Biodesign by Design: A Look at Soft Robotics

Mouser examines a human-centric approach to robotics. Soft Robotic designs are less metal and mechanical and utilize gentle, compliant mechanisms and actuators built using fluids and flexible materials. These enable a wide range of motion and are suitable for exoskeletons or wearables.

Silicon APDs are Optimized for LIDAR Applications

The Series 9 from First Sensor offers a wide range of silicon avalanche photodiodes (APDs) with very high sensitivity in the near infrared (NIR) wavelength range, especially at 905 nm. With their internal gain mechanism, large dynamic range and fast rise time the APDs are ideal for LIDAR systems for optical distance measurement and object recognition according to the time of flight method. Application examples include driver assistance systems, drones, safety laser scanners, 3D-mapping and robotics.

The Series 9 offers detectors as single elements as well as linear and matrix arrays with multiple sensing elements. The package options include rugged TO housings or flat ceramic SMD packages. The slow increase of the gain of the Series 9 photodiodes with the applied reverse bias voltage allows for easy and precise adjustments of high gain factors. For particularly low light levels, hybrid solutions are also available that further enhance the APD signal with an internal transimpedance amplifier (TIA). The integrated amplifier is optimally matched to the photodiode and allows compact setups as well as very large signal-to-noise ratios.

Using its own semiconductor manufacturing facility and extensive development capabilities, First Sensor can adapt its silicon avalanche photodiodes to specific customer requirements, such as sensitivity, gain, rise time or design.

Important features of the Series 9 APDs:

  • Very high sensitivity at 905 nm
  • Large dynamic range and fast rise time
  • Single element photodiodes as well as linear and matrix arrays
  • Rugged TO housings or flat ceramic SMD packages
  • Hybrid solutions with integrated TIA

First Sensor | www.first-sensor.com

Collaborative Robotics

In this issue of the Empowering Innovation Together ebook, Mouser takes an in-depth look at Collaborative Robotics. We examine “cobots” in industrial environments, motor control in complex robotics and design considerations. We also look at how robots and humans interact and coexist with each other as they work.

Generation Robot

Robots may be as disruptive as the automobile, causing us to rethink how we live and work. In Mouser’s 5-part Empowering Innovation Together video series, Grant Imahara departs on another technical journey to explore the frontier of robotics, meeting with researchers, engineers and visionaries shaping the robotic future.

March Circuit Cellar: Sneak Preview

The March issue of Circuit Cellar magazine is coming soon. And we’ve got a healthy serving of embedded electronics articles for you. Here’s a sneak peak.

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TECHNOLOGY FOR THE INTERNET-OF-THINGS

IoT: From Device to Gateway
The Internet of Things (IoT) is one of the most dynamic areas of embedded systems design today. This feature focuses on the technologies and products from edge IoT devices up to IoT gateways. Circuit Cellar Chief Editor Jeff Child examines the wireless technologies, sensors, edge devices and IoT gateway technologies at the center of this phenomenon.

Texting and IoT Embedded Devices
Texting has become a huge part of our daily lives. But can texting be leveraged for use in IoT Wi-Fi devices? Jeff Bachiochi lays the groundwork for describing a project that will involve texting. In this part, he gets into out the details for getting started with a look at Espressif System’s ESP8266EX SoC.

Exploring the ESP32’s Peripheral Blocks
What makes an embedded processor suitable as an IoT or home control device? Wi-Fi support is just part of the picture. Brian Millier has done some Wi-Fi projects using the ESP32, so here he shares his insights about the peripherals on the ESP32 and why they’re so powerful.

MICROCONTROLLERS HERE, THERE & EVERYWHERE

Designing a Home Cleaning Robot (Part 4)
In this final part of his four-part article series about building a home cleaning robot, Nishant Mittal discusses the firmware part of the system and gets into the system’s actual operation. The robot is based on Cypress Semiconductor’s PSoC microcontroller.

Apartment Entry System Uses PIC32
Learn how a Cornell undergraduate built a system that enables an apartment resident to enter when keys are lost or to grant access to a guest when there’s no one home. The system consists of a microphone connected to a Microchip PIC32 MCU that controls a push solenoid to actuate the unlock button.

