Today, I completed a certification course in robotics engineering — and it was an eye-opening experience. When I enrolled, I expected to learn some technical jargon, maybe a few programming tips, and an overview of how robots work. What I didn’t expect was how much this short course could reshape my understanding of robotics as a whole.
Until now, I thought robotics was mainly about building machines that could move — maybe a robotic arm or a walking robot. But after finishing this course, I see that robotics is really about systems thinking: combining mechanics, electronics, programming, sensors, and vision into one functional unit that can sense, decide, and act.
This course didn’t just teach me theories; it gave me a framework to understand how robots operate in the real world. I walked away with clarity on five major areas: the basics of robotics, kinematics, trajectory planning, sensors, and vision. Each of these topics opened a new door in my mind, and I’ll be unpacking them over this blog in detail.
In this first part, I want to share what I learned about the foundations of robotics — the building blocks that helped me connect everything else in the course.
The basics were the starting point of the course, and to my surprise, they weren’t “too basic” at all. Even simple concepts were explained in a way that showed me how essential they are for building more advanced robots.
Here are my biggest takeaways:
Before this course, I had a very general idea of robots. To me, they were machines that moved or did human-like tasks. But the course gave me a clearer definition:
A robot is a programmable machine that can sense, process, and act — either autonomously or semi-autonomously.
That means three things must come together:
Sensing – The robot must be able to take input from the environment (through sensors).
Processing – It needs a “brain” (a controller or computer) to decide what to do with the input.
Action – It must have actuators, motors, or mechanical parts to act on those decisions.
This made me realize that robots are not just about motion; they’re about decision-making and feedback loops. Even a simple line-following robot or an obstacle-avoiding robot works on this principle.
The course broke down robots into four simple but powerful categories. I noted this carefully because it felt like the foundation I can always return to:
Mechanical system (the body): The structure, wheels, joints, arms — everything physical that gives the robot its shape and ability to move.
Electrical system (the power and wiring): Batteries, circuits, motors — the “nervous system” that makes things run.
Control system (the brain): A microcontroller like Arduino or Raspberry Pi that executes commands.
Sensors (the eyes and ears): Devices that let the robot perceive its environment.
I realized that if even one of these is missing, you don’t really have a functioning robot — you just have a piece of hardware.
One of the surprising lessons was how important math is, even at the beginner level. I learned that things like linear algebra, geometry, and basic calculus form the foundation of robotics because they help us describe motion and control.
For example:
Angles and vectors explain how a robotic arm moves.
Basic matrices show how robots change position or orientation.
Velocity and acceleration tell us how smoothly or quickly a robot moves.
The course didn’t dive into complex math, but it gave me enough to see how mathematics is the “language” behind every robotic action.
Another key area was programming. I expected a lot of coding, but what struck me was the logic behind it. I worked with basic scripts in C++ and Python, and I saw how a few lines of code could make a robot sense input and respond.
For instance, programming an obstacle-avoiding robot meant writing conditions like:
If the sensor detects an obstacle → turn right.
Else → move forward.
It sounds simple, but seeing it happen in real time was powerful. It made me realize programming isn’t just about syntax — it’s about giving the robot a way to think.
Physics was another big takeaway. The course tied basic physics concepts to robotics in a way I hadn’t thought about before:
Newton’s laws explained how robots move.
Torque explained how motors turn wheels or arms.
Friction explained why a robot might slip or stall.
It reminded me that every movement is connected to physical laws — nothing in robotics happens outside of them.
The highlight of the course was applying these basics in small hands-on tasks. I worked on simple projects like:
A line-following robot (using sensors to detect a path).
An obstacle-avoiding robot (programming decisions using sensors).
Even though these robots were basic, the experience was unforgettable. Seeing theory turn into a moving machine made me realize how exciting robotics can be.
The basics of robotics — definition, components, math, physics, and programming — gave me a strong foundation in just one certification course. I realized that robotics is not about mastering one single subject; it’s about connecting multiple skills to make something intelligent and functional.
Finishing this first part of the course gave me confidence. Now, I feel ready to explore deeper concepts like kinematics (how robots move), trajectory planning, sensors, and vision systems.
