Autonomous Indoor Robot

Robotics were making advancements in the past decade and now, it can be used for localization and mapping of undetermined environment. The modern robot require installation of several devices in the like LIDAR, camera sensor, IR sensor, ultrasonic sensor for SLAM, detection and avoidance of obstacle

Project Title

Autonomous Indoor Robot

Project Area of Specialization

Artificial Intelligence

Project Summary

Robotics were making advancements in the past decade and now, it can be used for localization and mapping of undetermined environment. The modern robot require installation of several devices in the like LIDAR, camera sensor, IR sensor, ultrasonic sensor for SLAM, detection and avoidance of obstacles. SLAM based on the LIDAR techniques have been improved in the past years. We will use LIDAR SLAM for indoor navigation as it provide better localization with the help of 3D spatial information as compared to visual SLAM. The LIDAR SLAM robots are used in the industry and several techniques are used but the flaws are not discussed that may confuse the engineer. In this research several experiments are made for obstacle detection and avoidance using Raspberry Pi and LIDAR which will help us to make a suitable system for indoor navigation.

Project Objectives

The fundamental goal of this project is hardware implementation which can perform mapping and localization task in any environment. There are three main components in the hardware implementation. Raspberry pi is acting as a ROS Master and Teensy board instructs the motor drive to move the robot wheels and give encoders and IMU data as feedback to Raspberry pi and the ROS on PC acting as ROS Slave and publishing and Subscribing the different nodes related to command velocity, odometry, TF (Transform) and laser data. The ROS core and SLAM algorithm is running on raspberry pi which requires the LIDAR laser scan, IMU and encoders data to generate the odometry information using EKF and send command velocity to Teensy.

Project Implementation Method

The fundamental goal of this project is hardware implementation which can perform mapping and localization task in any environment. There are three main components in the hardware implementation. Raspberry pi is acting as a ROS Master and Teensy board instructs the motor drive to move the robot wheels and give encoders and IMU data as feedback to Raspberry pi and the ROS on PC acting as ROS Slave and publishing and Subscribing the different nodes related to command velocity, odometry, TF (Transform) and laser data. The ROS core and SLAM algorithm is running on raspberry pi which requires the LIDAR laser scan, IMU and encoders data to generate the odometry information using EKF and send command velocity to Teensy. The PC sends the command velocity to Raspberry pi and generate a visualization on RViz of the robot location and map after subscribing the nodes of odom (odometry), TF, and lase scan which is published by ROS Master running on Raspberry pi. Next Teensy board receives the command velocity instruction for the movement of the robot wheels. It first converts the velocity information to PWM signal for motor drive with the help of PID controller and verify the movement encoders feedback. The circuit is powered by the Li-ion battery to provide continuous and stable and voltages. The three main components and their explanation is given as ? Raspberry pi (ROS Master) ? PC (ROS Slave) ? Teensy (Control Algorithm)

Benefits of the Project

The applications and industrial use of the SLAM robot is given below ? Robot can be use to explore the urban environment ? Robot can be use to autonomously pick and place the product in the industries ? Robot with 3D SLAM can be use to autonomous navigation on the Roads ? Robot can be use for autonomous vacuum cleaner for house hold application ? SLAM algorithm can be use for autonomous navigation of drones ? Visual 3D SLAM can be use for autonomous tracking system ? Visual 3D SLAM can be use for autonomous vehicle driving ? Visual 3D SLAM can be use explore the land mines ? SLAM robot can be use map the under water land ? SLAM robot can be use for autonomous car parking system ? SLAM robot can be use for autonomous aerial mapping in the dynamic environment

Technical Details of Final Deliverable

In the hardware implementation of the SLAM robot is our main task. As, we have already design the SLAM, gmapping and AMCL algorithms in the simulation part of this project. First of all we assemble the robot chassis and attached the motors. Hardware Implementation of SLAM Robot 34 all the other components with the robot chassis. Then we have followed the procedure shown in the Figure 5.10 to complete the hardware implementation of SLAM Project. 

This is the first step of hardware implementation. First we boot up the Raspberry pi with Ubuntu version 18.04 and create a SSH connection with PC to create a headless setup, as Pi is mounted on the mobile robot. After the setting up the SSH connection we installed the ROS melodic version on Raspberry pi and create a workspcae in pi ROS same as we created on the ROS PC while simulating the SLAM. After that we install all the packages required for SLAM implementation. 5.6.2 Network Setup between PC and Pi ROS In the Network setup first we declare our Pi ROS as ROS master by defining the IP address and port number for communication between PC and Pi ROS and define the Host-name of ROS pi same as its IP. After that on the PC side, we first define the ROS Master using its IP and port number and at the end we declare the PC IP address as the Host-name for ROS PC. That’s how we created our network setup between PC and Pi to publish and subscribe the topic massages. 5.6.3 Save the Serial Ports of LIDAR and Teensy As we want to connect the LIDAR and Teensy board to Raspberry pi serially via USB interface. So, we need to to save the COM port numbers by defining the name to it with changing the uDev rules

Final Deliverable of the Project

Hardware System

Core Industry

IT

Other Industries

Core Technology

Internet of Things (IoT)

Other Technologies

Sustainable Development Goals

Required Resources

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