Self-Driving RC Truck Is A Master’s Thesis In Cybernetics And Robotics

RC cars are a fun pastime, but for many hackers, taking things to the next level involves making the cars drive themselves. For his Masters thesis, [Jon] did just that, building a self-driving robot truck that confidently cruises the floor of his laboratory.

The truck is based on a 1/14th scale Tamiya chassis, and had been fitted out by a prior group with an inductive charging system. On top of this platform, [Jon] added a Jetson TX2 to act as the brains of the system, hooking it up with a Slamtec RPLIDAR scanner to map its surrounding environment. There’s also a Teensy microcontroller onboard which handles synthesizing PWM signals for the radio control hardware that drives the truck, and a Logitech webcam up front for machine vision. The truck is capable of operating in a variety of modes, from full manual operation, to driving based on LIDAR mapping or with an AI controlling the truck based on camera data. The truck is programmed to drive a route including an inductive charging pad so it can keep its power levels up without human intervention.

It’s a great blueprint for a self-driving system, and [Jon]’s thesis goes into great detail on how everything works at the base level (available on this page as a 67 MB PDF). His Code is on Github for the curious. We’ve seen similar projects before too, like this robot that navigates its builder’s house using LIDAR. Video after the break.

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But Can Your AI Recognize Slugs?

The common garden slug is a mystery. Observing these creatures as they slowly emerge from their slimy lairs each evening, it’s hard to imagine how much damage they can do. With paradoxical speed, they can mow down row after row of tender seedlings, leaving nothing but misery in their mucusy wake.

To combat this slug menace, [Tegwyn☠Twmffat] (the [☠] is silent) is developing this AI-powered slug busting system. The squeamish or those challenged by the ethics of slug eradication can relax: no slugs have been harmed yet. So far [Tegwyn] has concentrated on the detection of slugs, a considerably non-trivial problem since there are few AI models that are already trained for slugs.

So far, [Tegwyn] has acquired 5,712 images of slugs in their natural environment – no mean feat as they only come out at night, they blend into their background, and their slimy surface makes for challenging reflections. The video below shows moderate success of the trained model using a static image of a slug; it also gives a glimpse at the hardware used, which includes an Nvidia Jetson TX2. [Tegwyn] plans to capture even more images to refine the model and boost it up from the 50 to 60% confidence level to something that will allow for the remediation phase of the project, which apparently involves lasers. Although he’s willing to entertain other methods of disposal; perhaps a salt-shooting turret gun?

This isn’t the first garden-tending project [Tegwyn] has tackled. You may recall The Weedinator, his 2018 Hackaday Prize entry. This slug buster is one of his entries for the 2019 Hackaday Prize, which was just announced. We’re looking forward to seeing the onslaught of cool new projects everyone will be coming up with.

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Hands-On Nvidia Jetson TX2: Fast Processing For Embedded Devices

The review embargo is finally over and we can share what we found in the Nvidia Jetson TX2. It’s fast. It’s very fast. While the intended use for the TX2 may be a bit niche for someone building one-off prototypes, there’s a lot of promise here for some very interesting applications.

Last week, Nvidia announced the Jetson TX2, a high-performance single board computer designed to be the brains of self-driving cars, selfie-snapping drones, Alexa-like bots for the privacy-minded, and other applications that require a lot of processing on a significant power budget.

This is the follow-up to the Nvidia Jetson TX1. Since the release of the TX1, Nvidia has made some great strides. Now we have Pascal GPUs, and there’s never been a better time to buy a graphics card. Deep learning is a hot topic that every new CS grad wants to get into, and that means racks filled with GPUs and CUDA cores. The Jetson TX1 and TX2 are Nvidia’s strike at embedded deep learningor devices that need a lot of processing power without sucking batteries dry.

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Nvidia Announces Jetson TX2 High Performance Embedded Module

The last year has been great for Nvidia hardware. Nvidia released a graphics card using the Pascal architecture, 1080s are heating up server rooms the world over, and now Nvidia is making yet another move at high-performance, low-power computing. Today, Nvidia announced the Jetson TX2, a credit-card sized module that brings deep learning to the embedded world.

The Jetson TX2 is the follow up to the Jetson TX1. We took a look at it when it was released at the end of 2015, and the feelings were positive with a few caveats. The TX1 is still a very fast, very capable, very low power ARM device that runs Linux. It’s low power, too. The case Nvidia was trying to make for the TX1 wasn’t well communicated, though. This is ultimately a device you attach several cameras to and run OpenCV. This is a machine learning module. Now it appears Nvidia has the sales pitch for their embedded platform down.

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