When I first heard the word kinematics in my robotics course, I immediately thought it would be something highly complex — something reserved for advanced mathematicians or roboticists working at NASA. But the truth is, kinematics is simply the study of motion without considering the forces behind it.
In robotics, kinematics is all about understanding how a robot moves:
Where is the robot positioned right now?
How will it move to a new position?
What path will it follow to get there?
The course made me realize that without kinematics, a robot is just a machine with motors. Kinematics gives it the ability to move intelligently and reach specific positions.
For example, imagine a robotic arm on an assembly line. It doesn’t just “swing around.” It must know how to extend to pick up a part, rotate correctly, and then place it in the right spot. All of this precision comes from kinematics.
The first concept we tackled was forward kinematics (FK).
Here’s how I understood it:
Forward kinematics is about calculating the position of the robot’s end-effector (like the hand of a robotic arm) when we know all the joint angles.
In simpler terms: If I know how much each joint of a robot arm has rotated, I can calculate where the tip of the arm is.
The math behind it involved matrices, but the course made it digestible. We used something called transformation matrices, which represent how an object moves in 3D space (rotations and translations).
One exercise showed how a 2-joint robotic arm can move in a plane. By plugging in the joint angles, I could calculate exactly where the “hand” of the robot would end up. That was my first taste of how math directly translates to robot motion.
My takeaway: forward kinematics is straightforward — it’s like moving step by step from the base of the robot to its end, multiplying matrices as you go.
If forward kinematics felt logical, inverse kinematics (IK) was the real challenge.
Here’s the difference:
Instead of starting with joint angles, inverse kinematics starts with the desired position of the robot’s end-effector.
The question is: What joint angles are needed to get there?
This is far harder because there’s often more than one solution — or sometimes no solution at all.
For example, if you ask a robotic arm to touch a point in space, there might be multiple ways it can bend its joints to reach it. Conversely, if the point is too far, the robot simply can’t reach it.
The course gave us simple problems, like finding the joint angles for a 2-link arm to reach a target point (x, y). At first, it looked intimidating, but with trigonometry and equations, we could solve it.
What I found fascinating is that this is the exact math behind robots in factories that weld cars, assemble electronics, or even assist in surgeries. They are solving IK problems in real time to move precisely.
My takeaway: inverse kinematics taught me that robotics is not just about movement, but about finding smart and efficient ways to reach goals.
Another concept I picked up was degrees of freedom (DoF).
In robotics, DoF refers to the number of independent movements a robot can make.
A simple robotic arm with 2 joints has 2 DoF (like moving up and down, left and right).
A human arm has 7 DoF (shoulder, elbow, wrist rotations, etc.).
The more DoF a robot has, the more flexible and capable it is. But higher DoF also means more complex kinematics.
During the course, I learned that industrial robots often have 6 DoF because that’s enough to move freely in 3D space (x, y, z + roll, pitch, yaw). It made me appreciate how human-like these robots can become with just six independent movements.
The course introduced me to the idea of kinematic chains — basically, the sequence of joints and links that make up a robot.
A serial chain is like a human arm: joints connected one after another in a sequence.
A parallel chain is when multiple arms or links work together to control a point (like in robotic flight simulators or hexapod robots).
Seeing these chains helped me visualize robots not as random machines, but as structured systems of links and joints that follow specific patterns.
What really brought the concept to life was how the instructor connected kinematics to real-world robotics. Here are a few examples that stuck with me:
Robotic Arms in Factories:
They rely on forward and inverse kinematics to pick, place, weld, or assemble parts with precision.
Humanoid Robots:
For a humanoid to walk, it must calculate joint angles for legs in real time — that’s IK at work.
Animation and Gaming:
Surprisingly, kinematics isn’t limited to robots. In gaming, IK helps animate characters naturally (like how a character’s hand touches an object smoothly).
Medical Robotics:
Surgical robots use IK to move with sub-millimeter accuracy inside the human body.
These examples showed me how what looked like “just math” is actually the backbone of machines shaping industries and even saving lives.
Even though the course simplified things, I still found kinematics challenging in a few ways:
The math looked overwhelming at first. Lots of symbols, angles, and matrices. But once I broke it down step by step, it started making sense.
Visualizing movement was tricky. I had to keep drawing diagrams of robot arms and labeling angles just to see what was happening.
