Engineers have worked made drones the size of a bumblebee and loaded them with even tinier sensors and cameras. Almost every part of a drone has been made smaller, except for the brains of the entire operation — the computer chip.
Standard computer chips for quadcoptors and other similarly sized drones process an enormous amount of streaming data from cameras and sensors, and interpret that data on the fly to autonomously direct a drone’s pitch, speed, and trajectory. To do so, these computers use between 10 and 30 watts of power, supplied by batteries that would weigh down a much smaller, bee-sized drone.
Now, engineers at MIT have taken a first step in designing a computer chip that uses a fraction of the power of larger drone computers and is tailored for a drone as small as a bottlecap. They will present a new methodology and design, which they call “Navion,” at the Robotics: Science and Systems conference, held this week at MIT.
A new streamlined chip performs all computations while using just below 2 watts of power — making it an order of magnitude more efficient than current drone-embedded chips.
Engineers at MIT have taken a first step in designing a computer chip that uses a fraction of the power of larger drone computers and is tailored for a drone as small as a bottlecap. Image: Christine Daniloff/MIT
From the ground up
Current minidrone prototypes are small enough to fit on a person’s fingertip and are extremely light, requiring only 1 watt of power to lift off from the ground. Their accompanying cameras and sensors use up an additional half a watt to operate.
“The missing piece is the computers — we can’t fit them in terms of size and power,” Karaman says. “We need to miniaturize the computers and make them low power.”
The group quickly realized that conventional chip design techniques would likely not produce a chip that was small enough and provided the required processing power to intelligently fly a small autonomous drone.
“As transistors have gotten smaller, there have been improvements in efficiency and speed, but that’s slowing down, and now we have to come up with specialized hardware to get improvements in efficiency,” Sze says.
The researchers decided to build a specialized chip from the ground up, developing algorithms to process data, and hardware to carry out that data-processing, in tandem.
This summer, the team will mount the FPGA chip onto a drone to test its performance in flight. Ultimately, the team plans to implement the optimized algorithm on an application-specific integrated circuit, or ASIC, a more specialized hardware platform that allows engineers to design specific types of gates, directly onto the chip.
“We think we can get this down to just a few hundred milliwatts,” Karaman says. “With this platform, we can do all kinds of optimizations, which allows tremendous power savings.”