Posture Corrector Leverages Bluetooth
Learn how these Cornell students built a posture corrector that helps remind you to sit up straight. Using vibration and visual cues, this wearable device is paired with a phone app and makes use of Bluetooth and Microchip PIC32 technology.

INTERACTING WITH THE ANALOG WORLD

Product Focus: ADCs and DACs
Makers of analog ICs are constantly evolving their DAC and ADC chips pushing the barriers of resolution and speeds. This new Product Focus section updates readers on this technology and provides a product album of representative ADC and DAC products.

Stepper Motor Waveforms
Using inexpensive microcontrollers, motor drivers, stepper motors and other hardware, columnist Ed Nisley built himself a Computer Numeric Control (CNC) machines. In this article Ed examines how the CNC’s stepper motors perform, then pushes one well beyond its normal limits.

Measuring Acceleration
Sensors are a fundamental part of what make smart machines smart. And accelerometers are one of the most important of these. In this article, George Novacek examines the principles behind accelerometers and how the technology works.

SOFTWARE TOOLS AND PROTOTYPING

Trace and Code Coverage Tools
Today it’s not uncommon for embedded devices to have millions of lines of software code. Trace and code coverage tools have kept pace with these demands making it easier for embedded developers to analyze, debug and verify complex embedded software. Circuit Cellar Chief Editor Jeff Child explores the latest technology trends and product developments in trace and code coverage tools.

Manual Pick-n-Place Assembly Helper
Prototyping embedded systems is an important part of the development cycle. In this article, Colin O’Flynn presents an open-source tool that helps you assemble prototype devices by making the placement process even easier.

Designing a Home Cleaning Robot (Part 2)

Part 2: Mechanical Design

Continuing with this four-part article series about building a home cleaning robot, Nishant and Jesudasan discuss the mechanical aspects of the design.

By Nishant Mittal and Jesudasan Moses
Cypress Semiconductor

In part one (Circuit Cellar 329, December 2017) of this home cleaning robot article series, I discussed the introduction to the concepts of cleaning robots and the crucial design elements that are part of a skeleton design. Apart from that I discussed various selection criteria of the components. In this part, with the help of my colleague Jesudasan Moses, I’ll explore the mechanical aspects of the design. This includes selecting materials, aligning all the components on base, designing the pulleys for optimal performance, selecting motors and so on. The mechanical design for such a system can be very challenging because it’s a moving system and that adds complexity to the process. While this part is focused on mechanical issues and making the base ready, all this paves the way for when we add the “brains” into the system in part three.

DESIGN ELEMENTS

Figure 1 shows the block diagram of the mechanical design for this project. The overall structure of this design requires a base that is strong, but not too heavy. Using a metal base isn’t a good option for this type of system because it would increase the overall weight. Such an increase might mean that a higher torque motor would be required. The next elements are the motors and wheels. We chose to include motors only in the back. Using a front motor would probably be an overdesign for such a system. If you examine professionally designed home cleaning robots—like those I covered in part one—all of them had only the back motors for movement.

Figure 1
Mechanical arrangement of the home cleaning robot

On the front side of the unit, only rollers are added. This gives the system a complete 360-degree freedom of movement. The most important parts of the system are the cleaner and the roller. These are placed toward the center of the system and are controlled using an arrangement of motors and pulleys. In the front of the system, side brushes are added that again are controlled using motors. Now let’s look at the selection of each of the design elements.

Selection of the base shape: The base shape selection is very important because it defines how efficiently your home cleaning robot can clean at corners. A circular base shape is the most recommended option. A circular base enables the robot to move around corners and thereby cover each and every part of the house. That said, for a hobby project like this one, a rectangular base means no advanced tools are needed to cut and shape the base. With that in mind, we chose to use an acrylic material in a square shape for the base.

Motor selection: For our design, we opted for two movement motors on the back of the unit and another motor at the back for the roller pulley. On the front, there are two more motors to move the side brushes. We’ll save the more technical discussion about motor selection in part three. Choice of motor size depends upon the total weight that the front and back need to handle. The total weight should be equalized, otherwise the system won’t remain stable when the robot is moving fast. The placement of the two movement motors should be aligned to their center of axis. That ensures that when the robot is moving straight, it won’t divert its direction. It’s also important to buy those two motors from the same vendor to make sure they share the same mechanical properties.

