Automation Archives - Robotics Academy

Precise measurement using machine vision

The Robotics Academy was commissioned to investigate further the potential of machine vision for Neorem Magnets’ needs. Atte Ali-Hokka and Juha Aalto were involved in the project in the Robotics Academy team. The project was directed by Joonas Kortelainen. The task of the team was to investigate the possibility to measure the object delivered by the company with a machine vision camera in the Satakunta University of Applied Sciences’ RoboAI collaborative laboratory.

The customer wanted to study the technical dimensions and quality of the object as accurately and reliably as possible. The project used an IDS camera that captures the subject in very high resolution. The team implemented the program in Halcon development environment that meets the customer’s requirements. The group had no previous experience with machine vision, but the Academy students were happy to take up the challenge.

The client was particularly interested in exploring the possibilities of measuring and examining the area of the object and its formal correctness compared to the technical drawings provided by the company. The students had no previous experience in creating such a program so students went on to study the measurement of the object by creating a program that can measure the length, angles of sides and perimeter of the object.

As the team learned about the new object an image was created in the real world coordinate system allowing the size of the object to be measured in millimeters. This development step opened the doors for the working group to a whole new way of measuring, as it was now possible to compare the image to the technical drawings provided by the customer to the working group.

Summary:

The customer seemed satisfied with the results achieved by the working group. The students and the client felt that both parties had benefited from the project. For students, this benefit is reflected in the amount of learning that students gained from working on the project with a great deal of machine vision, as well as camera, lighting, and programming techniques. The Robotics Academy would like to thank Neorem Magnets for taking the opportunity to explore their expertise and provide an interesting project.

Machine vision as an industrial tool

Our client, Boliden Harjavalta’s main products are copper, nickel, gold and silver. Boliden’s nickel smelter is the only one in Western Europe. Boliden produces high quality nickel matte using the latest technology in the industry.

The Robotics Academy students were commissioned to explore the possibilities of using machine vision in the Boliden process. Boliden was interested in the concentration of various components of the slag after the smelting process and hoped to use machine vision to simplify and speed up the process.

A machine vision system is one where computer imaging is used for industrial purposes. The system consists of a light source, an object to be photographed, a camera, a computer, and an image processing program that interprets the image automatically. Machine vision systems perform precisely pre-programmed tasks and are primarily used when optical inspection should be fast, accurate, round the clock and consistently repeatable. Machine vision can be used to perform tasks that are impossible for human vision by using wavelengths that cannot be detected by the human eye. In this assignment, a conveyor belt cared slag under the system to identify the customer-defined concentrations.

When using a smart camera, all image processing and counting is done in the camera itself. We chose to use Cognex’s In-Sight application, but the slowness of this camera was a disadvantage because the items had to be pictured on a fast conveyor belt. Due to scheduling concerns, Robotics Academy students were not able to continue their research further, however, it is possible to continue the project with another software and a different camera solution in the future. We recommend considering Halco software, which could handle all intellectual observation and computing, as well as speed up image processing.

Summary

In our project, we learned how to take advantage of different camera and light options. The overall benefits were mutual. The students’ gained knowledge and were able to give the customer the information they sought. With the help of additional instruction, we can better utilize machine vision in future projects.

Automated beverage bottle opener

Satakunta University of Applied Sciences has a Universal Robots 5 (UR5) robot arm, which is utilized for educational purposes by students as well as for school demonstrations. A demonstration that has proven popular at public events includes UR5 dispensing drinks. Through code, the robot is directed to use its machine vision to take a bottle from a bottle rack, pour the drink into the cups and drop the bottle into the trash. One major issue with this demonstration is that the bottles need to be manually opened. The number of drinks served at these public events range from tens to hundreds, depending on the audience, so it made more sense to use a machine for opening bottles.

This case was given to the students of the Robotics Academy to solve. After examining and analyzing the current situation, they discovered that the robot’s own grip strength was not enough to be able to spin the cap open and separate it from the bottle. The group responsible for this case mapped possible solutions, taking programming, mechanical and functional needs into account.

The biggest challenge was designing a gripper/opening head, which would suit the majority of bottle types and would also work for the robot, in terms of possible features. From the mechanical implementation point of view, the multifunctionality of the opening mechanism presented a challenge. The opening head had to rotate to remove the cap and then the cap had to be removed from the device before the next beverage bottle was brought to the machine. Used caps and bottles had to be deposited in the trash to make the presentation table ready for the next bottle.

