Free ebookSensors in Robotics: From Signals to Reliable Measurements

Course Description

Sensors in Robotics: From Signals to Reliable Measurements is a practical robotics course designed to help you turn raw sensor signals into trustworthy data for real-world robot behavior. If you are building mobile robots, robotic arms, or interactive systems, you will learn how physical quantities become digital readings and how to interpret those readings with confidence in noisy environments.

Through progressive learning, you will connect core sensor principles to everyday robotics tasks like motion estimation, obstacle detection, navigation, and safe interaction. You will explore how encoders support position, velocity, and odometry reliability, how IMU sensors combine accelerometers and gyroscopes for orientation estimation, and why sampling, timing, and synchronization can make or break a robot sensor system. You will also learn how ultrasonic range sensing, infrared proximity sensing, and optical time of flight sensors behave in practice, including common failure modes and surface dependencies that affect distance measurement.

Robotics perception goes beyond range sensors, so the course also connects cameras in robotics with the idea of extracting measurements from pixels, and it covers force and tactile sensors for contact detection, load awareness, and interaction safety. You will develop an intuition for noise, bias, and uncertainty, then apply calibration concepts such as offsets, scale factors, and frame alignment to improve accuracy. Finally, you will learn filtering sensor data with approachable methods like moving averages and complementary filters, and understand sensor fusion in robotics as a conceptual way to combine measurements into more reliable estimates.

Whether you are selecting sensors for a robotics project or improving measurement quality in an existing system, this course helps you make better engineering decisions and build more dependable robots. Start learning today and earn your free certificate as you move from signals to reliable measurements.