Wheel Selection: It’s very important to decide on the net height of the system early on. Wheel selection is the deciding factor for the net height. .

Read the full article in the January 330 issue of Circuit Cellar

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Note: We’ve made the October 2017 issue of Circuit Cellar available as a free sample issue. In it, you’ll find a rich variety of the kinds of articles and information that exemplify a typical issue of the current magazine.

January Circuit Cellar: Sneak Preview

The January issue of Circuit Cellar magazine is coming soon. And it’s got a robust selection of embedded electronics articles for you. Here’s a sneak peak.

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                                     IMPROVING EMBEDDED SYSTEM DESIGNS

Special Feature: Powering Commercial Drones
The amount of power a commercial drone can draw on has a direct effect on how long it can stay flying as well as on what tasks it can perform. Circuit Cellar Chief Editor Jeff Child examines solar cells, fuel cells and other technology options for powering commercial drones.

CC 330 CoverFPGA Design: A Fresh Take
Although FPGAs are well established technology, many embedded systems developers—particularly those used the microcontroller realm—have never used them before. In this article, Faiz Rahman takes a fresh look a FPGAs for those new to designing them into their embedded systems.

Product Focus: COM Express boards
COM Express boards provide a complete computing core that can be upgraded when needed, leaving the application-specific I/O on the baseboard. This brand new Product Focus section updates readers on this technology and provides a product album of representative COM Express products.

TESTING, TESTING, 1, 2, 3

LF Resonator Filter
In Ed Nisley’s November column he described how an Arduino-based tester automatically measures a resonator’s frequency response to produce data defining its electrical parameters. This time he examines the resultsand explains a tester modification to measure the resonator’s response with a variable series capacitance.

Technology Spotlight: 5G Technology and Testing
The technologies that are enabling 5G communications are creating new challenges for embedded system developers. Circuit Cellar Chief Editor Jeff Child explores the latest digital and analog ICs aimed at 5G and at the test equipment designed to work with 5G technology.

                                     MICROCONTROLLERS IN EVERYTHING

MCU-based Platform Stabilizer
Using an Inertial Measurement Unit (IMU), two 180-degree rotation servos and a Microchip PCI MCU, three Cornell students implemented a microcontroller-based platform stabilizer. Learn how they used a pre-programmed sensor fusion algorithm and I2C to get the most out of their design.

Designing a Home Cleaning Robot (Part 2)
Continuing on with this four-part article series about building a home cleaning robot, Nishant Mittal this time discusses the mechanical aspect of the design. The robot is based on Cypress Semiconductor’s PSoC microcontroller.

Massage Vest Uses PIC32 MCU
Microcontrollers are being used for all kinds of things these days. Learn how three Cornell graduates designed a low-cost massage vest that pairs seamlessly with a custom iOS app. Using the Microchip PIC32 for its brains, the massage vest has sixteen vibration motors that the user can control to create the best massage possible.

AND MORE FROM OUR EXPERT COLUMNISTS:

Five Fault Injection Attacks
Colin O’Flynn returns to the topic of fault injection security attacks. To kick off 2018, he summarizes information about five different fault injection attack stories from 2017—attacks you should be thinking about as an embedded designer.

Money Sorting Machines (Part 2)
In part 1, Jeff Bachiochi delved into the interesting world of money sort machines and their evolution. In part 2, he discusses more details about his coin sorting project. He then looks at a typical bill validator implementation used in vending systems.

Overstress Protection
Last month George Novacek reviewed the causes and results of electrical overstress (EOS). Picking up where that left off, in this article he looks at how to prevent EOS/ESD induced damage—starting with choosing properly rated components.

December Circuit Cellar: A Sneak Preview

The December issue of Circuit Cellar magazine is coming soon. Want a sneak peak? We’ve got a great selection of excellent embedded electronics articles for you.

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 Here’s a sneak preview of December Circuit Cellar:

MICROCONTROLLERS IN MOTION

Special Feature: Electronics for Wearable Devices
Circuit Cellar Chief Editor Jeff Child examines how today’s microcontrollers, sensors and power electronics enable today’s wearable products.

329 Cover Screen CapSimulating a Hammond Tonewheel Organ
(Part 2)

Brian Millier continues this two-part series about simulating the Hammond tonewheel organ using a microcontrollers and DACs. This time he examines a Leslie speaker emulation.