Multiple solutions confused me. In IK problems, realizing that a robot could reach the same point in two different ways was both exciting and mind-bending.
But these struggles also made the learning rewarding. Each time I solved a kinematics problem correctly, I felt like I unlocked a new skill that real engineers use.
The course gave us a mini-project to apply kinematics on a 2-link robotic arm simulation. We had to:
Use forward kinematics to calculate where the end-effector would be for given angles.
Then use inverse kinematics to figure out what angles were required to reach a desired point.
Even though it was just a simulation on a screen, the moment I saw the robotic arm follow my calculations, I felt the theory “click.” It was proof that what I was learning wasn’t just abstract math — it was real engineering.
Learning kinematics in this certification course opened my eyes to the mathematics of motion. Before, I thought robots just “moved” when you programmed them. Now I see that behind every move is a careful calculation of positions, angles, and paths.
Forward kinematics gave me confidence that I can calculate where a robot’s hand will be.
Inverse kinematics challenged me to think backwards from the goal.
Degrees of freedom showed me why some robots are more capable than others.
And kinematic chains helped me visualize how robots are structured.
After learning the basics and diving into kinematics, the next logical step in my robotics course was trajectory planning and control. If kinematics answers the question “How can a robot move from A to B?”, then trajectory planning goes further and asks:
“What’s the best way to move from A to B?”
“How do we make sure the robot’s motion is smooth, safe, and efficient?”
This was one of the most exciting parts of the course for me, because it showed how robots move in the real world, not just in theory. A robot isn’t useful if it just “knows” the final position — it has to get there in a way that makes sense, without crashing into things, wasting energy, or making jerky movements.
One of the first distinctions the course made was between path planning and trajectory planning. At first, I thought they were the same, but they’re not:
Path planning is about the geometry of movement: the sequence of points the robot must pass through to reach its goal.
Example: Drawing a line on a map for a delivery drone to follow.
Trajectory planning adds the time factor: it specifies how fast and smoothly the robot should move along that path.
Example: Deciding how the drone accelerates, slows down, and adjusts speed while following the line.
That clicked for me: a path is just where the robot goes, but a trajectory is how it goes there.
The course emphasized that robots don’t move in a vacuum; they face constraints. These constraints are what make trajectory planning challenging. Some of the main ones I noted:
Velocity constraints:
A robot can’t accelerate instantly to maximum speed.
Motors and actuators have limits.
Acceleration constraints:
Too much acceleration might tip over a wheeled robot or make a robotic arm unstable.
Jerk (rate of change of acceleration):
Sudden jerks make motion look unnatural and can damage mechanical parts.
Obstacle constraints:
Robots must avoid collisions with people, walls, or other machines.
This part opened my eyes: trajectory planning isn’t just math, it’s designing motion with safety and efficiency in mind.
We were introduced to several methods used in robotics. While I didn’t master them fully in one certification course, I got a solid overview:
Polynomial Trajectories
Using polynomial equations (like cubic or quintic polynomials) to design smooth paths.
The advantage: smooth start and stop (no jerky motions).
Example: A robotic arm picking and placing objects gracefully.
Spline Trajectories
Splines are curves that pass smoothly through a series of points.
Great for when the robot has to visit multiple waypoints.
Example: A drone scanning multiple points in an area.
Sampling-Based Algorithms
RRT (Rapidly-exploring Random Tree): Builds paths by exploring space randomly until it finds a feasible route.
PRM (Probabilistic Roadmap): Creates a “roadmap” of possible paths in the environment.
These are often used in mobile robots and autonomous vehicles.
I found RRT especially interesting because it felt like the robot was “exploring” its environment — almost like problem-solving in real time.
Planning a trajectory is one thing; making the robot actually follow it is another. That’s where control systems come in.
The course explained control in a simple way:
The robot tries to follow a desired path.
Sensors give feedback about its current position.
The controller adjusts the inputs (like motor speed) to correct any errors.
It’s like a self-correcting loop:
Desired trajectory → Robot moves → Sensors check → Controller corrects → Repeat.
We explored two main types of control:
Open-Loop Control
Executes commands without checking if the robot actually followed them.
Simple but unreliable in real-world conditions.
