Slamtec China has announced the RPLIDAR A2 2D laser scanner which can take up to 4000 samples of laser ranging per second at a range of 6 meters. The RPLIDAR A2 features a LIDAR unit A2M4-R1 and an embedded motor controller to increase/decrease the speed of the motor or stop them.
This sensor is supposed to be used in robotics for autonomous navigation, localization, and obstacle avoidance. This means that you can use it indoor as well as outdoor. If the LIDAR technology embedded in the sensor has excellent performance when is used for indoor applications, for outdoor applications it should not be exposed to direct sunlight.
RPLIDAR A2 – 360 Degree Laser Scanner Development Kit
Distance Range: 0.15 – 6 m
Angular Range: 0-360 degree
Distance Resolution: <0.5 (0.15~1.5 meters) <1% of the distance (All distance range)
Angular Resolution: 0.9degree
Sample Duration: 0.25 millisecond
Sample Frequency 4000Hz
Scan Rate: 10Hz
The LIDAR kit comes with a USB adapter to connect the sensor to a PC. But if you’re looking to work with Arduino or any other development board, the RPLIDAR A2 is interfaced using the UART bus. Read more →
Two weeks ago, I wrote a review of the mBot Ranger kit. It was for the first time when I’ve been playing with a Makeblock kit, and I was impressed. Quality components, a lot of room for further extensions, etc. You can read more about my experience with the kit in the mBot Ranger review. In this post, I’ll continue my experience with the mBot Ranger, and I shared with you how I hacked the mBot Ranger kit to work autonomously using the ultrasonic sensor.
After 30 minutes of working, I assembled all the hardware components of the mBot kit into a nice robot tank. I was very happy, at least until I get into the software side. The kit can be programmed via PC or using an application. Very soon I found that I have some problems with the documentation, and the software version of the smartphone was not up to date because the kit hadn’t been released at that moment. Regarding the tablet version of the software, I don’t own a tablet. (Yes, I don’t want a tablet!)
Luckily, I knew that the brain of the robot is a Me Auriga brick based on Arduino Mega 2560. Being an Arduino fan, I know what to do to move forward.
The plan was to search in the libraries and find the pins to control the robot motors using the Arduino sketch. After a few minutes of research, I found the pins that I needed to control the two DC motors. Working with the ultrasonic sensor was much easier. I use the Makeblock library to read the distance from it. Read more →
The Taiwanese company Towerpro released a new digital servo motor – MG938. The MG938 is part of the X-Large Servo 50g+ series and features a metal gear, a stall torque of 2.9kg/cm at 4.8V or 3.7kg/cm at 6.1V, and an operating voltage range between 4.8 and 6V. The price for one piece of MG938 is around $14.90 on the Towerpro website. If you want to buy four of them, the price is around $83.17 + $3.81 shipping on Amazon.
The rally car of the future is a robot that drives itself. The researchers from Georgia Tech uses an RC truck, ROS, and the NVIDIA graphics board to build a mini rally robot car that teaches itself to powerslide.
The code is open-source and ROS compatible. They use GitHub to host the source code and instructions. You can download the files from here.
The robots can learn to choose the better path.
The robot can drive aggressively and safe at the same time.
Rally drivers become obsolete.
Taking a seat in a self-driving sports car will not be boring anymore.
The designing and building process of the metallic structure for an all-terrain robot is often harder and longer. Robotpark released a new 4×4 metallic platform body for wheeled mobile robots able to explore uneven terrains.
The platform comes with all the mechanical components. In that way solve all the problems associated with the mechanical structure.
Besides the ultrasonic sensor attached in front of the platform, the only thing that has to add are the electronics systems that control the robot.
Two big DC motors provide tractions to all four wheels. Each wheel has attached a suspension system for locomotion on uneven terrain, and differential steering systems for all four wheels.
These days I got an mBot Ranger for free, and I want to say many thanks to Makeblock for this great kit. The normal price for this kit is around $149.99, but it hasn’t yet been released. If you want to play these days with an older version of mBot, you can find them on Amazon. Otherwise, you have to wait until 23rd May 2016 can buy the mBot Ranger kit.
