Raspberry-Pi-based Pick and Place Robotic Arm for the Chemical Industry
Objective
The objective of this project is to develop a Raspberry-Pi-based robotic arm to safely handle hazardous chemicals in the chemical industry. The system automates container transfer with 360-degree movement, controlled via Python, and integrates IoT for real-time monitoring and remote access, ensuring efficiency and safety.
Abstract
The Raspberry-Pi-Based Pick and Place Robotic Arm for the Chemical Industry is an intelligent automation system designed to improve material handling and sorting processes in industrial environments. The system utilizes a Raspberry Pi as the main controller and a USB camera for real-time object detection and classification. An Arduino is used to control the robotic arm movements through DC motors and motor drivers. Based on the detected object type, the robotic arm automatically picks and places chemical-related items into their respective designated segments. A Bluetooth module enables wireless monitoring and control of the system. The integration of image processing and automation reduces human intervention in handling industrial materials, improves operational efficiency, and minimizes the risk associated with manual sorting. The proposed system provides a cost-effective and reliable solution for chemical industries requiring automated segregation and material management.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Specifications
Hardware components:
- Raspberry Pi
- SD Card
- USB Camera
- Arduino Uno
- Robotic Arm
- DC Motors (4)
- Motor Drivers (2)
- Bluetooth Module
- Power Supply (2)
- 12V Adapter (2)
- Connectors – 30
Software components:
- Python
- Raspbian OS
Learning Outcomes
- Understand Raspberry Pi architecture and GPIO configuration
- Learn how to install and configure Raspbian OS and required Python libraries
- Interface analog sensors with Raspberry Pi using MCP3008 ADC
- Implement image classification using Artificial Neural Networks
- Develop real-time skin analysis using USB camera input
- Build automated health screening systems with display and alert features
- Integrate temperature and heartbeat monitoring in diagnostic systems
- Analyze and interpret classification output for healthcare applications
- About Project Development Life Cycle:
- Planning and Requirement Gathering (software’s, Tools, Hardware components, etc.,)
- Schematic preparation
- Code development and debugging
- Hardware development and debugging
- Development of the Project and Output testing
- Practical exposure to:
- Hardware and software tools.
- Solution providing for real time problems.
- Working with team/ individual.
- Work on Creative ideas.
- Project development Skills:
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