Closed-Loop Control (Feedback Control)
Continuously adjusts based on sensor feedback.
Much more reliable.
Example: PID controllers (Proportional-Integral-Derivative).
PID control fascinated me because it’s used everywhere — from balancing robots to temperature control systems. It showed me how simple mathematical rules can create stability and precision.
The highlight of this section of the course was when we applied trajectory planning to a simulated robotic arm.
First, we created a path for the arm to move between points.
Then, we applied trajectory planning to make it move smoothly with acceleration limits.
Finally, we added feedback control so the arm corrected itself when drifting off the path.
Watching the arm move gracefully instead of jerkily was a big “aha!” moment for me. It drove home the idea that good robots are not just about reaching the goal, but about how they reach it.
The instructor shared examples that made trajectory planning and control feel alive:
Autonomous Cars:
Cars must plan paths around obstacles while also deciding how fast to go at each point. Safety depends on good trajectory planning.
Industrial Robots:
Arms that weld or assemble parts can’t just slam into position. They must move smoothly to avoid errors and damage.
Drones:
Drones rely heavily on trajectory planning for flight stability, especially in windy conditions or crowded spaces.
Space Robotics:
Robots on Mars or in orbit must plan and control motions very carefully, since mistakes can’t be fixed easily.
These stories made me realize that the concepts I was learning in a short course are the same principles powering some of the most advanced robots in the world.
Trajectory planning wasn’t easy. Here’s what I struggled with:
Visualizing paths: Sometimes it was hard to picture how equations turned into smooth curves.
Math complexity: Polynomials and splines got heavy quickly, though the instructor kept it simplified.
Understanding PID tuning: Knowing the theory was easy, but tuning the parameters (P, I, D) to get smooth control felt like an art.
But the course reassured me: these challenges are normal, and mastering them takes practice.
From this part of the certification, I walked away with a few strong lessons:
Robots don’t just need to move — they need to move well.
Path planning is about geometry, trajectory planning is about time.
Constraints are as important as goals. If you ignore limits like acceleration or obstacles, the plan fails.
Feedback is everything. Without control loops, robots can’t handle real-world unpredictability.
Theory comes alive when tested. Simulations made everything clearer for me.
Trajectory planning and control took my understanding of robotics to the next level. It taught me that robots aren’t just machines that “go from point A to B.” They’re systems that need to move smoothly, safely, and intelligently.
When I reached the section on sensors and computer vision in my robotics certification course today, everything started to feel more alive. Up until this point, I had been dealing with fundamentals, kinematics, and trajectory planning — the mathematics and mechanics of movement. But movement without perception is like walking blindfolded in a crowded street. This was the part where I realized: if kinematics is the skeleton and trajectory planning is the brain’s motor function, then sensors and vision are the senses that make a robot truly intelligent.
In this part of my learning journey, I dove into the world of sensors — the devices that let robots feel, measure, and respond to their environment — and then explored computer vision, which allows machines to “see” and interpret the world like we do with our eyes and brain.
I’ll break down my key learnings, reflections, and future outlook in a structured way.
The course started by framing sensors as the bridge between the physical world and the digital brain of a robot. Without them, robots can’t:
Detect obstacles.
Know where they are in space.
Measure how fast they’re moving.
Recognize when they’ve touched or grasped something.
This clicked instantly for me. I had always seen robots as machines that “just work,” but I hadn’t realized that every intelligent action comes from a flow of sensory input.
The instructor used a great analogy: sensors are to robots what the five senses are to humans. But unlike us, robots are not limited to five — they can have dozens, each designed for a very specific measurement.
This was one of the most fascinating parts of the course. We didn’t just study them theoretically — we explored use cases, how they’re integrated, and their importance in different robotic systems.
These are used to detect the presence of objects without physical contact. Think of an automatic door at a mall — it “knows” when you’re there. In robotics, proximity sensors help robots avoid bumping into walls or colliding with objects.
Example: In warehouse robots, proximity sensors stop them from running into shelves or humans.
Both of these are widely used in small-scale robotics projects and even in real-world robots.
IR sensors detect reflected infrared light, good for short-range detection.
Ultrasonic sensors (like a bat’s echolocation) measure distance by sending out sound waves and calculating their return time.