The body of the device is constructed of an aluminum profile and the cage was designed from plexiglass. SolidWorks -3D design software was used to design mechanical parts, each track and gear carefully modelled to fit together. When the models were ready, they were printed on the school’s 3D printers. We chose biodegradable and inexpensive PLA plastic. In this way, making the new parts was as easy with the ability to print more parts, if needed.

For programming the opener, we chose Ardo’s Uno. It is a small microcomputer that is programmed in C ++ programming language.

Summary

The working solution: The robot takes the bottle under the opening head and the attachment removes the cap. The necessary equipment was made to place the removed cap in the garbage. We tested the operation of the device, and after the adjustments we got it to work consistently.

Collaborative robot in assembly work

Robotics Academy was commissioned by Oras Group to investigate the robot’s suitability for assembly work. The goal was to automate the five-part line using a assembly robot. ABB’s two-handed YuMi® robot, developed for the assembly of small parts, was selected. Robot has flexible position-changing hands, camera-based positioning and accurate control.

Academy’s students were responsible for programming and simulating the robot, using machine vision, and designing and implementing parts of the robot’s tools, ie. jigs, palettes and levels, by 3D printing. Students in the project have a wide-ranging understanding of robot activity and constraints. The most important goal was achieved – the assembly task was accomplished with a robot.

Automated disposable cup dispenser

Satakunta University of Applied Sciences has a Universal Robots 5 (UR5) robot arm, which is utilized for educational purposes by students as well as for school demonstrations. A demonstration that has proven popular at public events includes a program in which the robot pours the contents of the bottle into three cups. One major issue with this demonstration is the need to manually ensure the placement of the cups. The number of drinks served at these public events range from tens to hundreds, depending on the audience, so it made more sense to use a machine to place cups automatically.

Robotics Academy was commissioned to build an automated dispenser that drops individual cups for the robot to place. Initially, we studied different options and mechanisms for our cup dispenser. We wanted to be able to change out the mechanical parts easily if necessary, so we decided to design parts that can be 3D-printed. Our entire dispenser is designed with SolidWorks, a 3D mechanical engineering software, from the body to all the mechanical parts specifically to fit the robot.

The final model was printed on the school’s Ultimaker 3D printer as individual physical pieces. We chose to use biodegradable and inexpensive PLA plastic. The roll of plastic cable is fed into the printer’s extruder head, which melts the material and extrudes the plastic as thin layers on the heated print bed. 3D printers can use materials such as plastic, metal, ceramic or glass. In this way, making the new parts (e.g., gears and other parts) is as easy as possible. The biggest challenge in designing was to make the cups drop one by one, not in multiples.

Arduino is a small microcomputer capable of executing a variety of C ++ programming languages. Implementing the project required functional teamwork and creative problem solving, along with learning C++. The coding of the Arduino Uno, the brains of the dispenser, also brought its own challenge. It took some time to get the dispenser to drop the cup and then extend after receiving the signal to put it within reach of the UR 5 robot. The solution is designed to be editable if there is a need for it in the future.

Summary

In the project, we learned a lot about 3D design, 3D printing and Arduino coding. It is perfectly possible to utilize every aspect of corporate painting or even at home, so please contact us, so we look at how we can help you.

Robotics Academy Team in Sick Innovation Competition

In spring 2019, the sensor manufacturer Sick organized an Innovation Competition between universities and universities of applied sciences. Satakunta University of Applied Sciences participated in the competition with a Robotics Academy team. A key part of the competition was the sensor that Sick gave and each team had to create a new innovation with the sensor. The competition sensor was a MRS- 6000 3D-LiDAR sensor, which is one of the most powerful laser meters in the product range. Its main features are 24 scan layers and a very large measuring range.

Robotics academy students set up brainstorming and set the goal of combining robotics and sensor accuracy. Buildings and environments have been scanned for some time as 3D models, but no automated applications were available on the market. The team decided to solve this problem and the next step in the project was to design a working prototype. The Omron Mobile Robot is one of the robots in SAMK’s technology lab. It is known to be an excellent transport tool for the sensor, thanks to its high carrying capacity and automated travel. Before the sensor was ready to be mounted on the mobile robot, it needed a rack. Rotating rack was built with 3D printer, and it was designed to rotate with the help of a stepper motor and an Arduino microcircuit. The time of the team was limited, so converting the data from the sensor to the 3D format remained at the level of thought.

The final prototype was made as planned and the team was satisfied with the outcome. During the competition, the team was dealing with many new topics for them, so the new learning was shared with a big bucket.