Waste Classification Using Machine Learning and Robotic Arm

Published  December 24, 2024   



This project introduces an innovative solution to automate waste segregation using a SCARA Robot enhanced with computer vision and machine learning. The project used a pre-deployed AI model to classify the objects into organic or inorganic waste. At its core, the project utilises a Raspberry Pi paired with a USB camera to capture images of waste materials, this captured image is then processed with the help of inference SDK and the Roboflow waste classification API. Once classified, the robotic arm, controlled by an ESP32-C6 WROOM module, handles the waste, placing it into designated bins. This integration of robotics and artificial intelligence not only enhances automation but also contributes to effective waste management solutions.

The SCARA robot retains its compact and precise design, featuring NEMA 17 stepper motors powered by TMC2209 drivers for smooth operation, alongside SG90S servo motors for precise gripper control. The robust power system includes an LM2596-5 buck converter and an ADP7118AUJZ-3.3 LDO, ensuring the reliable operation of the ESP32-C6 and its peripherals. The Raspberry Pi handles image capture, and ML inferencing, leveraging the RoboFlow platform API for real-time inference. A Python-based control script on the Raspberry Pi seamlessly interfaces with the ESP32-C6 via serial communication to coordinate waste classification and robotic arm movements. This project exemplifies the potential of combining robotics and AI to solve real-world challenges. It provides a hands-on platform for exploring automation, embedded systems, and machine learning applications. The system’s modular design and open-source development ensure.

Code File

Code and Schematics Waste Classification - SCARA Robotic ArmCode and Schematics Zip File of Waste Classification - SCARA Robotic Arm

PCB Gerber File

PCB Geber File Waste Classification - SCARA Robotic ArmPCB Geber ZIP File Waste Classification - SCARA Robotic Arm


 
Value Manufacturer DigiKey Part Number Datasheet Link Quantity
330uF Chinsan (Elite) 4191-UGS1V331MP51016UCT-ND

Datasheet

2
10uF Samsung Electro-Mechanics 1276-1096-1-ND

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2
0.1uF YAGEO 311-1088-1-ND

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4
2.2uF Samsung Electro-Mechanics 1276-1134-1-ND

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1
SS34 Comchip Technology 641-2115-1-ND

Datasheet

2
ADP7118AUJZ-3.3-R7 Analog Devices 505-ADP7118AUJZ-3.3-R7CT-ND

Datasheet

1
Screw_Terminal_2_P5.00mm Phoenix Contact 277-12547-ND

Datasheet

1
Barrel_Jack_Switch GlobTek, Inc. 1939-G-1005B-ND

Datasheet

1
USB_C_Receptacle_USB2.0 Assmann WSW Components 123-A-USBC-20F0-EA-GSR11CT-ND

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1
47uH Sumida America Components Inc. 308-1339-1-ND

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1
150505M173300 Harvatek Corporation 3147-T3A83RGB-20C001011U1930CT-ND

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1
0R Stackpole Electronics Inc RMCF0603ZT0R00CT-ND

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1
1K YAGEO 311-1.00KHRCT-ND

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11
10K Stackpole Electronics Inc RMCF0603JT10K0CT-ND

Datasheet

8
47K YAGEO 311-47.0KHRCT-ND

Datasheet

1
RST CIT Relay and Switch 2449-CS1213AGF160CT-ND

Datasheet

2
LM2596S-5 UMW 4518-LM2596S-5.0CT-ND

Datasheet

1
MAX6899AAZT+T Analog Devices Inc./Maxim Integrated MAX6899AAZT+TCT-ND

Datasheet

1
ESP32-C6-WROOM-1 Espressif Systems 1965-ESP32-C6-WROOM-1-N8CT-ND

Datasheet

1
TMC2209 STEPPER DRIVER BOARD Analog Devices Inc./Maxim Integrated 505-TMC2209SILENTSTEPSTICK-ND

Datasheet

4
Raspberry Pi 3 Model B+ Raspberry Pi 2648-SC0073-ND

Datasheet

1
USB 2.0 MINI WEBCAM Seeed Technology Co., Ltd 402990004-ND

Datasheet

1