Money Sorting Machines (Part 1)
In this new article series, Jeff Bachiochi looks the science, mechanics and electronics that are key to sorting everything from coins to paper money. This month he discusses a project that uses microcontroller technology to sort coins.

Designing a Home Cleaning Robot (Part 1)
This four-part article series about building a home cleaning robot starts with Nishant Mittal discussing his motivations behind to his design concept, some market analysis and the materials needed.

SPECIAL SECTION: GRAPHICS AND VISION

Designing High Performance GUI
It’s critical to understand the types of performance problems a typical end-user might encounter and the performance metrics relevant to user interface (UI) design. Phil Brumby of Mentor’s Embedded Systems Division examines these and other important UI design challenges.

Building a Robotic Candy Sorter
Learn how a pair of Cornell graduates designed and constructed a robotic candy sort. It includes a three degree of freedom robot arm and a vision system using a Microchip PIC32 and Raspberry Pi module.

Raster Laser Projector Uses FPGA
Two Cornell graduates describe a raster laser projector they designed that’s able to project images in 320 x 240 in monochrome red. The laser’s brightness and mirrors positions are controlled by an FPGA and analog circuitry.

ELECTRICITY UNDER CONTROL

Technology Spotlight: Power-over-Ethernet Solutions
Power-over-Ethernet (PoE) enables the delivery of electric power alongside data on twisted pair Ethernet cabling. Chief Editor Jeff Child explores the latest chips, modules and other gear for building PoE systems.

Component Overstress
When an electronic component starts to work improperly, Two likely culprits are electrical overstress (EOS) and electrostatic discharge (ESD). In his article, George Novacek breaks down the important differences between the two and how to avoid their effects.

AND MORE FROM OUR EXPERT COLUMNISTS:

Writing the Proposal
In this conclusion to his “Building an Embedded Systems Consulting Company” article series, Bob Japenga takes a detailed look at how to craft a Statement of Work (SOW) that will lead to success and provide clarity for all stakeholders.

Information Theory in a Nutshell
Claude Shannon is credited as one of the pioneers of computer science thanks to his work on Information Theory, informing how data flows in electronic systems. In this article, Robert Lacoste provides a useful exploration of Information Theory in an easily digestible way.

October Circuit Cellar: A Sneak Preview

The October issue of Circuit Cellar magazine is on the launch pad, ready to deliver a selection of excellent embedded electronics articles covering trends, technology and design.

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TECHNOLOGY FOR DRONES / ROBOTIC HAND

Commercial Drone Design Solutions Take Flight: Chips, Boards and Platforms
The control, camera and comms electronics inside today’s drones have to pack in an ambitious amount of functionality. Circuit Cellar Chief Editor Jeff Child explores the latest Oct 327 Coverand greatest chip and module solutions serving today’s commercial and consumer drone designs.

Building a Robot Hand: With Servos and Electromyography
Learn how three Cornell University students developed a robotic hand. The system captures impulses generated by muscle contractions and then filters and feeds those signals to a microcontroller which controls finger movement.

 

CAN’T STOP THE SIGNAL

Signal Chain Tech Pushes Bandwidth Barriers: ADCs, FPGAs and DACs
FPGAs and D-A converters are key  technologies making up a signal chain. Here, Circuit Cellar Chief Editor Jeff Child steps through the state-of-the-art options available for crafting efficient, highly-integrated signal-centric systems.

Antenna Performance Measurement Made Easy: Covering the Basics
If you’re doing any kind of wireless communications design, chances are you’re including an antenna. Columnist Robert Lacoste shows how the task of measuring an antenna’s performance is less costly and exotic than you’d think.

MONITORING GEAR WITH MICROCONTROLLER BRAINS

Gas Monitoring and Sensing (Part 1): Fun with Fragrant Analysis
Columnist Jeff Bachiochi covers the background issues surrounding gas monitoring and sensing. Then he describes how he uses sensors, A/D conversion and Arduino technologies to do oxygen measurement.

Logger Device Tracks Amp Hours (Part 1): Measuring Home Electricity
Setting out to monitor and log electricity usage in his house, Bill Wachsmann built an amp-hour logger using a microcontroller and a clamp on ammeter.

