Smarter Robotic Systems for Autonomous Heavy-Duty Mining Machines
MSc (Tech) Tuomo Kivelä’s doctoral dissertation explores methods that, among others, prevent collisions between mining robots. Kivelä is the first person to graduate from the Doctoral School of Industry Innovations (DSII) at TUT.
While the development of driverless cars is challenging, many feel that the development of autonomous mobile heavy-duty machines, such as robotic drilling systems, represents an even greater challenge. Autonomous machines must be capable of not only moving but also performing productive tasks that vary depending on the industry, such as drilling through rock in mines or moving shipping containers in ports.
The development of an autonomous system with multiple drilling robots working in the same tunnel at the same time requires a comprehensive approach. Robots must be able to work without colliding with the tunnel walls or each other and to identify exceptional circumstances. For example, they must deal with situations where one of the robots malfunctions or falls behind the planned path.
Tuomo Kivelä’s dissertation explores the structural design of serial robotic manipulators and their collision-free path control.
He developed methods for optimizing the structure of a serial robotic manipulator by using computational methods.
“The optimal structure of the robotic manipulator, among others, improves the productivity of the machine and reduces the need for different product variations,” says Kivelä.
He also developed sophisticated control solutions for heavy-duty machines based on a digital tunnel environment, which, among others, enables multiple machines working in the same tunnel to move autonomously and without interruption to the desired location. The system prevents collisions and finds the machines an alternative route, if necessary.
The methods developed by Tuomo Kivelä are designed to work in demanding tunnel environments, where human capacity is not enough to predict and prevent all the potential collisions between, for example, four robots.
The methods are also applicable to more simple autonomous robotic systems, such as forestry cranes and conventional robot cells, where it is easier to anticipate potential collisions.
Close collaboration with industry
Tuomo Kivelä is the first person to graduate from the Doctoral School of Industry Innovations (DSII) established by TUT in 2014. DSII brings together a unique combination of dissertation research, the latest innovation methods, real-world business challenges and professional contacts.
Kivelä completed his dissertation in close collaboration with Sandvik and under the scientific supervision of Professor Jouni Mattila of TUT.
- The DSII model brings together research, product development and business and is a fine example of what university–industry collaboration can ideally be, says Jani Vilenius, Director of Research and Technology at Sandvik Mining and Rock Technology.
DSII invites applications from students and potential partner companies once a year and admits 6-10 new doctoral students on an annual basis. DSII’s current partner companies include, among others, Bosch Rexroth (Germany), Ekokumppanit, FIMA ry, Koja, Metsäteho, Parmaco, Sandvik, SSAB, Suomen Hyötytuuli, Vaisala, Volvo CE (Sweden) and Wärtsilä.
Public defence of a doctoral dissertation on Friday, 1 December
The doctoral dissertation of MSc (Tech) Tuomo Kivelä in the field of automation technology titled ‘Increasing the Automation Level of Serial Robotic Manipulators with Optimal Design and Collision-free Path Control’ will be publicly examined in the Faculty of Engineering Sciences at Tampere University of Technology (TUT) in room K1702 in the Konetalo building (address: Korkeakoulunkatu 6, Tampere Finland) on Friday, 1 December 2017 at 12:00. The opponents will be Professor Shaoping Bai (Aalborg University, Denmark) and Professor Aki Mikkola (Lappeenranta University of Technology, Finland). Professor Jouni Mattila from the Laboratory of Automation and Hydraulics at TUT will act as Chairman.
Tuomo Kivelä (34 years) comes from Perho, Finland, and currently works in the Laboratory of Automation and Hydraulics at TUT.
The dissertation is available online at http://urn.fi/URN:ISBN:978-952-15-4062-2
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