This blog post explores a Mechanical Engineering Master’s thesis on designing and building an omnidirectional automotive mobile robot for industrial logistics.
Master’s Thesis Focus: Robot for Industrial Use
Imagine a robot that seamlessly integrates into factory environments, navigating busy aisles and assisting with various tasks. This thesis tackles this challenge by developing an industrial logistics robot with the following capabilities:
- Omnidirectional Movement: The robot can move and turn in any direction, making it highly maneuverable in tight spaces.
- Heavy Load Handling: With a 100 kg capacity, the robot can efficiently transport materials within the workplace.
- Dynamic Environment Navigation: Equipped with advanced sensors, the robot can navigate through dynamic environments with both stationary and moving obstacles.
Building the Robot: Hardware and Software Integration
The thesis delves into the hardware and software components that bring this robot to life:
- Hardware: The robot utilizes a powerful combination of a myRIO micro-controller for precise motor control and a Raspberry Pi 4 for running the Robot Operating System (ROS). This setup enables real-time data processing and communication.
- Software: Leveraging LabVIEW for myRIO programming and ROS for Raspberry Pi programming, the thesis explores user-friendly development tools with extensive functionalities for robot applications.
- Sensors: Multiple Lidar sensors provide accurate distance measurements, while an IMU (inertial measurement unit) and wheel encoders offer crucial data on robot orientation, speed, and obstacle proximity.
Algorithms for Intelligent Navigation
The thesis explores various algorithms for mapping the environment, planning optimal paths, and precise robot localization:
- SLAM (Simultaneous Localization and Mapping): This technique builds a real-time map of the environment while simultaneously estimating the robot’s position within it.
- RRT (Rapidly Exploring Random Tree): This algorithm efficiently plans feasible and optimal paths for the robot to navigate within the mapped environment.
- AMCL (Adaptive Monte Carlo Localization): This technique utilizes a particle filter to pinpoint the robot’s location with high accuracy based on sensor data and its movement patterns.
Conclusion: A Thesis with Practical Applications
This Master’s thesis presents a well-designed and functional omnidirectional mobile robot for industrial logistics. This innovative solution has the potential to revolutionize factory workflows, improving efficiency and safety in various industrial settings.
