
Scientists in China are turning nectar-sucking bees into modern warfare cyborgs by inserting small controllers into their brains and dictating where they can fly. Once nature's most efficient pollinator is turned into a cyborg, it can be used for covert military operations or in missions in locations inaccessible to humans.
Researchers at the Beijing Institute of Technology have developed the world's lightest brain controller, weighing 74 milligrams, that can be used to control the bees. The device is strapped on the bee's back and pierces its brain using three needles. It creates illusions with electronic pulses to command flight: turn left, turn right, advance, retreat. Nine out of 10 times, the bee obeyed, according to a report in the South China Morning Post.
"Insect-based robots inherit the superior mobility, camouflage capabilities and environmental adaptability of their biological hosts," said Professor Zhao Jieliang, whose team built the mind-controller.
"Compared to synthetic alternatives, they demonstrate enhanced stealth and extended operational endurance, making them invaluable for covert reconnaissance in scenarios such as urban combat, counterterrorism and narcotics interdiction, as well as critical disaster relief operations," they added.
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To develop the mind-controller device, Chinese scientists took inspiration from the cyborg controller developed in Singapore that can command beetles and cockroaches. However, it was triple the weight.
Mr Zhao's team brought the weight down by printing the circuits on polymer film, which houses numerous chips, including an infrared remote, whilst being as thin as insect wings. Despite reducing the weight, the challenge remains for Mr Zhao's team.
The current batteries are not able to last very long, but any larger and the packs would be too heavy for the bees to carry. The same device cannot easily be used on different insects as each responds to signals on different parts of their bodies.
"In future research, precision and repeatability of insect behaviour control will be enhanced by optimising stimulation signals and control techniques," wrote Zhao's team.
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