Kyoto University robotics lecture notes: differential drive, odometry and Nav2 navigation basics
4310sy · x · 2026-09-03
Kyoto University's robot programming lecture notes (Intro 2) cover the fundamentals of wheeled robot mobility and navigation, paired with chomado's beginner path: assemble a differential-drive robot → control it with ROS → run autonomous navigation with Nav2.
Key points from the notes:
- Drive types compared: differential two-wheel drive (simple, cheap, turns in place, but hard to hold a straight line), car-like steering (handles outdoor terrain, complex path planning), and omnidirectional drive (moves and rotates freely, but expensive and complex).
- Odometry: pose (x, y, θ) updated via x += v·cos(θ)·T style equations from a kinematic model; speeds typically measured with optical wheel encoders.
- Error accumulation: uneven wheel diameters, rough floors and slippage cause drift that can't be corrected without other sensors — odometry alone is never enough for localization.
- Two position-control methods: the simple rotate–drive–rotate sequence (jerky, hard to correct drift) versus a smoother approach that computes the bearing with atan2(Δy, Δx) and controls angular error while holding constant speed.
- In ROS, odometry is usually computed internally and published on the /odom topic, so you rarely compute it from raw encoder data yourself.
A replier quips that the jump from step 2 (ROS control) to step 3 (Nav2 autonomy) is huge, reflecting the real difficulty of this learning path.
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