Autonomous Multitasking Agent include path planning obstacle avoidance and image processing

Autonomous Multitasking Agent is a robot that takes decision on its own. It uses stereo camera as its main sensor to generates disparity map and detect  the distance of the obstacle and pass its data to SLAM(Simultaneous Localization And Mapping) algorithm to generate the map of unknown en

Project Title

Autonomous Multitasking Agent include path planning obstacle avoidance and image processing

Project Area of Specialization

Robotics

Project Summary

Autonomous Multitasking Agent is a robot that takes decision on its own. It uses stereo camera as its main sensor to generates disparity map and detect  the distance of the obstacle and pass its data to SLAM(Simultaneous Localization And Mapping) algorithm to generate the map of unknown environment. The algorithm takes decisions and deliver the pulses (current) to stm32 Microcontroller to run the omni-wheels.

Project Objectives

The main objective of this project is to implement autonomous mobile agent. The system will use either stereo camera as its main sensor for distance evaluation, path planning, and obstacle avoidance.

Project Implementation Method

Design and implement the robot circuity

Components Required:

• Raspberry pi 4 model B
• 2 Raspberry pi Cameras
• 1 STM32f103c8 microcontroller
• 3 NH5019a Motor Driver
• 3 Quadrature Encoder Motors
• Lipo 3S battery
• USB to Serial TTL module

Raspberry Pi 4 is the main processor to compute SLAM and obstacle avoidance Algorithm. Connection are as follows:

•  Stereo pi (Stereo camera) is connected to raspberry pi to detect obstacle and generate disparity map.
•  STM32f103c8 is connected to raspberry pi 4 via serials.
• 3 Motor Drivers are connected to STM32f103c8.
• 3 Motors are connected to Motor Drivers.
• Motor Encoders are connected to STM32F103c8.

Implementation of SLAM algorithm

The SLAM algorithm is assumed to simultaneously create a map of the mobile robot environment as well as calculating the position within this map. The environmental learning determined by SLAM algorithm of the mobile robotic autonomous tasks. SLAM only has been actively discussed by using cameras because the sensor configuration is simple and the technical difficulties are higher than the others.

Implementation of obstacle avoidance algorithm

Obstacle avoidance method is designed and concern to an experimental autonomous ground vehicle system. This algorithm is easy to tune and it takes into consideration the field of view as well as it’s easy to test in both simulated and real-time of the mobile. Also, it’s very inefficient and therefore various improvements have been proposed.

Benefits of the Project

• The main benefits of this project is its self-driving techniques in transportation.
• Used in restaurants as robot waiters.
• Run in mobile environment without colliding to any obstacle.
• Used as surveillance robot.

Technical Details of Final Deliverable

• Design Omni-wheel Embaded System (Robot circuity) 
• SLAM Algorithms implementation in python.
• Obstacle Avoidance Algorithm implementation in python.
• Autonomous agent run in the moblile environment without collision.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Transportation

Other Industries

Security

Core Technology

Robotics

Other Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Industry, Innovation and Infrastructure, Responsible Consumption and Production, Partnerships to achieve the Goal

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com