Pick & Place

Start from raw joint targets and build up the full manipulation stack: PD control, forward kinematics, inverse kinematics, straight-line trajectories, and finally a robust pick-and-place program that survives a new layout on every run.

What you'll learn

• >Command joints through PD position controllers
• >Derive forward kinematics with trigonometry
• >Use and then write analytic inverse kinematics
• >Generate straight-line, minimum-jerk trajectories
• >Structure pick-and-place as pre-grasp → grasp → lift → place phases
• >Verify grasps and recover from failures

Before you start

You should be comfortable with basic Python: variables, loops and functions. We'll introduce the robotics and the maths as you go. The first visit downloads the physics engine and Python, about 20 MB, and later visits load from your browser's cache.

Steps

1. 1Joints, targets & PD controlCommand joint angles, let physics run, and meet the PD controller behind every industrial arm.Joint spacePD position controlSimulation timeOpen
2. 2Forward kinematicsPredict exactly where the gripper is from the joint angles, using nothing but trigonometry.Forward kinematicsLink framesTrigonometryOpen
3. 3Inverse kinematics & straight-line motionTurn positions into joint angles, then move the gripper along straight lines with smooth, jerk-free timing.Inverse kinematicsJoint vs. Cartesian spaceMin-jerk trajectoriesOpen
4. 4Your first pick & placeChain the classic manipulation phases into a program that moves the red ball into the cup.Grasp phasesApproach & retreatState machinesOpen
5. 5Clear the tableGeneralise your program: perceive every ball, pick each one, verify, retry, and survive a new layout on every run.PerceptionLoops & functionsFailure recoveryOpen
6. ★Bonus: build your own IKReplace the black box. Derive the analytic inverse kinematics with the law of cosines and run a full pick with it.Analytic IKLaw of cosinesWorkspace limitsOpen