Bachelor and master students in Mechatronics are involved in project “DEEPCOBOT”. The projects are supervised by Prof. Jing Zhou and Assoc. Prof. Ilya Tyapin. The bachelor and master students have worked together with PhD students Emil and Jayant.
- Bachelor Thesis title: Human-Cobot Sequential Cooperation
The objective of this bachelor project is to provide a solution to perform semi autonomous collaborative tasks, between a robot and a human. The project is focused on making a usable autonomous solution, that will find a sequence of actions that will lead to a desired goal. The tasks include solving different planning problems such as identifying objects, picking and placing objects, moving around in a known environment, and interacting with the environment to complete its goal. To achieve these tasks it was necessary to make new software for the robot to run. All programming was done within the ROS framework. By using ROS melodic, Linux Ubuntu 18.04 and Python programming language. The majority of all testing was in simulation using Gazebo, testing was done on the physical robot when simulation was satisfactory.
Students: Eirik Magnus Skår, Benjamin Årøy Ims and Bjørn Enehaug
Thesis title: Human-Cobot Sequential Cooperation
Students: Ravi Kumar, Jan-Philip Radicke, Eirik Eidhammer
Bachelor Thesis: Multi Robot Cell Operation using Robot Operating System
Bachelor Thesis: Multi Robot Cell Operation using Robot Operating System
This project contains the work for a bachelor’s thesis at the University of Agder (UiA) in spring 2021. With cobots being the fastest growing segment of industrial automation, this thesis will investigate a multi robot cell operation. This cell consists of the Universal Robot 5 (UR5) and TIAGo. This project was carried out with the use of Robot Operating System (ROS) with the Melodic Morenia distribution along side Linux Ubuntu 18.04. The goal of this thesis is to make these cobots perform a pick and place operation in a fast and effective
manner. TIAGo will identify an object, pick it up, and navigate to the UR5. Then, UR5 will detect the object and store it. This task is performed both in simulation, as well as on a physical setup.
Master Thesis: Model-free object grasping with a learning-free approach
Student: Tom Erik Vange
The industry standards and capability are constantly advancing and pushing forward to increase data collection, efficiency, profit, and quality as well as decrease downtime, injuries, and hazards as much as possible. In recent years, robot systems have received more attention in the context of a large number of industrial applications, such as automotive manufacturing, additive manufacturing, assembly, quality inspection, and co-packing. The collaboration between multiple robots and human operators is considered to be the most prominent strategy in Industry 4.0 and future Industry 5.0, sharing the same space and collaborating on tasks according to their complementary capabilities. With the use of robots and their abilities could efficiency, profit, safety, and quality be further increased, potentially revolutionizing the industry and production.
This project has looked into the problem of grasping an unknown and previously unseen object with a learning-free approach. By implementing a model-free picking algorithm onto a robot arm with a gripper and robot vision could it be able to pick up a vast variety of objects. A virtual environment has been created with a robot arm and depth-camera during this project. The result from this project is a setup that is able to scan objects placed on a workbench and create a point cloud representation of these objects. The point cloud is since used to calculate the curvature of the objects, creating a foundation for further use in a learning-free setup for grasping previously unseen objects.