Autonomous metal robots are hard to build from scratch unless you own a chassis kit and have degrees in electronics and programming. That’s what makes Makeblock’s mBot Ranger sets so amazing. The mBot Ranger kit combines sensors, motors, and an electronic brain with solid anodized aluminum parts, wheels, and tracks to make robots anyone can build. Of course, the robot can also be wirelessly controlled via Bluetooth connection. But I like more the autonomous way.
The good, the bad
well designed with focus on details;
strong and quality components;
a lot of room for further extensions;
if you want to change the batteries, you need to have with you the wrench included in the kit;
the documentation is not up to date;
I have some bad experience with the MeAuriga firmware. A lot of errors at installation in the Arduino IDE;
The more intelligent you want to build a robot, the more complicated it seems that your project becomes. If you’re feeling overwhelmed but still want to build a cheap autonomous robot, take it one step at a time by following this guide about how to build an intelligent robot.
The Instructables user Imetomi shows us how to build on a cheap chassis a robot able to detect obstacles, explore the world with its FPV camera, react to sounds, hold and push objects and recharge its batteries with a solar panel.
The designer uses an Arduino clone to control the robot and a cheap 4WD platform which is priced around $25/kit.
For more information, you’ll want to check out the DIY intelligent autonomous robot’s post at the link below.
Computer vision applications aren’t only particular to experienced developers these days. The amateur DIYers are looking to develop with new and advanced algorithms in computer vision the next autonomous robot or security system. Considering all of these aspects, in this post I explore the latest OpenCV detection and tracking tutorials with applicability in robotics and automation.
Nobody Parks Here! Computer vision is an excellent way to check if somebody uses your parking spot. DIYer Ron Dagdag shows his cloud application which uses a Raspberry Pi as the brain, a cheap Web camera, and the OpenCV library.
Detecting Circles with OpenCV and Python Since its release, the OpenCV library has been hailed as the perfect all-in-one computer vision library. Now, it’s easier to detect and track objects in real-time. In this Instructables tutorial, the developer ShubhamIoT shows us how to detect circles in real-time. The computer vision application can be used for common scenarios.
Determining object color with OpenCV Adrian Rosebrock helps you understand how to determine the shape detection and color labeling in images using Python and OpenCV. This computer vision application is useful in semi-controlled lighting conditions and only for small color sets.
Saving key event video clips with OpenCV Do you need a computer vision application to detect key events? If yes, the same guru – Adrian Rosebrock – in OpenCV and computer vision applications explains how to build from scratch an application that saves key event video clips.
This application is useful if you want to detect motion in a restricted access zone or an intruder in your house.
Messenger Basketball Playing Robot If you’re an Arduino user, DIY-er, game builder, or just like to use computer vision for fun, you need to see this application. In this application, the OpenCV library is used to play a basketball game on a smartphone. The robot is a 3 degree-of-freedom arm that uses a stick to interact with the screen of the smartphone.
Measuring size of objects in an image with OpenCV One of the things you’re probably thinking of when looking at a photo with different objects is the size of the objects. The OpenCv library is again the best way to measure the size of objects reported at a reference object in an image.
Finding extreme points in contours with OpenCV In a tutorial on his blog, Adrian Rosebrock shows us how to find the extreme points in contours with OpenCV. This tutorial is useful for a more advanced hand gesture recognition application.
Building an autonomous indoor robot able to inspect a building, create a map of it and locate objects on the map is not a handy task since it requires knowledge, time and budget.
This indoor mobile robot aims to make the autonomous navigation easier by integrating a set of sensors with artificial intelligence algorithms.
The sensors attached to the mobile platform are:
Sick LMS100 Lidar (a sensor with sensing range of 18 meters and an error of about 20mm)
RGB-D Depth Camera (Kinect-like)
The idea here is to combine hardware resources with many artificial intelligence algorithms to increase the autonomy of the robot. In this case, the autonomous exploration, finding the optimal path, Simultaneous Localization and Mapping (SLAM), automatic path following, obstacle detection and avoidance, and a real-time path re-planning.
All of these features make the robot available for teleoperation, mission planning on maps, real-time mission follower, or robot state and alerts display.
It’s not a simple autonomous robot, but you can find out some inspiration to build a replica of it for yourself over on Robopec’s page.