Theory & Background

SCARA (Selective Compliance Assembly Robot Arm) has 2 rotary joints in horizontal plane (θ1, θ2) + vertical linear axis (Z) + end-effector rotation (θ4). Horizontal IK: given target (x,y), compute θ2 = ±acos((x²+y²-L1²-L2²)/(2L1L2)). θ1 = atan2(y,x) - atan2(L2sin(θ2), L1+L2cos(θ2)). Implement both elbow-up and elbow-down configurations. Generate smooth trajectories using trapezoidal velocity profiles for stepper control.

Components & Requirements

10 components required for this project.

#	Component	Purpose	Qty
1	SCARA Robot Kit (Delta 4-DOF)	Pick and place mechanism	x1
2	Stepper Motors (NEMA 23)	SCARA arm joints	x3
3	Linear Actuator (Z-axis)	Vertical pick motion	x1
4	Conveyor Belt (12V motor drive)	Object transport	x1
5	Webcam (Logitech C920)	Object detection	x1
6	Raspberry Pi 4	Vision processing	x1
7	Arduino Mega + RAMPS 1.4	Stepper motor control	x1
8	Vacuum Pump + Suction Cup	Object picking tool	x1
9	Proximity Sensor (inductive)	Object detection on belt	x2
10	Sorting Bins (3 positions)	Sorted object destinations	x3

Step-by-Step Implementation

Follow these 3 steps carefully.

1

SCARA Kinematics

SCARA (Selective Compliance Assembly Robot Arm) has 2 rotary joints in horizontal plane (θ1, θ2) + vertical linear axis (Z) + end-effector rotation (θ4). Horizontal IK: given target (x,y), compute θ2 = ±acos((x²+y²-L1²-L2²)/(2L1L2)). θ1 = atan2(y,x) - atan2(L2sin(θ2), L1+L2cos(θ2)). Implement both elbow-up and elbow-down configurations. Generate smooth trajectories using trapezoidal velocity profiles for stepper control.

2

Object Detection and Classification

Use OpenCV + MobileNet SSD for multi-class object detection on conveyor. Alternatively, train custom classifier with 3 object classes (red cylinder, blue cube, green sphere) using transfer learning (TensorFlow + ResNet50). Detect object position in camera image, transform to robot workspace coordinates using camera calibration matrix (pixel → mm on conveyor surface using checkerboard calibration + known camera height).

3

Conveyor Synchronization

Object moves on conveyor while robot picks. Measure conveyor speed using a rotary encoder on belt roller. When proximity sensor detects approaching object, calculate: time to reach pick zone, projected pick zone position accounting for belt movement during robot move time. Adjust pick target position to meet object at the right instant — similar to how a goalkeeper anticipates ball trajectory.

Code & Implementation

Core code for pick_place_vision.py:

pick_place_vision.py
  Python

import cv2, numpy as np

def detect_objects(frame):
    """Simple color-based object detection"""
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    objects = []
    # Red objects
    mask_r = cv2.inRange(hsv, (0,120,70), (10,255,255))
    mask_r |= cv2.inRange(hsv, (170,120,70), (180,255,255))
    contours, _ = cv2.findContours(mask_r, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    for cnt in contours:
        if cv2.contourArea(cnt) > 500:
            M = cv2.moments(cnt)
            cx = int(M['m10']/M['m00']); cy = int(M['m01']/M['m00'])
            objects.append({'color': 'red', 'pixel': (cx, cy)})
    return objects

def pixel_to_robot(px, py, H):
    """Convert image pixel to robot coordinates using homography H"""
    pt = np.array([[[px, py]]], dtype=np.float32)
    robot_pt = cv2.perspectiveTransform(pt, H)
    return robot_pt[0][0]

Testing & Troubleshooting

Test Pick and Place Industrial Robot by verifying each subsystem individually before full integration.

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Troubleshooting Tips

Verify power voltages, check ground connections, use serial monitor for debug.

Real-World Applications

*Electronics PCB component placement

*Food sorting and packaging

*Pharmaceutical pill sorting

*Semiconductor chip handling

*Postal parcel sorting

*Agriculture produce grading

*Quality inspection and rejection

*Toy and consumer product assembly

Extensions & Next Steps

Implement deep learning for defect detection during pick
Add force feedback on gripper for fragile object handling
Build dual-arm system for assembly operations
Implement bin picking from random pile using depth camera
Add conveyor tracking for continuous-motion picking without stopping belt

Interactive Playground

Coming Soon

An interactive simulator will be available here — simulate circuits and run code in-browser without hardware.

Frequently Asked Questions

What is the cycle time of a typical pick and place operation?

For a SCARA robot picking from a conveyor: approach to object (~0.5s), pick (vacuum on + 0.2s), retract (0.3s), move to bin (0.5–1.0s depending on distance), place (release vacuum + 0.2s), return to pick position (0.5s). Total cycle: 2.5–3.0 seconds per pick, or 20–24 picks per minute. Industrial robots achieve 0.5–1.5 second cycle times with optimized path planning and simultaneous motion between axes.

Introduction