The summer, and Louis Wood’s internship with our Maker in Residence, was creeping to a close without his final build making it off the ground. But as if by magic, on his very last day, Louis got his handmade drone flying.

3D-printed CAD design

The journey of building a custom drone began with designing in CAD software. My initial design was fully 3D-printed with an enclosed structure and cantilevered arms to support point forces. The honeycomb lid provided cooling, and the enclosure allowed for embedded XT-60 and MR-30 connections, creating a clean and integrated look. Inside, I ensured all electrical components were rigidly mounted to avoid unwanted movement that could destabilise the flight.

Testing quickly revealed that 3D-printed frames were brittle, often breaking during crashes. Moreover, the limitations of my printer’s build area meant that motor placement was cramped. To overcome these issues, I CNC-routed a new frame from 4 mm carbon fibre, increasing the wheelbase for better stability. Using Carveco software, I generated toolpaths and cut the frame on a WorkBee CNC in our Maker Lab. After two hours, I had a sturdy, assembled frame ready for electronics.

Not one, not two, but three Raspberry Pis

For the drone’s brain, I used a Raspberry Pi Pico 2 connected to an MPU6050 gyroscope for real-time orientation data and an IBUS protocol receiver for streamlined control inputs. Initially, I faced issues with signal processing due to the delay of handling five separate PWM signals. Switching to IBUS sped up the loop frequency by tenfold, which greatly improved flight response. The Pico handled PID (Proportional-Integral-Derivative) calculations for stability, and a 4-in-1 ESC managed the motor signals. The drone also carries a Raspberry Pi Zero with a Camera Module 2 and an analogue VTX for real-time FPV (first-person view) flying.

Programming was based on Tim Hanewich’s Scout flight controller code, implementing a ‘rate’ mode controller that uses PID values to maintain desired angular velocities. Fine-tuning the PID gains was essential; improper settings could lead to instability and dangerous oscillations. I followed a careful tuning process, starting with low values for each parameter and slowly increasing them.

To make the process safer, I constructed a testing rig to isolate each axis and simulate flight conditions. This allowed me to achieve a rough tune before moving on to actual flight tests, ultimately ensuring the drone’s safe and stable performance.

The post Building a Raspberry Pi Pico 2-powered drone from scratch appeared first on Raspberry Pi.

AI models are adept at distinguishing one winged creature from another. This #MagPiMonday, Rosie Hattersley goes beyond the buzz.

Fun fact that might get you a point in the local pub quiz: Vespa, Piaggio’s iconic scooter, is Italian for wasp, which its buzzing engine sounds a bit like. Less fun fact: nature’s counterpart to the speedy two-wheeler has an aggressive variant that has been seen in increasing numbers across western Europe and which is a direct threat to bees, which are one of their key food sources. Bees are great for biodiversity; Asian hornets (the largest type of eusocial wasp) are not. But it’s only particular hornet species that pose such a threat. Most citizen reports of Asian hornets are native species, and a key issue is ensuring that existing hornet species are not being destroyed on this mistaken assumption. To combat misinformation and alarm at the so-called ‘killer’ hornet (itself a subset of wasp), academics at the University of Exeter have developed a VespAI detector that presents a positive identification system showing where new colonies of the invasive hornet Vespa velutina nigrithorax have begun to spread. The system works by drawing the insects to a pad that is impregnated with tasty (to wasps) smelling foodstuffs.

Considerate response

VespAI provides a nonharmful alternative to traditional trapping surveys and can also be used for monitoring hornet behaviour and mapping distributions of both the Asian hornet (Vespa velutina) and European hornet (Vespa crabro), which is protected in some countries. “Live hornets can be caught and tracked back to the nest, which is the only effective way to destroy them,” explains the team’s research paper.

Creepy feeling

VespAI features a camera positioned above a bait station that detects insects as they land to feed and gets to work establishing whether the curious mite is, in fact, an Asian hornet. The Exeter team developed the AI algorithm in Python, using YOLO image detection models. These identify whether Asian hornets are present and, if so, send an alert to users. Raspberry Pi proved a great choice because of its compact size, ability to run the hornet recognition algorithm, real-time clock, and support for peripherals such as an external battery. The prototype bait station design was made with items that the team had at hand in their lab, including a squirrel baffle for the weather shield, Petri dishes and sponges to hold hornet attractant, and a beehive stand for the monitor to rest on.

Design challenges included optimising the hornet detection algorithm for use on Raspberry Pi. “An AI algorithm may work well during training or when validated in the lab. However, field deployment is essential to expose it to potentially unforeseen scenarios that may return errors”, they note. The project also involved developing a monitor with an integrated camera, processor, and peripherals while minimising power consumption. To this end, the VespAI team is currently optimising their software to run on Raspberry Pi Zero, having watched footage of the AntVideoRecord device monitoring leafcutter ant (Acromyrmex lundi) foraging trails and been impressed by its ability to run for extended periods remotely due to its low power consumption.

As this interactive map shows, Asian hornets have quickly made inroads across Western Europe.

The Raspberry Pi-enabled setup is “intended to support national surveillance efforts, thus limiting hornet incursions into new regions,” explains Dr Thomas O’Shea-Wheller, a research fellow in the university’s Environment and Sustainability Institute. He and his colleagues have been working on the AI project since 2022, conducting additional fieldwork this summer with the National Bee Unit and the Government of Jersey (Channel Islands) mapping new locations and fine-tuning its accessibility to potential users ahead of a planned commercial version. 

Given Raspberry Pi’s extensive and enthusiastic users, they hope sharing their code on GitHub will help expand the number of VespAI detection stations and improve surveillance and reporting of hornet species.

This article originally featured in issue 146 of The MagPi magazine.

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