environments for blind people.”
Dr Antoun said the findings also have implications for navigation and localisation in extreme or hostile environments such as the deep ocean, disaster scenes or underground environments where darkness, pollution, and dust render cameras, laser scanners, and other sensors ineffective.
“Our work is progressing towards building more robust robotic systems and better understanding the limits of autonomous navigation,” he said.
In continuation of this research, Dr Antoun hopes to equip swarms of nanorobots with navigation and localisation capabilities based on this research to test how they would work on mine collapse sites. Using wireless networks and ultrasonic sensor to build 3D maps of the sites, it is hoped the nanorobots would provide rescuers with real-time data on safe pathways through the disaster site.