Example: Roomba vacuum cleaners use ultrasonic sensors to navigate around furniture.
These are like a robot’s “skin.” They allow it to know when it has touched something and with how much force.
Without these, robotic arms might crush delicate objects like a tomato while trying to pick it up.
Example: Modern prosthetic limbs use tactile sensors to provide feedback to the user.
These measure orientation and acceleration.
The accelerometer tells the robot how quickly it’s speeding up or slowing down.
The gyroscope keeps track of its orientation (tilt, rotation, angle).
Example: Drones heavily rely on these to stay stable mid-air.
These are attached to motors or wheels to measure how much they’ve rotated. This is essential for odometry, where a robot estimates its position based on wheel rotations.
Example: Delivery robots like those used by Amazon track how far they’ve traveled using encoders.
This is where sensors transition into the world of computer vision. Unlike proximity or tactile sensors, cameras provide a rich stream of visual data that needs processing.
Example: Self-driving cars use multiple cameras to detect lanes, pedestrians, and traffic signals.
The leap from sensors to computer vision was huge for me. Sensors give “numbers” — distances, forces, speeds. Cameras give images, which are far more complex. The course explained that computer vision is about teaching machines to interpret these images in meaningful ways.
I broke down my learnings into layers:
This is simply capturing the image through a camera. It sounds easy, but factors like lighting, camera quality, and frame rate can dramatically affect results.
Here, raw images are cleaned up and prepared for analysis. Things like noise reduction, edge detection, and filtering happen at this stage.
This is where the system identifies “important” parts of an image — edges, shapes, colors, or patterns. For example, detecting a red traffic light or the edge of a road.
This is one of the most powerful applications. Using algorithms (and increasingly AI/ML models), robots can detect objects like humans, cars, tools, or even specific defects in a factory product.
Robots don’t just recognize objects; they track how they move and understand context. For instance, a robot car can tell if a pedestrian is crossing the road or standing still.
One of the highlights of the course was connecting sensors and vision to real applications:
Autonomous Vehicles: They combine lidar, radar, cameras, ultrasonic sensors, and AI for safe driving.
Healthcare Robotics: Surgical robots use vision systems to navigate inside the human body with precision.
Agricultural Robots: Drones use cameras and sensors to monitor crop health and detect weeds.
Industrial Automation: Vision systems inspect assembly lines, catching defects faster than humans.
Humanoid Robots: Sensors allow them to balance, walk, and interact naturally with humans.
These examples made me realize robotics isn’t about “cool tech” alone — it’s about solving real problems
At first, I thought sensors were just “extra hardware” and vision was just “cameras.” But today I realized:
Sensors are the reason robots can interact safely and effectively.
Vision is what takes robotics from mechanical automation to intelligent autonomy.
The big takeaway for me was how interconnected everything is. A sensor gives raw data. A vision system interprets. The control system makes decisions. And the kinematics and trajectory planning execute it.
After completing this certification today, my mind is buzzing with possibilities. I want to:
Experiment with simple sensor-based robots (maybe an obstacle-avoiding bot using ultrasonic sensors).
Dive deeper into OpenCV (the most common computer vision library).
Explore AI-powered vision (object detection with YOLO or TensorFlow).
Eventually, combine my learnings into a project like a robotic arm that can see and sort objects.
Learning about sensors and computer vision felt like unlocking a new dimension in robotics. Before this, robots in my head were just machines that moved according to code. Now, I see them as entities that can perceive, analyze, and respond — almost like living beings with artificial senses.
This part of the certification didn’t just add knowledge; it gave me perspective. It showed me that robotics is not only about moving parts but also about how intelligently those parts interact with the world.
When I started this certification course in robotics engineering, I thought I was only going to learn about gears, motors, and maybe a bit of coding. What I didn’t expect was to walk away with a sense of how everything in robotics fits together like a living ecosystem. The basics, the kinematics, the trajectory planning, the sensors, and the vision modules were not just separate topics — they were puzzle pieces. And today, as I finished this course, I finally understood how those pieces lock into each other to create something much bigger: a robot that can actually sense, decide, and act.
This final part is about integration, soft skills, and reflection. It’s where I put the puzzle together and share how this short certification gave me not only technical awareness but also personal insights into what it means to think like a roboticist.
