My course plan for Mechanical Engineering with a Minors in Control, Robotics and Autonomous Systems

Finally at the end of July my Aalto account was activated(restored actually since I already had an account because I registered for some public online courses). Excitedly I jumped to the course plan planner service which is called SISU.

Fun fact: SISU is an actual word in Finnish which loosely translates to tenacity.

Unfortunately around the same time a migration from the previous course planner system to sisu was going on so sisu was not available till 9th August.

That was a hard 2 weeks of waiting!

As soon as sisu became available I made my study plan. I want to raise my understanding of current robotics research to an advanced level so I focus all my time on campus with that goal in mind.

My Study Plan

The masters program in Mechanical Engineering in Aalto requires completing 120 credits. 30 of those credits are reserved for the graduate thesis.

Following is the initial plan that I have, broken according to the different sections in sisu.

Basic studies plan

I am taking basic dynamics courses in the first term itself. Dynamics for both rigid bodies and fluids. This is to set the stage for doing more advanced courses which expect the dynamical formulation to be well understood.

Here is the brief introduction for each course:

Dynamics of Rigid Body: Rigid body motion in three dimensions and Lagrangian formalism.

Fluid Dynamics: Understanding of the physical background of fluid flow phenomena and the mathematical description and solution of different fluid flow problems. The possibilities for the solution of Navier-Stokes equations in their full form and the simplifications and solution of the equations in specific cases with particular emphasis on boundary layer and boundary layer like flows. You will learn what turbulence is, how it affects the mean flow and how it is typically handled in the mathematical model. You will also get an introduction to the numerical solution of the flow equations.

Modelling in Applied Mechanics: Develop understanding on the use of common numerical modelling methods in mechanics. The students will work on a numerical modelling projects that are related to their studies. Most projects are related to the use of finite element method and related software, but projects on rigid body dynamics, may be also offered.

I am also taking some wider scoped courses to understand current production methods and product development techniques. While I have no idea about production and manufacturing at all, I do understand product development quite well. My experience of product development obviously comes from building software but the software industry was initially inspired in it's methods from production and manufacturing for example lean and kanban. Design thinking is also spoken of a lot in software

Production Engineering: Seminar course consisting of topics in the field of production engineering. Variable topics include: robotics and automation, digital manufacturing tools and software, and production planning and scheduling.

Methods in Early Product Development: Product development (including Design Thinking), the process and iterative nature of it as well as a selected methods in it.

Required Advanced Studies

Time to build on the basic mechanical engineering from the last 2 terms. My whole point of being on campus is to actually learn these well.

Mechatronic Machine Design: Background of mechatronics, mechatronic machine design process. Analyse the prevailing physics in common mechatronic machines including rigid-body mechanical systems, basic electrical systems, power transmission, and control. Carry out design and numerical simulations of a mechatronic machine.

Mechatronics Project: The aim of the course is to introduce the student into demanding mechatronics machine design and building using a practical project assignment.

I am hoping to use this course time to bring some prototypes I have in mind around micro fulfilment centres and plug and play drone in a box.

Vehicle Mechatronics: Powertrain: Introduction to modern vehicles, electro-hybrid powertrain. Comparison of alternative powertrain and powertrain modelling. Design optimise and simulate electric and hybrid vehicles.

Electric Drives: Applications of electric drives and power-electronic systems. Equation of motion, typical load torque profiles, gears and transmissions. Cascade-controlled DC motor drives. Permanent-magnet synchronous motor drives, space vectors. Motor and converter selection.

Fluid Power Basics: Technical fundamentals of hydraulic and pneumatic systems. Operation and control of hydraulic and pneumatic components. Simulate basic hydraulic and pneumatic systems using software packages. Calculate the characteristics/properties of hydraulic and pneumatic components and systems.

Elective Studies – Minor Required Studies

I will not be waiting to take courses from my minor for later terms. Instead of that I am, for example, taking Modelling, Estimation and Dynamic Systems right from the start as well as Reinforcement Learning.

Modelling, Estimation and Dynamic Systems: First principle and data-driven modelling, for static and dynamic systems: Basics of regression methods, static parameter estimation for linear and non-linear systems. Formulate mathematical models of physical systems, construct models and estimate parameters of systems from measurement data using MATLAB and Simulink.

