Free ebook on robotics sensors, covering signals, calibration, filtering, fusion, timing, and reliable measurement design.
Free ebook content
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Sensor Signals in Robotics: From Physical Quantities to Digital Data
+ Exercise: Which approach best helps prevent aliasing when sampling a sensor signal that may contain high-frequency vibration?
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Encoders for Robot Motion: Position, Velocity, and Odometry Reliability
+ Exercise: A mobile robot needs to know each joint’s angle immediately after a power cycle without performing a homing routine. Which encoder choice best matches this requirement?
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IMU Sensors in Robotics: Accelerometers, Gyroscopes, and Orientation Estimation
+ Exercise: Why can an accelerometer help correct long-term roll and pitch drift but not yaw drift in an IMU-based orientation estimate?
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Ultrasonic Range Sensing: Time-of-Flight with Sound and Its Failure Modes
+ Exercise: In a robot with multiple ultrasonic range sensors, what is the most effective way to reduce false distance readings caused by cross-talk?
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Infrared Proximity Sensors: Reflectance-Based Distance and Surface Dependencies
+ Exercise: Why can two objects at the same physical distance produce different readings on a simple reflective IR proximity switch?
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Time-of-Flight Optical Sensors: Direct Distance with Photons
+ Exercise: When using optical time-of-flight (ToF) distance readings in a robot, what is the best first step to improve reliability before applying filtering or control logic?
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Cameras in Robotics: From Pixels to Measurements
+ Exercise: When a robot needs accurate geometry while the camera is moving quickly, which shutter type is typically the better choice and why?
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Force and Tactile Sensors in Robotics: Contact, Load, and Interaction Safety
+ Exercise: During calibration of a wrist-mounted 6-axis force/torque sensor, why is tool gravity compensation (or zeroing at a known pose) important?
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Noise, Bias, and Uncertainty: Turning Raw Readings into Reliable Measurements
+ Exercise: A robot computes velocity as v = (p2 − p1)/Δt using two position samples with independent noise variance σp². According to first-order uncertainty propagation, what happens to the velocity noise when Δt is made smaller?
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Sampling, Timing, and Synchronization in Robot Sensor Systems
+ Exercise: In a multi-sensor robot system, which practice best reduces systematic timing errors when aligning measurements from different sensors?
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Calibration Concepts for Robotics Sensors: Offsets, Scale Factors, and Frame Alignment
+ Exercise: Which activity is primarily part of geometric calibration rather than sensor model calibration?
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Filtering Sensor Data: From Moving Averages to Complementary Filters
+ Exercise: In a complementary filter for estimating tilt, what is the main role of the parameter β in the update equation?
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Sensor Fusion in Robotics: Conceptual Models for Combining Measurements
+ Exercise: In a prediction–correction sensor fusion loop, what is the main purpose of gating a sensor measurement before applying a correction?
About the free ebook
Sensors in Robotics: From Signals to Reliable Measurements
This free ebook explains how robots turn physical events—motion, distance, light, contact, and orientation—into usable digital measurements. It connects sensor principles with the practical decisions that determine whether a robot behaves predictably in the real world.
Build confidence in robot data
Explore the strengths and limitations of encoders, IMUs, ultrasonic and infrared sensors, optical time-of-flight devices, cameras, and force sensors. Learn why readings can drift, fluctuate, saturate, or fail when surfaces, lighting, vibration, timing, or mounting conditions change.
From raw signals to useful estimates
The ebook introduces noise, bias, uncertainty, calibration, sampling, synchronization, filtering, and sensor fusion in clear robotics-focused terms. Rather than treating a sensor value as unquestionable, it shows how to evaluate measurement quality and improve reliability.
Make practical design choices
Use these concepts to select sensors that fit a robot’s task, environment, accuracy needs, budget, and computing constraints. Whether you are building a mobile rover, robotic arm, drone, or interactive project, this guide helps you reason about what your robot can measure and how much to trust it.
What you will gain
- A practical understanding of common robotics sensing methods
- Awareness of real-world measurement failure modes
- A foundation for calibration, filtering, and fusion decisions
- Better criteria for choosing sensors for robotics projects
How do encoders affect robot odometry accuracy?
Encoders estimate wheel motion, but wheel slip, unequal wheel sizes, missed counts, and poor calibration can cause odometry drift.
Why can ultrasonic and infrared distance sensors give incorrect readings?
Ultrasonic sensors are affected by angle and soft surfaces, while infrared sensors depend strongly on target color, reflectivity, and ambient light.
What is the purpose of sensor fusion in robotics?
Sensor fusion combines complementary measurements to produce an estimate that is more stable, accurate, or complete than one sensor alone.
This ebook includes:
14 content chapters
Digital certificate of course completion (Free)
Exercises to train your knowledge
100% free, from content to certificate
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