KEEPING THE LEGACY ALIVE

Emulating Legacy Interfaces: Do it with Microcontrollers
There’s a number of important legacy interface technologies—like ISA and PCI—that are no longer supported by the mainstream computing industry. In his article Wolfgang Matthes examines ways to use microcontrollers  to emulate the bus signals of legacy interconnect schemes.

Building a Retro TV Remote : PIC MCU-Based Design
Dev Gualtieri embarks on building a retro-style TV remote, based on a Microchip PIC microcontroller. He outlines the phototransistor, battery and software designs he made along the way.

AND MORE FROM OUR EXPERT COLUMNISTS:

Get in the Loop on Positive Feedback: New Value in an Old Concept
Positive feedback loops are an important element of modern circuitry such as crystal oscillators, PLLs and other devices. Here, George Novacek goes deep into the math and circuit analysis of positive feedback and how it’s used in electronics.

Build an Embedded Systems Consulting Company (Part 6): Trade-Offs of Fixed-Price Contracts
Continuing his “Building an Embedded Systems Consulting Company” article series, this month Bob Japenga explores the nature of contracts and how fixed price contracts can be an effective, albeit dangerous tool in marketing.

Adaptive Robotics: An Interview with Henk Kiela

The Adaptive Robotics Lab at Fontys University in Eindhoven, Netherlands, has a high “Q” factor (think “007”). Groups of students are always working on robotics projects. Systems are constantly humming. Robots are continually moving around. Amid the melee, Circuit Cellar interviewed Professor Henk Kiela about the lab, innovations like adaptive robotics, and more.

“Adaptive robotics is the new breed of robots that are going to assist workers on the shopfloor and that will take care of a high variety of routine activities. Relieving them from routine work allows the workers to concentrate on their skills and knowledge and prevent them from getting lost in details. In a car-manufacturing operation you have a lot of robots doing more or less the same job, a top-down controlled robotization. We recognise that the new generation of robots will act more like an assistant for the worker— a flexible workforce that can be configured for different types of activities.”—Henk Kiela

3-D Object Segmentation for Robot Handling

A commercial humanoid service robot needs to have capabilities to perform human-like tasks. One such task for a robot in a medical scenario would be to provide medicine to a patient. The robot would need to detect the medicine bottle and move its hand to the object to pick it up. The task of locating and picking a medicine bottle up is quite trivial for a human. What does it take to enable a robot to do the same task? This, in fact, is a challenging problem for a robot. A robot tries to make sense of its environment based on the visual information it receives from a camera. Even then, creating efficient algorithms to identify an object of interest in an image, calculating the location of the robot’s arm in space, and enabling it to pick the object up is a daunting task. For our senior capstone project at Portland State University, we researched techniques that would enable a humanoid robot to locate and identify a common object (e.g., a medicine bottle) and acquire real-time position information about the robot’s hand in order to guide it to the target object. We used an InMoov open-source, 3-D humanoid robot for this project (see Photo 1).

Photo 1 The InMoov robot built at Portland State University’s robotics lab

Photo 1: The InMoov robot built at Portland State University’s robotics lab

SYSTEM OVERVIEW

In the field of computer vision, there are two dominant approaches to this problem—one using pixel-based 2-D imagery and another using 3-D depth imagery. We chose the 3-D approach because of the availability of state-of-the-art open source algorithms, and because of the recent influx of cheap stereo depth cameras, like the Intel RealSense R200.

Solving this problem further requires a proper combination of hardware and software along with a physical robot to implement the concept. We used an Intel Realsense R200 depth camera to collect 3-D images, and an Intel NUC with a 5th Generation Core i5 to process the 3-D image information. Likewise, for software, we used the open-source Point Cloud Library (PCL) to process 3-D point cloud data.[1] PCL contains several state-of-the-art 3-D segmentation and recognition algorithms, which made it easier for us to compare our design with other works in the same area. Similarly, the information relating to the robot arm and object position computed using our algorithms is published to the robot via the Robot Operating System (ROS). It can then be used by other modules, such as a robot arm controller, to move the robot hand.

