Collision Detector Mimics Locust Swarms

Pennsylvania State University, University Park

Plagues of locusts, containing millions of insects, fly across the sky to attack crops but the individual insects do not collide with each other within these massive swarms. A team of engineers is creating a low-power collision detector that mimics the locust avoidance response to help robots, drones, and even self-driving cars avoid collisions.

Locusts use a single, specialized neuron called the Lobula Giant Movement Detector (LGMD) to avoid collisions. The neuron receives two different signals. The first is an image of an approaching locust that falls on the avoiding locust’s eye. The closer the invading locust gets, the larger the image and the stronger this excitation signal becomes. The other input is the change in angular velocity of the invading locust with respect to the avoiding locust. Because the neuron has two branches, the locust computes the changes in these two inputs and realizes that something is going to collide, so the avoiding locust changes direction.

The researchers developed a compact, nanoscale collision detector using monolayer molybdenum sulfide as a photodetector. They placed the photodetector on top of a programmable floating gate memory architecture that can mimic the locust’s neuron response using only a tiny amount of energy.

Locusts move at two to three miles per hour and make directional changes in hundreds of milliseconds. The decision to move employs nonlinear mathematics and a miniscule energy expenditure. This quick reaction and modest energy use is attractive for mechanized collision detectors. Current detectors for autonomous automobiles are very large and very heavy — the researchers’ collision detector responds in two seconds. Also, rather than be a jack-of-all trades detector, the molybdenum sulfide-based sensor is task-specific but because it is so small and uses so little energy, that is acceptable.

The photodetector 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 found in locusts. The new device can detect potential collisions of a variety of objects at varying speeds.

The researchers have only tested the device with objects on a direct collision path; they still need to optimize the responses for additional situations.

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