Table-Top Self Driving With The Pi Zero

Self-driving technologies are a hot button topic right now, as major companies scramble to be the first to market with more capable autonomous vehicles. There’s a high barrier to entry at the top of the game, but that doesn’t mean you can’t tinker at home. [Richard Crowder] has been building a self-driving car at home with the Raspberry Pi Zero.

The self-driving model is trained by first learning from the human driver.

[Richard]’s project is based on the EOgma Neo machine learning library. Using a type of machine learning known as Sparse Predictive Hierarchies, or SPH, the algorithm is first trained with user input. [Richard] trained the model by driving it around a small track. The algorithm takes into account the steering and throttle inputs from the human driver and also monitors the feed from the Raspberry Pi camera. After training the model for a few laps, the car is then ready to drive itself.

Fundamentally, this is working on a much simpler level than a full-sized self-driving car. As the video indicates, the steering angle is predicted based on the grayscale pixel data from the camera feed. The track is very simple and the contrast of the walls to the driving surface makes it easier for the machine learning algorithm to figure out where it should be going. Watching the video feed reminds us of simple line-following robots of years past; this project achieves a similar effect in a completely different way. As it stands, it’s a great learning project on how to work with machine learning systems.

[Richard]’s write-up includes instructions on how to replicate the build, which is great if you’re just starting out with machine learning projects. What’s impressive is that this build achieves what it does with only the horsepower of the minute Raspberry Pi Zero, and putting it all in a package of just 102 grams. We’ve seen similar builds before that rely on much more horsepower – in processing and propulsion.

Make Cars Safer By Making Them Softer

Would making autonomous vehicles softer make them safer?

Alphabet’s self-driving car offshoot, Waymo, feels that may be the case as they were recently granted a patent for vehicles that soften on impact. Sensors would identify an impending collision and adjust ‘tension members’ on the vehicle’s exterior to cushion the blow. These ‘members’ would be corrugated sections or moving panels that absorb the impact alongside the crumpling effect of the vehicle, making adjustments based on the type of obstacle the vehicle is about to strike.

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Ultrasonic Array Gets Range Data Fast and Cheap

How’s your parallel parking? It’s a scenario that many drivers dread to the point of avoidance. But this 360° ultrasonic sensor will put even the most skilled driver to shame, at least those who pilot tiny remote-controlled cars.

Watch the video below a few times and you’ll see that within the limits of the test system, [Dimitris Platis]’ “SonicDisc” sensor does a pretty good job of nailing the parallel parking problem, a driving skill so rare that car companies have spent millions developing vehicles that do it for you. The essential task is good spatial relations, and that’s where SonicDisc comes in. A circular array of eight HC-SR04 ultrasonic sensors hitched to an ATmega328P, the SonicDisc takes advantage of interrupts to make reading the eight sensors as fast as possible. The array can take a complete set of readings every 10 milliseconds, which is fast enough to allow for averaging successive readings to filter out some of the noise that gets returned. Talking to the car’s microcontroller over I2C, the sensor provides a wealth of ranging data that lets the car quickly complete a parallel parking maneuver. And as a bonus, SonicDisc is both open source and cheap to build — about $10 a copy.

Rather use light to get your range data? There are some pretty cheap LIDAR units on the market these days.

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Driverless Lorries To Be Tested On UK Roads by End of 2018

The [BBC] is reporting that driverless semi-trailer trucks or as we call them in the UK driverless Lorries are to be tested on UK roads. A contract has been awarded to the Transport Research Laboratory (TRL) for the trials. Initially the technology will be tested on closed tracks, but these trials are expected to move to major roads by the end of 2018.

All  of these Lorries will be manned and driven in formation of up to three lorries in single file. The lead vehicle will connect to the others wirelessly and control their braking and acceleration. Human drivers will still be present to steer the following lorries in the convoy.

This automation will allow the trucks to drive very close together, reducing drag for the following vehicles to improve fuel efficiency.”Platooning” as they call these convoys has been tested in a number of countries around the world, including the US, Germany, and Japan.