Digital and Optimal Control: Principles of computer control. -Discrete-time modelling, the z-transform, solving difference equations. -Discretization of continuous time dynamical systems. -Basic characteristics of discrete time systems. -Controller design and performance analysis in discrete time. -Discrete-time PID controllers. -Basics in optimal control theory. -Dynamic programming. -Linear quadratic (LQ) control.

Basics of Sensor Fusion: The course content includes: Probabilistic modelling of dynamic systems, sensor models, batch estimation, Kalman and extended Kalman filtering, bootstrap particle filtering.

The following is the only course from the computer science department which I have currently planned. This is by design since I have spent so many years doing software work. I did some work even with computer vision while in Microsoft so this should be a refresher but I am hoping to study the latest advances and how it would apply to robotic vision and vision based control systems.

Computer Vision: Image formation and processing, feature detection and matching, motion estimation, structure-from-motion, object recognition, image-based 3D reconstruction. The course gives an overview of algorithms, models and methods, which are used in automatic analysis of visual data.

Electives: The fun list

The current list of electives I still need to get approval for. I would love to take all these courses(and then some more) but I am not sure I can. The number of credits is more than 120 if I was to take all of these and then who has the time!

I am taking a few though so lets start in priority order

Reinforcement Learning: Modeling uncertainty. Markov decision processes. Model-based reinforcement learning. Model-free reinforcement learning. Function approximation. Policy gradient. Partially observable Markov decision processes.

I would like to start working with the intelligent robotics lab so that's why I am taking this course early on so that I can start taking part in the lab activities as and when an opportunity become available.

Robotic Manipulation: Robotic manipulation. Grasping and pushing. Motion planning. Motion control. Control in contact. Redundancy. Learning manipulation skills.

Autonomous Mobile Robots: The locomotion and kinematics of mobile robots and intelligent vehicles. Machine perception and sensors for mobile robots; representing uncertainty, wheel/motor/heading sensors, inertial measurement unit (IMU), beacons, active ranging and machine vision for outdoor use. Mobile robot localization and mapping, probabilistic and other map representations, different approaches for SLAM. Path and trajectory planning and navigation, reactive control, obstacle avoidance and safety. Motion Control; trajectory and path following, NMPC. Intelligent autonomous heavy duty work machines and vehicles. Fleet control. Autonomous cars.

Model Based Control Systems: Basic model types of multivariable linear systems. Structural properties of multivariable systems. Canonical control configurations. Analysis of the closed-loop system by sensitivity functions. Fundamental restrictions in control. Relative gain array analysis and decoupling compensators.  Dynamic programming and linear quadratic control.  Loop shaping techniques. Introduction to model predictive control

Networked Control Systems(NCS): Identifying the building blocks of control and communications comprising NCS. Designs and analyse simple control systems over different communication channels. Understands the estimation issues of systems with uncertainties having to communicate over noisy communication channels. Learns when control and estimation problems can be decoupled in such systems and understands the limitations of the current state-of-the-art and trends in NCSs.

The first course from Applied Mathematics. Since so many problems can be formulated as optimization problems I thought of taking and revising what I know about optimization.

Introduction to Optimization: Learn the basic optimisation theory, how to formulate problems and how they can be solved. Linear, integer, and nonlinear optimisation will be covered in the course. At the end of this course, it is expected that the student will be capable of analysing the main characteristics of an optimisation problem and decide what is the most suitable method to be employed for its solution.

Since I worked on Embedded Systems before I have a fair idea of how to work on those. However, never worked on real time embedded systems so if time permits

Embedded Real Time Systems: Fundamentals of real-time systems, hardware for embedded real-time systems, real-time operating systems, applications.

And finally

Neurorobotics: The course content includes: Biosignal acquisition, Biosignal processing, Human in the loop controllers, Human-robot interaction

Main Focus: Design a shippable product

Of course no plan survives the first contact. Plans are nothing but planning is everything.

I might discover something extraordinary in the next 2 years which I cannot even fathom right now what it might be. I am open to the possibility and infact welcome it.


I am NOT going to Aalto to get a masters and get a job.

I am leaving a high paying job so that option is useless for me.

I am going to Aalto because I believe it gives me an opportunity to make something which I can bring to the market. I want to spend my time learning advances in robotics so that I can take the learning and build a commercial product for the market. I am going to Aalto to tap the startup ecosystem and network with the founding team.

That's the plan.

I know it's challenging because courses in Aalto at masters level are no piece of cake so just keeping up with them can be challenging. To add to that learning, by building a product simultaneously is going to be tough.

Never to shirk away from a genuine and interesting challenge. I say bring it on!