US engineers are trying to develop a low-power collision detector for robots, drones and even self-driving cars that copies the ability of locusts to fly in huge swarms without hitting each other.
It’s not new to design automatic systems inspired by insect vision (see, for example, the recent Cosmos story about a beetle with a camera backpack), but locusts, it seems, do things differently.
According to the team from Penn State University, they have a single specialised neuron, called the Lobula Giant Movement Detector (LGMD), which receives two different inputs; one is the image of an approaching locust (excitatory signal), the other the change in angular velocity of that invading locust with respect to themselves (inhibitory signal).
“Because the neuron has two branches, the locust computes the changes in these two inputs and realises that something is going to collide, so the avoiding locust changes direction,” says Darsith Jayachandran, co-author of a paper in the journal Nature Electronics.
These changes are made in hundreds of milliseconds and require minimal energy, which is a large part of the appeal.
Jayachandran and colleagues developed a compact, nanoscale collision detector using monolayer molybdenum sulfide as a photodetector, which is placed on top of a programmable floating gate memory architecture that can mimic the locust’s neuron response using only a tiny amount of energy.
It causes an increase in device current in response to an oncoming object (the excitatory signal) while the underlying programmable memory stack always causes a decrease in the current (the inhibitory signal).
When an object approaches, the excitatory signal is added to the inhibitory stimuli, causing a non-monotonic change in the device current, mimicking the escape response of the LGMD neuron. Response time is two seconds.
“While locusts can only avoid collisions with other locusts, our device can detect potential collisions of a variety of objects at varying speeds,” says co-author Saptarshi Das.
At this point, the sensor is task-specific, and researchers have only tested the device with objects on a direct collision path. They still need to optimise the responses for additional situations.
“We can’t do every measurement, every situation, so we developed a numerical model,” says Aaryan Oberoi. “We can also test if multiple devices on the same chip would work better.
“So far, it looks like a single device will be sufficient. However, a multi-pixel collision detector array can offer collision avoidance in 3D space.”
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