Swarming robots hold the potential to solve a range of real-world problems, from <a href="https://www.wevolver.com/article/dlr-measures-flow-phenomena-around-wind-turbines-with-a-swarm-of-drones" target="_blank">analyzing airflow around wind turbines</a> and improving agricultural data gathering to monitoring the weather or exploring space. There’s only one problem: Research into robotic swarms can be an expensive endeavor.That’s the problem HeRo 2.0 aims to solve. The creation of a quartet of roboticists from Brazil, HeRo 2.0 — an upgraded, streamlined version of the earlier HeRo platform from the same team — offers a low-cost compact robotics platform for swarm research and education based on off-the-shelf components and an easily-assembled 3D-printed chassis. Better still: The whole project is open source.<h1>Miniature marvels</h1>“The robotic platform presented in this work takes advantage of the recent technological advancements to use mass-produced components that are smaller, affordable, and long-term available,” the team explains of the robot. “In addition, the design and assembly process follow new trends, such as the Maker Movement and Do It Yourself, which allow others to reproduce and customize the robot using additive manufacturing.”The team set about designing HeRo 2.0 with a view to six key design principles: The robot should be as inexpensive as possible, to allow for larger swarms to be built without great expense; each individual robot should be small yet include on-board sensing capabilities and enough power for hours of autonomous operation; it should be highly tolerant to faults; communication between robots should be scalable to a large number of individuals; the robots should be easy to assemble and use only readily-available components; and they should be easy to program using existing development frameworks.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQ5Mjc1NTczODU4LWhlcm8yLTIuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib" alt="A diagram of the HeRo 2.0 robotics platform, with the following parts labelled from the top: e-Hat, PCB board, cover, rotary encoder, battery, pinion gear encoder, rubber O-ring, servo motor SG90, castor wheel, motor chassis, board chassis, motor gear, spur gear wheel.">Built using off-the-shelf components in a 3D-printed chassis, HeRo 2.0 costs just $18 yet includes all the key functionality required for basic swarm research.The final design, which first author Paulo Rezeck and colleagues say meets all six of the requirements, is certainly low cost: The full bill of materials for each unit comes out at just $18.72, including $0.10 for a 38mm rubber O-ring and a battery powerful enough to push the robot for &nbsp;three hours of active experimentation — and that's a cost which could drop in large-volume production.The bargain-basement price comes courtesy of some unusual design choices, such as turning to a low-cost rotary encoder usually used to track the movement of the scroll wheel on a computer mouse to instead track the turning of the wheels. Continuous servo motors supporting simple control via the pulse-width modulation (PWM) output of a microcontroller further reduce costs, while the 3D-printable chassis uses just a little plastic and some electricity to be produced.<iframe src="https://www.youtube.com/embed/hAS7FKYkKWQ?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" allowfullscreen="" class="fr-draggable" width="640" height="360" frameborder="0" data-gtm-yt-inspected-30="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-37="true" id="946569574" title="HeRo 2.0: Cooperative Transportation"></iframe> The microcontroller, too, was picked for its high performance at a low price: An Espressif ESP-12E, based on the ESP8266 and boasting a single 32-bit Tensilica LX106 core running at speeds of up to 160MHz plus 4MB of flash memory. To this, the team added eight infrared sensors — officially limited to measuring distances no larger than 2cm (around 0.8"), but extended through clever operation to 30cm (11.8"). Further expansion, meanwhile, is possible through what the team calls “e-Hats,” add-on modules which attach to the top of the robot to provide additional functionality — such as, the team has proven in prototype, a display screen or an inertial measurement unit (IMU).<h1>Programming perfection</h1>On the software side, the HeRo 2.0 platform offers a choice of programming paradigm. Its firmware was developed on the Arduino integrated development environment (IDE) using the Wiring language, an extended variant of C designed for embedded work. The “rosserial” framework was added to provide compatibility with the Robot Operating System (ROS), a popular platform for robotics work.As a result, the robots can be programmed in two ways. For direct programming, a new firmware can be written in the Arduino IDE and flashed to the robots over a Wi-Fi connection using the Firmware Over the Air (FOTA) protocol, allowing algorithms to run directly on the robots’ microcontrollers. Otherwise, the stock firmware’s ROS support can be leveraged to place the swarm under control of a centralized server communicating via ROS messages.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQ5Mjc1NjM0MTQyLWhlcm8yLTQuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib" alt="A screenshot of the RViz software showing a map generated by a HeRo 2.0 robot; its path can be seen as a continuous line surrounded by data from its on-board distance sensors.">The researchers put the robot through a range of experimental tasks, including mapping, flocking, and collaboration on an object-transportation task.“This mode is very convenient and scalable in the early stages of testing with multiple robots,” the team notes of the server-based ROS programming mode. “Furthermore, using the ROS framework for implementation, we have a series of tools and may use different programming languages.”To prove the overall performance of the platform, the team set about comparing its capabilities to the E-puck — a highly popular commercial robot which costs $975 per unit, making it an order of magnitude more expensive than the HeRo 2.0 platform albeit with additional functionality including Bluetooth connectivity, additional sensors, and an on-board camera.In all tests the HeRo 2.0 proved capable of keeping up with its commercial rival, though with one caveat: While testing functionality like flocking and cooperative transportation, the team extended the robots’ on-board sensing capabilities with an overhead camera and the Apriltag tracking algorithm — to, as the team puts it, “emulate a sensor in the robot that captures its neighbors’ position and relative velocity” which is lacking in the hardware as-implemented.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQ5Mjc1NjQ3MDQ4LWhlcm8yLTEuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib" alt="Four photographs of a HeRo 2.0 prototype shown from the top with its circuit board and distance sensors, from underneath with its wheels and castors visible, from the front, and from the side.">The compact HeRo 2.0 shows real promise for lowering the barrier to entry for swarm research, thanks to accessible development tools and its extremely low cost. There’s room for improvement elsewhere, too. Proposed enhancements include a migration to the newer ROS 2 platform on the software side, the development of additional e-Hat add-ons in addition to the display and IMU units already prototyped, improvements to the internal filters for localization, and investigations in how to reduce variability of the 3D-printed parts in order to reduce post-processing and thus streamline assembly. More studies are also required to see how the low-cost parts stand up to repeated use — the rotary encoders in particular requiring replacement “after months of use.”The team’s work has been submitted to the journal <cite>Autonomous Robots</cite>, with a preprint available <a href="https://arxiv.org/abs/2202.12391" target="_blank">on Cornell’s arXiv server</a>.<h1>Reference</h1>Paulo Rezeck, Hector Azpurua, Mauricio FS Correa, and Luiz Chaimowicz: <cite>HeRo 2.0: A Low-Cost Robot for Swarm Robotics Research</cite>. DOI <a href="https://arxiv.org/abs/2202.12391" target="_blank">arXiv:2202.12391</a> <a href="http://cs.RO" target="_blank"></a>[cs.RO].

