AI_Controlled_Robotic_Arm_Presentation.pptx

Briefly summarise the main points. short, simple sentences with a few keywords are best

1. AI-Controlled Robotic Arm with Object Recognition Course: Artificial Intelligence in Robotics Team Members: [Your Names & Roll Numbers] Guide: [Instructor Name] Objective: To design a low-cost robotic arm that can detect and classify objects using AI vision and manipulate them automatically using inverse kinematics. Key Technologies: Python, OpenCV, Arduino, Servo Control Total Cost: ₹2500–₹3200

2. System Architecture System Flow: Camera → Object Detection (Python + OpenCV) ↓ Object Classification (Color / CNN) ↓ Inverse Kinematics Calculation ↓ Servo Angle Commands (via Serial) ↓ Robotic Arm Movement (Arduino) Hardware: Arduino Uno, 3–4 Servos, Camera, Acrylic Frame Software: Python, OpenCV, PySerial, Arduino IDE

3. Object Detection & AI Module Techniques Used: - Color-based detection (HSV threshold + contour) - Shape detection (contour approximation) - Optional CNN classification Process: 1. Capture camera frame 2. Detect and mark object 3. Calculate (x, y) coordinates 4. Send to Arduino for motion Expected Accuracy: ~90%

4. Motion Control & Kinematics Inverse Kinematics Equations: θ₂ = cos⁻¹((x² + y² - L₁² - L₂²) / (2L₁L₂)) θ₁ = tan⁻¹(y/x) - tan⁻¹((L₂sinθ₂)/(L₁+L₂cosθ₂)) Implementation: - Python computes angles - Serial data sent to Arduino - Servos control θ₁, θ₂, θ₃ Testing Results: - Motion error < 5% - Response delay < 1 sec

5. Results, Challenges & Applications Results: ✔ Detects and sorts objects autonomously ✔ Real-time control via Python-Arduino ✔ Smooth inverse kinematic movement Challenges: - Servo jitter (fixed with smoothing) - Lighting variation (adaptive HSV) - Limited workspace (calibration) Applications: - Smart manufacturing - Warehouse automation - Robotics education

6. Conclusion & Future Scope Conclusion: - Demonstrated AI-powered robotic arm using low-cost hardware. - Integrated vision, kinematics, and embedded control. - Achieved intelligent motion control in real-time. Future Enhancements: - Gesture or voice control - AI learning for multiple object types - Simulation in Gazebo / PyBullet "Robots with vision bring intelligence to motion."