Are these actually autonomous vehicles? This question is folly when looking toward the future of “self-driving”. The transition to robot vehicles will not happen in the blink of an eye, even if the technological barriers were all suddenly solved. That’s because it’s untenable for human drivers to suddenly be on the road with vehicles that don’t have a human brain behind the wheel. These changes will happen incrementally. The lorry tests are akin to networked cruise control. But we can see a path that will add in lane drift warnings, steering correction, and more incremental automation until only the lead vehicle has a person behind the wheel.

There is a lot of interest in the self driving industry right now from the self driving potato to autonomous delivery. We’d love to hear your vision of how automated delivery will sneak its way into our everyday lives. Tell us what you think in the comments below.

Automate the Freight: The Robotic Garbage Man

When I started the Automate the Freight series, my argument was that long before the vaunted day when we’ll be able to kick back and read the news or play a video game while our fully autonomous car whisks us to work, economic forces will dictate that automation will have already penetrated the supply chain. There’s much more money to be saved by carriers like FedEx and UPS cutting humans out of the loop while delivering parcels to homes and businesses than there is for car companies to make by peddling the comfort and convenience of driverless commuting.

But the other end of the supply chain is ripe for automation, too. For every smile-adorned Amazon package delivered, a whole bunch of waste needs to be toted away. Bag after bag of garbage needs to go somewhere else, and at least in the USA, municipalities are usually on the hook for the often nasty job, sometimes maintaining fleets of purpose-built trucks and employing squads of workers to make weekly pickups, or perhaps farming the work out to local contractors.

Either way you slice it, the costs for trash removal fall on the taxpayers, and as cities and towns look for ways to stretch those levies even further, there’s little doubt that automation of the waste stream will start to become more and more attractive. But what will it take to fully automate the waste removal process? And how long before the “garbage man” becomes the “garbage ‘bot”?

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Self Driving Potato Hits the Road

Potatoes deserve to roam the earth, so [Marek Baczynski] created the first self-driving potato, ushering in a new era of potato rights. Potato batteries have been around forever. Anyone who’s played Portal 2 knows that with a copper and zinc electrode, you can get a bit of current out of a potato. Tubers have been powering clocks for decades in science classrooms around the world. It’s time for something — revolutionary.

[Marek] knew that powering a timepiece wasn’t enough for his potato, so he picked up a Texas Instruments BQ25504 boost converter energy harvesting chip. A potato can output around 0.4 V at 0.6 mA. The 25504 uses this power to slowly charge a capacitor. Every fifteen minutes or so, enough energy is stored to power a motor for a short time. [Marek] built a car for his potato — or more fittingly, he built his potato into a car.

The starch-powered capacitor moves the potato car about 8 cm per cycle. Over the course of a day, the potato can travel around 7.5 meters. Not very far, but hey, that’s further than the average potato travels on its own power. Of course, any traveling potato needs a name, so [Marek] dubbed his new pet “Pontus”. Check out the video after the break to see the ultimate fate of poor Pontus.

Now that potatoes are mobile, we’re going to need a potato detection system. Humanity’s only hope is to fight fire with fire – break out the potato cannons!

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Self-Driving RC Cars with TensorFlow; Raspberry Pi or MacBook Onboard

You might think that you do not have what it takes to build a self-driving car, but you’re wrong. The mistake you’ve made is assuming that you’ll be controlling a two-ton death machine. Instead, you can give it a shot without the danger and on a relatively light budget. [Otavio] and [Will] got into self-driving vehicles using radio controlled (RC) cars.

[Otavio] slapped a MacBook Pro on an RC car to do the heavy lifting and called it carputer. The computer reads Hall effect sensor data from the motor to establish distance traveled (this can be used to calculate speed) and watches the stream from a webcam perched on the chassis. These two sources are fed into a neural network using TensorFlow. You train the system by driving the vehicle manually through the course a few times and then let it drive itself.

In the video interview below, you get a look at the car and [Otavio] gives commentary on how the system works as we see playback of a few races, including the Sparkfun 2016 Autonomous Vehicle Competition. I apologize for the poor audio, they lost the booth lottery and were next door to an incredibly noisy robot band (video proof) so we were basically shouting at each other. But I think you’ll agree it’s worth it to get a look at the races. Continue reading “Self-Driving RC Cars with TensorFlow; Raspberry Pi or MacBook Onboard”