Visual odometry vs SLAM: motion, maps, and metric scale
Compare visual odometry with SLAM, understand what local maps and loop closures add, and use a pinhole-camera inspector to see why monocular images cannot establish metric scale alone.
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Clear accounts of what was tried and learned.
Explanations, tutorials, build notes, and essays grounded in work that can be examined. Explore a concept, try an experiment, and follow the reasoning behind it.
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What is physical AI? Follow a robot from sensing to action
Follow a mobile robot from sensing to action. Compare physical AI and embodied AI, locate learned and classical methods, and calculate how measurement bias changes a stop.
Bode plots and loop shaping: place gain and phase together
Read Bode plots and shape a position-control loop with a lead controller. Calculate crossover and phase margin, compare closed-loop tracking, and check what the model leaves out.
Cascade control: let position request velocity and velocity request torque
Build cascade control from nested position and velocity loops. Follow command units, compare finite inner dynamics with ideal velocity tracking, and calculate the effect of an opposing load.
Control bandwidth: tracking speed and physical limits
Calculate closed-loop bandwidth relative to DC gain, then compare sinusoidal tracking with the torque it requires. Separate bandwidth from loop crossover and account for resonance, delay, sampling, and noise.
Controller discretization: sample, compute, and hold
Explore controller discretization with exact held-input dynamics. Compare immediate and delayed commands, calculate discrete poles, and connect sample timing to control stability.
Derivative kick and filtering: choose what D responds to
Explain derivative kick and filtering with a target step and measured noise. Compare derivative on error with derivative on measurement, calculate filtered command peaks, and weigh noise gain against lag.
Feedforward control: predict torque, then correct error
Calculate model-based torque from a smooth speed reference, then add feedback correction. Compare feedforward, proportional feedback, and their combination under inertia error, drag error, and unknown external torque.
How to become a robotics engineer in 2027: a roadmap built from job postings
Choose a robotics role, build the skills it asks for, and show the work. A practical roadmap with checked job examples, project ideas, and posted pay.
Integral windup: what happens when the actuator runs out of effort
Explore integral windup in a sampled PI controller. Compare requested effort, actuator limits, and integral memory as an impossible reference returns to a feasible value.
PID control: build a command from error and measured motion
Build a PID controller from proportional, integral, and derivative terms. Compare tracking and load rejection, calculate each command contribution, and check the stability limit of integral gain.
PID tuning: calculate gains, then test the response
Tune a PID controller against explicit response and effort targets. Convert Ziegler–Nichols settings into parallel gains, understand relay auto-tuning, and compare manual adjustments.