OBJECT SEGMENTATION PIPELINE

Object segmentation is widely applied in computer vision to locate objects in an image.[2] The basic architecture of our package, as well as many others in this field, is a sequence of processing stages—that is, a pipeline. The segmentation pipeline starts with capturing an image from a 3-D depth camera. By the last stage of the pipeline, we have obtained the location and boundary information of the objects of interest, such as the hand of the robot and the nearest grabbable object.

Figure 1: 3-D object segmentation pipeline

Figure 1: 3-D object segmentation pipeline

The object segmentation pipeline of our design is shown in Figure 1. There are four main stages in our pipeline: downsampling the input raw image, using RANSAC and plane extraction algorithms, using the Euclidean Clustering technique to segment objects, and applying a bounding box to separate objects. Let’s review each one.

The raw clouds coming from the camera have a resolution which is far too high for segmentation to be feasible in real time. The basic technique for solving this problem is called “voxel filtering,” which entails compressing several nearby points into a single point.[3] In other words, all points in some specified cubical region of volume will be combined into a single point. The parameter that controls the size of this volume element is called the “leaf size.” Figure 2 shows an example of applying the voxel filter with several different leaf sizes. As the leaf size increases, the point cloud density decreases proportionally.

Figure 2: Down-sampling results for different leaf sizes

Figure 2: Down-sampling results for different leaf sizes

Random sample consensus (RANSAC) is a quick method of finding mathematical models. In the case of a plane, the RANSAC method will create a virtual plane that is then rotated and translated throughout the scene, looking for the plane with the data points that fit the model (i.e., inliers). The two parameters used are the threshold distance and the number of iterations. The greater the threshold, the thicker the plane can be. The more iteration RANSAC is allowed, the greater the probability of finding the plane with the most inliers.

Figure 3: The effects of varying the number of iterations of RANSAC. Notice that the plane on the left (a), which only used 200 iterations, was not correctly identified, while the one on the right (b), with 600 iterations, was correctly identified.

Figure 3: The effects of varying the number of iterations of RANSAC. Notice that the plane on the left, which only used 200 iterations, was not correctly identified, while the one on the right, with 600 iterations, was correctly identified.

Refer to Figure 3 to see what happens as the number of iterations is changed. The blue points represent the original data. The red points represent the plane inliers. The magenta points represent the noise (i.e., outliers) remaining after a prism extraction. As you can see, the image on the left shows how the plane of the table was not found due to RANSAC not being given enough iterations. The image on the right shows the plane being found, and the objects above the plane are properly segmented from the original data.

After RANSAC and plane extraction in the segmentation pipeline, Euclidean Clustering is performed. This process takes the down-sampled point cloud—without the plane and its convex hull—and breaks it into clusters. Each cluster hopefully corresponds to one of the objects on the table.[4] This is accomplished by first creating a kd-tree data structure, which stores the remaining points in the cloud in a way that can be searched efficiently. The cloud points are then iterated again with a radius search being performed for each point. Neighboring points within the threshold radius are then added to the current cluster and marked as processed. This continues until all points in the cloud have been marked as processed and put into different segments before the algorithm terminates. After the object segmentation and recognition has been performed, the robot knows which object to pick up, but it doesn’t know the boundaries of the object.


Saroj Bardewa (saroj@pdx.edu) is pursuing an MS in Electrical and Computer Engineering at Portland State University, where he earned a BS in Computer Engineering in June 2016. His interests include computer architecture, computer vision, machine learning, and robotics.

Sean Hendrickson (hsean@pdx.edu) is a senior studying Computer Engineering at Portland State University. His interests include computer vision and machine learning.


This complete article appears in Circuit Cellar 320 (March 2017).

Scribbler 3 (S3) Hackable Robots

Parallax’s Scribbler 3 (S3) is a fully-assembled, preprogrammed, reprogrammable, and hackable robot that’s well suited for students and electronics enthusiasts. You can program the S3 in Parallax’s Graphical User Interface (GUI) software or its BlocklyProp tool. The visual programming support in Google’s Blockly makes learning to program easier than ever.

The S3’s improvements over its predecessor include:

  • Rechargeable lithium ion battery pack
  • Exposed Hacker Port with access to I/O and high-current power connections
  • XBee socket inside for RF networking and future wireless programming
  • Line sensor improvement: easy to follow lines of all types with Blockly
  • Up to 25% faster

The Scribbler 3 robot costs $179.

