Projects per year
Dr. Mark A Post has been a Lecturer at the University of Strathclyde in the United Kingdom since January 2014, and works with the Space Mechatronic Systems Technology (SMeSTech) Laboratory in the Department of Design, Manufacture and Engineering Management (DMEM). He is principal investigator on two European Commission-funded projects between 2016-2020 on sensor fusion and reconfigurable self-awareness for Space Robotics, which supports the European Space Agency’s future robotic missions. Dr. Post received his B.A.Sc. in electrical engineering from the University of Toronto in 2004, and his M.Sc. in automated ultrasonic sensing and Ph.D. in space robotics from York University, Canada in 2008 and 2014 respectively. His research focuses on the development of fully autonomous manufacturing systems, mobile robots, and spacecraft. This includes development of many highly-integrated mechatronic design, sensing, learning, and control technologies that allow intelligent and efficient autonomous operation of robots in harsh environments. He has produced works on many subjects including sensing and modular embedded systems, mechatronic design and testing, autonomous vehicle control, and Bayesian Intelligence. He is an associate editor of the Canadian Aeronautics and Space Journal and has reviewed for several international journals including IEEE Transactions on Industrial Electronics and Mechatronics, the Journal of Spacecraft and Rockets, Advances in Space Research, Sensors, and Acta Astronautica.
He is currently looking for motivated Masters and Ph.D. students in the areas of sensor fusion for space robotics, probabilistic machine learning and reasoning for manufacturing, deep learning for FPGA based real time embedded processing acceleration, dynamic and control for robot and AUV, and structural design for planetary rovers.
Mark Post's research interests focus on technologies to make robots and vehicles fully autonomous for long periods and capable of mobility, comprehensive sensing, and decision-making while handling harsh and rugged environments. This includes high-reliability and efficient hardware and software architectures for autonomous operation, the design of lightweight and actuated deployable mechatronic structures, and adaptable machine vision and sensor fusion algorithms to give robots a comprehensive understanding of their environment.
Expertise & Capabilities
Mark Post's experience in research includes machine vision for navigation and recognition, data fusion and actuator control systems, semantic probabilistic learning and reasoning methods, reliable and efficient embedded electronic and power architectures for robots in harsh environments, satellite orbit and attitude control, and manufacturing of structures in both Earth and Space environments. He has developed feature-based visual mapping algorithms for robotic SLAM and object recognition, nonlinear filters and controllers for deeply-embedded processing and logic, a novel framework for high-reliability real-time distributed autonomous robotics, and innovative applications of probabilistic reasoning for robotic intelligence.
I teach the DM942 Manufacturing Automation and DM952 Intelligent Sensing, Learning, and Reasoning modules at Strathclyde. I also lecture in DM309 Mechatronics, DM101 Integrating Studies 1, and DM204 Integrating Studies 2.
Two of my most basic philosophies are “Everything is interesting in its own right” and “Anything is possible”. It greatly disturbs me to see students give up on something just because it is “completely boring” or “too hard to understand”. I make a great effort to determine how to present course material in such a way as to make it both interesting and easy to understand, to pass my enthusiasm on to students, andto give students in-class and out-of-class opportunities to play with new science and technology concepts and reach “Eureka” moments of understanding. Memory is most active when new and surprising elements are introduced and every student has a different learning style, so I use a multi-modal learning approach in the classroom incorporating slides and images, video, group activities, real-hardware demonstrations, and visits to laboratories. Learning and memorizing is easier if existing schema in the brain are activated before new information is introduced, so I focus on connecting subjects of study to immediate experiences that students can relate to easily with minimal abstraction. I also balance both summative and formative assessment in my classes to ensure a continuum of learning, and focus on making assessment methods authentic and easy for students to connect to real-world problems by including both memorization of concepts and use of those concepts.
The signature pedagogy of Engineering education is focused on practical applications, but many university programs have considerably more focus on theoretical knowledge rather than its application. When students cannot apply or understand technical concepts, I have found that it is often the link between the theoretical and the practical that is lacking, sometimes termed “Praxis” (Stierer, 2008). To ensure graduates can effectively apply what they have learned, I have frequently championed the cause of experiential education in the university. In a 1995 paper, Coleman has argued for the necessity of ”multi-disciplinary experience and vertical and horizontal integration of skills and teamwork.“ in an engineering program. (R. J. Coleman, "STEP’, 1995). I worked with the York University Rover Team (YURT) to make this student group into an excellent complement to a classroom education, and published results in an IAC conference paper and a journal paper (M.A.Post and R. Lee, “YURT”, Acta Astronautica, 2010). I currently run a Vertically Integrated Projects (VIP) program called Robotic Vehicles for Research and Education (ROVER) so that students can complete real-world robotics projects in a team, and broaden their experiences while obtaining credit for their work.
My philosophies of teaching have centred on these basic concepts:
- Provide many points of view to choose from and focus on conceptual simplicity and clarity
- Recognize that everyone is different, and structure material to allow for varying skill levels
- Ensure that theoretical and practical concepts are connected and associated to familiar ideas
- Give everyone a voice, and encourage constructive communication and feedback
- Give students freedom to innovate, choose their own path, and have fun with learning
Doctor of Engineering, York University
Master of Science, York University
Bachelor of Engineering, University of Toronto
- Control Systems
- Intelligent Systems
- Embedded Systems
- Real Time systems
- Machine Learning
- Computational Vision
- Power Electronics
- Manufacturing Automation
1/10/18 → 30/09/21
Project: Research - Studentship › Research Fellowship
1/10/17 → 30/09/20
Project: Research - Studentship › Research Studentship
Research Output per year
Research output: Contribution to conference › Paper
Design of a novel wheeled tensegrity robot: a comparison of tensegrity concepts and a prototype for travelling air ductsCarreño, F. & Post, M., 30 Apr 2018, (Accepted/In press) In : Robotics and Biomimetics.
Research output: Contribution to journal › Article
Development of spine design process and implementation of axiomatic design theory for cyber physical system design analysisAuthor: Yang, X., 1 Apr 2016
Student thesis: Master's Thesis