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A $ 250 Raspberry Pi based robot dog. Mini Pupper can do much more than a regular toy

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It looks like the fashion for robops has come to the world. The Spot model from Boston Dynamics has been around for a long time, recently joined by fellow CyberDog from Xiaomi. Now the Mini Pupper project has appeared on Kickstarter.

This is also a robot dog, only very small and cheap. You can buy a baby as part of a crowdfunding campaign at a price of $ 255 or more.

Mini Pupper is a small robot with a Raspberry Pi 4 at its heart. It is based on the open source Stanford Pupper platform. The robot is running Ubuntu or ROS.

Despite its modest size, low-performance platform and low price, this is not just a toy. The robot supports SLAM (Simultaneous Localization and Mapping), that is, it is able to simultaneously display its environment and learn in real time. For this he has a camera and lidar. The robot is capable of building maps of premises and supports navigation. Mini Pupper also supports the OpenCV 3D camera module, which allows the robot to get hold of face recognition systems, gestures, object detection and so on.

And the robot is also able to imitate various gaits, which looks very funny with such dimensions. By the way, it has 12 degrees of freedom against eight for many similar solutions of this class. The robot also has a front display that allows you to display various animations. And, of course, you can configure them yourself.

A $ 250 Raspberry Pi based robot dog.  Mini Pupper can do a lot more than a regular toy

The robot is available in various configurations. The basic one for $ 255 is a fully disassembled robot and without the Raspberry Pi 4 included. In order to assemble such a kit, you will need a single board PC and even a 3D printer. The full set within the campaign is offered for a minimum of $ 480, and in retail it will cost $ 800.

The dimensions of the baby are 209 x 109 x 165 mm with a weight of 560 g. The robot is equipped with a pair of 1600 mAh batteries. The campaign has already raised more than $ 190,000 against the requested $ 10,000. Deliveries are expected to begin in November.

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TSMC presented the N4P process technology

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TSMC introduced the N4P process, a performance-optimized enhancement to the 5nm technology platform. New process technology added to N5, N4, N3. According to the manufacturer, the choice between them gives customers the opportunity to choose the most suitable combination of power consumption, performance, die area and product cost.

The new process technology is the third major improvement on TSMC’s 5nm platform. The company estimates that N4P delivers an 11% increase in productivity over the original N5 technology and 6% over N4. Compared to N5, N4P will also provide 22% more energy efficiency and 6% more transistor density. In addition, N4P reduces process complexity and shorter insert cycle times by reducing the number of masks.

N4P projects are said to be well supported by TSMC’s comprehensive design ecosystem. The first products based on N4P technology are expected to go into production by the second half of 2022.

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Xturismo Flying Motorcycle Orders Started in Japan

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The Japanese company ALI Technologies has presented the “flying motorcycle” Xturismo, capable of speeds up to 100 km / h and stay in the air for up to 40 minutes. During the demonstration, the device rose several meters above the ground and flew for about one and a half minutes. The weight of the device, designed to carry one person, is 300 kg.

ALI Technologies, backed by Kyocera and Mitsui Sumitomo Insurance Venture Capital, has already started accepting orders and plans to begin shipping the first batch of 200 pieces in the first half of next year. The Xturismo is priced at approximately $ 682,000.

Xturismo Flying Motorcycle Orders Started in Japan

Interestingly, in the Xturismo, the main propulsion system does not use electric motors, but an internal combustion engine that drives the two main propellers. Auxiliary propellers help maintain balance and perform maneuvers. To do this, the “motorcycle” uses technology tested by ALI in drones.

One of the applications of the apparatus is called rescue operations on the water. True, it is not clear how exactly the assistance is supposed to be provided – neither the carrying capacity, nor the cruising range, nor the lack of the possibility of landing on water contribute to such an application, not to mention the fact that for aircraft operated over water, it is mandatory to reserve engines with the ability to continue flight if one of them fails.

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NASA: You Can Help Teach Rovers To Explore Mars Better

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The US Aeronautics and Space Administration NASA has published a note with the intriguing headline “You Can Help Teach NASA Mars Rovers to Explore Mars Better.”

As the department explains, artificial intelligence has tremendous potential to change the way spacecraft study the universe. But since all machine learning algorithms require human training, NASA is asking members of the public to help identify elements of scientific interest in images taken by the Perseverance rover.

The project, called AI4Mars, is a follow-up to a project launched last year based on imagery from the Curiosity rover.

NASA: You Can Help Teach Rovers To Explore Mars Better

Early contributors to this project mapped out nearly half a million images, using a tool to highlight objects such as sand and rock that NASA’s Jet Propulsion Laboratory (JPL) Mars rover drivers typically look at when planning routes on the Red Planet. The end result was an algorithm called SPOC (Classification of Soil and Object Properties) that could correctly identify these features in almost 98% of cases.

The SPOC algorithm is still in development, and NASA hopes that one day it can be sent to Mars aboard a spacecraft of the future that can do more autonomous driving than Perseverance’s AutoNav technology allows.

AI4Mars now provides additional markers to indicate more precise details, allowing people to choose options such as moving rocks or nodules (globular mineral formations).

The goal is to hone an algorithm that could help a future rover pick “needles out of a haystack” of data sent from Mars.

Equipped with 19 cameras, Perseverance sends tens to hundreds of images to Earth every day for scientists and engineers to view specific geologic features. But time is running out: after these images have traveled millions of kilometers from Mars to Earth, NASA team members have hours to develop the next set of instructions, based on what they see in these images, to send to Perseverance.

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