Source: Parallax

The Future of Robotics Technology

Advancements in technology mean that the dawn of a new era of robotics is upon us. Automation is moving out of the factory and in to the real world. As this happens, we will see significant increases in productivity as well as drastic cuts in employment. We have an opportunity to markedly improve the lives of all people. Will we seize it?

For decades, the biggest limitations in robotics were related to computing and perception. Robots couldn’t make sense of their environments and so were fixed to the floor. Their movements were precalculated and repetitive. Now, however, we are beginning to see those limitations fall away, leading to a step-change in the capabilities of robotic systems. Robots now understand their environment with high fidelity, and safely navigate through it.

On the sensing side, we’re seeing multiple order of magnitude reductions in the cost of 3-D sensors used for mapping, obstacle avoidance, and task comprehension. Time of flight cameras such as those in the Microsoft Kinect or Google Tango devices are edging their way into the mainstream in high volumes. LIDAR sensors commonly used on self-driving cars were typically $60,000 or more just a few years ago. This year at the Consumer Electronics Show (CES), however, two companies, Quanergy and Velodyne, announced new solid-state LIDAR devices that eliminate all moving parts and carry a sub-$500 price point.

Understanding 3-D sensor data is a computationally intensive task, but advancements in general purpose GPU computing have introduced new ways to quickly process the information. Smartphones are pushing the development of small, powerful processors, and we’re seeing companies like NVIDIA shipping low cost GPU/CPU combos such as the X1 that are ideal for many robotics applications.

To make sense of all this data, we’re seeing significant improvements in software for robotics. The open-source Robot Operating System (ROS), for example, is widely used in industry and at 9 years old, just hit version 2.0. Meanwhile advances in machine learning mean that computers can now perform many tasks better than humans.

All these advancements mean that robots are moving beyond the factory floor and in to the real world. Soon we’ll see a litany of problems being solved by robotics. Amazon already uses robots to lower warehousing costs, and several new companies are looking to solve the last mile delivery problem. Combined with self-driving cars and trucks this will mean drastic cost reductions for the logistics industry, with a ripple effect that lowers the cost of all goods.

As volumes go up, we will see cost reductions in expensive mechanical components such as motors and linkages. In five years, most of the patents for metal 3-D printers will expire, which will bring on a wave of competition to lower costs for new manufacturing methods.
While many will benefit greatly from these advances, there are worrying implications for others. Truck driver is the most common job in nearly every state, but within a decade those jobs will see drastic cuts. Delivery companies like Amazon Fresh and Google Shopping Express currently rely on fleets of human drivers, as do taxi services Uber and Lyft. It seems reasonable that those companies will move to automated vehicles.

Meanwhile, there are a great number of unskilled jobs that have already reduced workers to near machines. Fast food restaurants, for example, provide clear cut scripts for workers to follow, eliminating any reliance on human intelligence. It won’t be long before robots are smart enough to do those jobs too. Some people believe new jobs will be created to replace the old ones, but I believe that at some point robots will simply surpass low-skilled workers in capability and become more desirable laborers. It is my deepest hope that long before that happens, we as a society take a serious look at the way we share the collective wealth of our Earth. Robots should not simply replace workers, but eliminate the need for humans to work for survival. Robots can so significantly increase productivity that we can eliminate scarcity for all of life’s necessities. In doing so, we can provide all people with wealth and freedom unseen in human history.

Making that happen is technologically simple, but will require significant changes to the way we think about society. We need many new thinkers to generate ideas, and would do well to explore concepts like basic income and the work of philosophers like Karl Marx and Friedrich Engels, among others. The most revolutionary aspect of the change robotics brings will not be the creation of new wealth, but in how it enables access to the wealth we already have.

Taylor Alexander is a multidisciplinary engineer focused on robotics. He is founder of Flutter Wireless and works as a Software Engineer at a secretive robotics startup in Silicon Valley. When he’s not designing for open source, he’s reading about the social and political implications of robotics and writing for his blog at tlalexander.com.

This essay appears in Circuit Cellar 308, March 2016.