Costing just $18 each, the HeRo 2.0 robot platform includes expandability via "e-Hat" add-on hardware installed on the top.

Designed using off-the-shelf parts, a low-cost microcontroller, and a 3D-printed chassis, the HeRo 2.0 robot costs just $18 to build — and is at the heart of an effort to open swarm robotics up to a broader audience.

HeRo 2.0, an ultra-low cost 3D-printed robotics platform, could open swarm robotics experimentation up to all

The current record for a bicycle travelling across flat road is 133.78 km/h, set in 2012 by a Dutch team at the World Human Powered Speed Challenge, which takes place every year in the Nevada desert. But last September, a team from IUT Annecy aimed to beat that record. The team used artificial-intelligence-based software developed by <a href="https://www.neuralconcept.com/" rel="noopener noreferrer" target="_blank">Neural Concept</a>, to boost the performance of its bike. In just a few minutes, Neural Concept’s technology can calculate the optimal shape of a bike to make it as aerodynamic as possible. It can also be used for aerodynamics calculations in a number of other applications. The company is presenting its software in Stockholm today at the International Conference on Machine Learning.<iframe src="https://www.youtube.com/embed/9u6EyLiytJk?&amp;wmode=opaque" allowfullscreen="" class="fr-draggable" style="width: 771px; height: 437px;" frameborder="0"></iframe>From the outside, the IUT Annecy team’s recumbent bike looks more like a tiny racecar than a human-powered bicycle. It was custom-made to fit closely to the cyclist’s body. During the Challenge, he will have to ride down a 200-meter stretch of straight, flat road as fast as possible, after a run-up of 8 km. The design objective clearly isn’t cyclist comfort, but making the most out of every inch of the vehicle.Coming up with faster, more detailed and more effective designsExisting aerodynamic design methods require an enormous amount of computing power. Traditionally bicycle engineers think up different forms and then test them using computer simulation. But here, for the first time, engineers employed optimization software – rather than their own intuition – to define the recumbent bike fairing. The IUT Annecy team used Neural Concept’s software, specifying the bike’s maximum length and width and the space needed for the drivetrain and wheels. The program then sorted through all kinds of shapes, quickly comparing them in order to come up with the best one. For instance, the program helped the engineers determine the best location for the vehicle’s maximum width.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1562770461847-1920x1080.jpg" style="width: 766px;" class="fr-fic fr-dib">Pierre Baqué, CEO of Neural Concept © 2018 Alain Herzog&nbsp;To develop the technology behind the software, researchers at EPFL’s <a href="https://cvlab.epfl.ch/" rel="noopener noreferrer" target="_blank">Computer Vision Laboratory</a> trained a convolutional neural network to calculate the aerodynamic properties of various forms represented by generic polygon meshes, which are collections of points used to generate 3D shapes. This type of artificial intelligence works by running through several layers, categorizing information from the simplest to the most complex. In the initial layers, the program identifies a shape’s contours; then it assigns the contours to an object and determines what category the object belongs to based on the expected outcome.Engineers can use the software to carry out detailed analyses of different designs more rapidly and with better accuracy. “Our program results in designs that are sometimes 5–20% more aerodynamic than conventional methods. But even more importantly, it can be used in certain situations that conventional methods can’t,” says Pierre Baqué, CEO of Neural Concept. Another benefit is that the software can compare designs without human bias. “The shapes used in training the program can be very different from the standard shapes for a given object. That gives it a great deal of flexibility,” adds Baqué.The World Human Powered Speed Challenge is a competition involving bicycles designed by teams of university students. The Challenge is a real-world test for both the IUT Annecy team and Neural Concept’s machine-learning technology. The software has myriad other potential applications too, such as for designing drones, wind turbines and aircraft. Other industry professionals clearly see its potential – Baqué has been invited to speak at the world’s biggest machine learning conference today in Stockholm. IUT Annecy and Neural Concept have already started working on the bike for next year’s race. It will be designed exclusively and entirely by the software, without any human intervention.

Thanks to software developed bicycle engineers can quickly calculate the most aerodynamic shape for a bike. The software applies artificial intelligence to a set of user-defined specifications. Engineers have used the program to design a bike that they hope will break the world speed record.

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HeRo 2.0, an ultra-low cost 3D-printed robotics platform, could open swarm robotics experimentation up to all

Artificial intelligence helps design an ultra-aerodynamic bike

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