A vision of futuristic robotic aircraft and land vehicles that can sense and close in on hidden targets is being explored by a new four-university research initiative led by Duke University’s Pratt School of Engineering.
‘The idea of doing multi-sensing on multiple unmanned platforms is new,’ said Lawrence Carin, a Pratt School professor of electrical and computer engineering who is leading the effort. ‘It hasn’t been done before. Almost everything we have proposed is new.’
The technology being developed would allow increasingly localised sensor searches for quarry so hidden that ‘you don’t even know where to start to look without this technology,’ Carin said. ‘This is a very challenging problem. It will constitute a big leap ahead from where things are today.’
Researchers at Duke, Georgia Institute of Technology, Stanford University and the University of Michigan will each take on different parts of developing the enabling mathematical underpinnings of this technology with $6 million in US Defence Advanced Research Projects Agency (DARPA) funding.
The objective is to develop algorithms to ‘train’ and control multiple sensors that, with increasing precision, could detect invisible signals emanating from such targets, and trace those signals back to their sources – a technique called inversion.
Once potential targets are perceived, the search might be pinpointed using a different mix of sensors. ‘For instance, if vehicles are moving through trees, you could actually sense the motion,’ said Carin. ‘A hole in the ground will cause perturbation to the gravity that’s observed on the surface, so you can detect that.’ Other sensors, he noted, might likewise register acoustic vibrations.
These arrays would do more than just sense passively. They would also have to infer from these different signals what their sources are through the inversion process. ‘In the campaign in Afghanistan, they’re using a lot of unmanned Predator drone aircraft,’ Carin said. ‘Our vision is that in the future you could have multiple drones out there, not just collecting data but actually doing the inversion.’
‘You would like them to be self-controlled,’ he added. That means they would not need detailed updating to tell them what to do after being sent out to a certain area. ‘They would make decisions on their own,’ he said. ‘A sensor would have to be able to think.’
‘You could have multiple drones and multiple land robots that communicate with one another and do the inversion on the fly, because it is too complicated for them to communicate back, and moreover through communications traffic they will reveal themselves,’ Carin added.