Morteza Daneshmand holds a Ph.D. in Engineering and Technology from the University of Tartu, Estonia, earned in 2018. Prior to that, he completed his M.Sc. in Mechatronics Engineering at the University of Tehran, Iran, in 2014, and obtained a B.Sc. in Electrical Engineering (Control) from Amirkabir University of Technology, Tehran, Iran, in 2011. His academic journey reflects a diverse background, with a major focus on mechatronics and control engineering.
A seasoned researcher and expert in human-robot collaboration, Morteza has extensive experience in the field. Currently serving as a Postdoc at the Norwegian Institute of Bioeconomy Research (NIBIO) in Ås, Norway, he previously worked as a Researcher at Kyoto University, Japan, and as a Research Fellow in Human-Robot Collaboration in Manufacturing at the University of Tartu, Estonia. His career spans various roles, each contributing to his expertise in industrial and collaborative robotics.
Morteza has made significant contributions to the field, reflected in his publications. Notable works include articles in Frontiers in Bioengineering and Biotechnology, Robotics and Autonomous Systems, and the Journal of Alzheimer’s Disease. His research interests encompass robotics, computer vision, digital forestry, medical robotics, artificial intelligence, and advanced algorithms.
In addition to his research pursuits, Dr. Daneshmand actively engages in teaching and supervision. With a passion for education, he has successfully delivered courses and mentored students in various research projects. His commitment to advancing knowledge in engineering and technology is evident in his multifaceted contributions to academia and research.
Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, among others, by sampling-based methods and convex optimization strategies. Although they have been investigated from various perspectives in the existing literature, recent developments aimed at integrating robots into social, healthcare, industrial, and educational contexts have attributed greater importance to additional concepts that would allow them to communicate, cooperate, and collaborate with each other, as well as with human beings, in a meaningful and efficient manner. Therefore, in this survey, in addition to a brief overview of some of the essential aspects of motion planning algorithms, a few vital considerations required for assimilating robots into real-world applications, including certain instances of social, urban, and industrial environments, are introduced, followed by a critical discussion of a set of outstanding issues worthy of further investigation and development in future scientific studies.
SFI SmartForest: Bringing Industry 4.0 to the Norwegian forest sector
SmartForest will position the Norwegian forest sector at the forefront of digitalization resulting in large efficiency gains in the forest sector, increased production, reduced environmental impacts, and significant climate benefits. SmartForest will result in a series of innovations and be the catalyst for an internationally competitive forest-tech sector in Norway. The fundamental components for achieving this are in place; a unified and committed forest sector, a leading R&D environment, and a series of progressive data and technology companies.