One of the biggest lessons I learned today is that robots are not about individual parts. They are about the connections between those parts.
Basics gave me the hardware skeleton — motors, joints, actuators, microcontrollers.
Kinematics gave me the rules of movement — the mathematical language of joints, degrees of freedom, and motion limits.
Trajectory planning gave me the brain’s sense of intention — how to move from Point A to Point B with purpose and smoothness.
Sensors gave me the nervous system — the ability to touch, hear, and feel the world.
Vision gave me the eyes and perception — the ability to see, recognize, and react.
The integration is where you stop seeing them as modules in a course and start seeing them as parts of a body. You don’t just say “this is a motor” or “this is a sensor.” You say: “The motor moves because the kinematics calculated the angle, the trajectory planned the path, the sensor confirmed the environment is safe, and the vision system recognized the object.”
That’s the leap this course gave me — moving from parts to systems
Another insight I gained was about feedback loops.
Every robot has some form of loop:
Sense → Sensors or vision capture information.
Think → The processor (using kinematics, trajectory, algorithms) decides what action to take.
Act → Motors, actuators, and joints move.
Check again → Sensors verify if the action was correct.
This continuous cycle — sense, think, act, repeat — is what makes a robot feel alive. Without it, a robot is just a machine repeating pre-programmed motions. With it, a robot adapts, corrects itself, and responds to change.
I realized that everything I studied in the course was a building block of this loop. And now when I look at a robotic arm, a drone, or even a self-driving car, I can see the invisible feedback loops running inside.
One surprising thing this certification showed me is that robotics is not just about coding or equations. It’s also about mindset and soft skills.
Problem-solving: Robots rarely work perfectly the first time. You need to approach problems step by step, asking: is it the sensor? Is it the calibration? Is it the trajectory algorithm?
Debugging patience: Sometimes a robot doesn’t move not because the math is wrong but because a loose wire isn’t connected. That patience to check everything calmly is a soft skill in itself.
Systems thinking: Robotics forces you to think about the bigger picture. If the vision module is lagging, maybe it’s not just a camera issue but also a processing bottleneck.
Communication & teamwork: Even in a short course, I realized robotics is rarely a solo activity. Engineers, programmers, designers, and testers all have to talk to each other.
Creativity: Robotics is imagination grounded in engineering. A robot vacuum wasn’t obvious until someone asked, “Why can’t we make a robot clean the floor?”
These soft skills are not written in the syllabus, but they are silently taught in every exercise, every module, and every error message.
Since I did this certification in one go today, my learning curve was steep. But that steepness itself was a gift.
In the basics, I learned that robotics isn’t mystical — it starts with simple parts.
In kinematics, I saw math come alive as motion.
In trajectory, I realized robots need planning, not just power.
In sensors, I understood how machines feel the world.
In vision, I got a glimpse of how they see it.
And in this final integration step, I now see how all of them work together to make something purposeful.
This doesn’t mean I am an expert. It means I now have a map. I know the areas I can dive deeper into — ROS (Robot Operating System), AI for vision, advanced control systems, swarm robotics, reinforcement learning. But none of that would make sense without this foundation.
When I signed up for this course, I thought I was learning robotics. But what I actually learned is that robotics is about imagination turned into motion.
It’s about seeing a problem in the real world and asking: Can a machine help solve this?
It’s about knowing the basics well enough to build.
It’s about using kinematics and trajectory to guide precision.
It’s about sensors and vision to give robots a connection to reality.
And it’s about integrating everything into a loop of continuous improvement.
Today, after completing this certification, I don’t feel like I know everything. I actually feel the opposite: I now know how much there is still to learn. But that’s the beauty of it. The basics gave me the language. The course gave me a map. And now the real journey begins.
Robotics is not just engineering. It’s a mindset — the belief that we can imagine something, break it into parts, integrate those parts, and make it move.
And that is what I carry with me from this course: robots are not just built with motors and code; they are built with curiosity, persistence, and the courage to try.
Akshat’s passion for marketing and dedication to helping others has been the driving force behind AkshatSinghBisht.com. Known for his insightful perspectives, practical advice, and unwavering commitment to his audience, Akshat is a trusted voice in the marketing community.
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