Brain Control: An Interview with Dr. Max Ortiz Catalan

Dr. Max Ortiz Catalan is Research Director at Integrum AB, a medical device company based in Molndal, Sweden. Wisse Hettinga recently interviewed him about his work in the field of prosthetic design and biomedical systems.MOC_Lab3

As an electrical engineer, your first focus is to create new technology or to bring a new schematic design come to life. Dr. Max Ortiz Catalan is taking this concept much further. His research and work is enabling people to really start a new life!

People without an upper limb often find it difficult to manage tasks due to the limitations of prostheses. Dr. Catalan’s research at Chalmers University of Technology and Sahlgrenska University Hospital in Gothenburg, Sweden, focuses on the use of osseointegrated implants and a direct electronic connection between the nervous system and a prosthetic hand. People can control the prosthesis just like you control your hand, and they are able to sense forces as well. The results are impressive. The first patient received his implant three years ago and is successfully using it today. And more patients will be treated this year. I recently interviewed Dr. Catalan about his work. I trust this interview will inspire seasoned and novice engineers alike.—Wisse Hettinga

HETTINGA: What led you to this field of research?

CATALAN: I was always interested in working on robotics and the medical field. After my bachelor’s in electronics, my first job was in the manufacturing industry, but I soon realized that I was more interested in research and the development of technology. So I left that job to go back to school and do a master’s in Complex Adaptive System. I also took some additional courses in biomedical engineering and then continued working in this field where I did my doctoral work.

HETTINGA: I was surprised you did not mention the word “robot” once in your TEDx presentation (“Bionic Limbs Integrated to Bone, Nerves, and Muscles”)? Was that coincidence or on purpose?

CATALAN: That was coincidence, you can call a prosthesis a “robotic device” or “robotic prosthesis.” When you talk about a “robot,” you often see it as an independent entity. In this case, the robotic arm is fully controlled by the human so it makes more sense to talk about bionics or biomechatronics.

HETTINGA: What will be the next field of research for you?

CATALAN: The next step for us is the restoration of the sense of touch and proprioception via direct nerve stimulation, or “neurostimulation.” We have developed an embedded control system for running all the signal processing and machine learning algorithms, but it also contains a neurostimulation unit that we use to elicit sensations in the patient that are perceived as arising from the missing limb. The patients will start using this system in their daily life this year.

HETTINGA: You are connecting the controls of the prosthesis with nerves. How do you connect a wire to a nerve?

CATALAN: There are a variety of neural interfaces (or electrodes) which can be used to connect with the nerves. The most invasive and selective neural interfaces suffer from long-term instability. In our case we decided to go for a cuff electrode, which is considered as a extra-neural interface since it does not penetrate the blood-nerve barrier and is well tolerated by the body for long periods of time, while also remaining functional.

HETTINGA: Can you explain how the nerve signals are transferred into processable electric signals?

CATALAN: Electricity travels within the body in the form of ions and the variations in electric potentials, or motor action potentials for control purposes. They are transduced into electrons by the electrodes so the signals can be finally amplified by analog electronics and then decoded on the digital side to reproduce motor volition by the prosthesis.

HETTINGA: What is the signal strength?

CATALAN: Nerve signals (ENG) are in the order of microvolts and muscle signals (EMG) in the order of millivolts.

HETTINGA: What technologies are you using to cancel out signal noise?

CATALAN: We use low-noise precision amplifiers and active filtering for the initial signal conditioning, then we can use adaptive filters implemented in software if necessary.

HETTINGA: How do you protect the signals being disturbed by external sources or EM signals?

CATALAN: Since we are using implanted electrodes, we use the body as a shielding, as well as the titanium implant and the electronics housing. This shielding becomes part of the amplifier’s reference so it is rejected as common noise.

HETTINGA: How are the signals transferred from the nerves to the prosthesis?

CATALAN: The signals from nerves and muscles are transferred via the osseointegrated implant to reach the prosthesis where they are amplified and processed. In a similar ways, signals coming from sensors in the prosthesis are sent into the body to stimulate the neural pathways that used to be connected to the biological sensors in the missing hand. Osseointegration is the key difference between our work and previous approaches.

HETTINGA: What sensors technologies are you using in the prosthetic hand?

CATALAN: At this point it is rather straightforward with strain gauges and FSRs (Force Sensitive Resistor), but on research prostheses, motors are normally instrumented as well so we can infer joint angles.

This interview appears in Circuit Cellar 307 February.