Development of a mobile robot performing transport implementations in a manufacturing plant

daha sonra doldurulacaktırThis thesis investigates the development of a mobile robot engineered to enhance material transportation processes and improve efficiency within contemporary manufacturing facilities. The primary aim is to address the inefficiencies and errors inherent in manual material handling by implementing advanced robotics and automation technologies. The research encompasses several comprehensive stages: mechanical design, sensor technology integration, and software development, all coordinated through the Robot Operating System (ROS) framework. The thesis commenced with defining the robot's specifications based on the operational requirements of the manufacturing environment. This was followed by the creation of a detailed mechanical design using 3D parametric modeling to ensure the robot met the necessary specifications for payload capacity, maneuverability, and adaptability to factory floor conditions. The design phase included mechanical static structural analysis and topology optimization, ensuring structural integrity with total deformation maintained below 0.2 mm under maximum load conditions. A skid steering movement system was chosen for its reliability and efficiency in navigation. In the electronic integration phase, various advanced sensors were selected to provide essential data acquisition and control capabilities for autonomous operation. Key components included LIDAR for precise navigation, IMU sensors for orientation, encoders for movement tracking, and stereo cameras for enhanced environmental perception. These sensors were integrated with microcontrollers and an onboard computer, facilitating the necessary processing power for the robot's tasks. Wireless communication between the microprocessor and microcontroller was also established to ensure seamless operation. The software development phase involved creating robust algorithms for path planning, obstacle avoidance, and task execution. The ROS framework was utilized to facilitate communication between hardware components and develop control algorithms. Specific tools within ROS, such as Gmapping for mapping, Adaptive Monte Carlo Localization (AMCL) for positioning, and a Hybrid A* algorithm for path planning, were employed. An Extended Kalman Filter (EKF) was used to obtain accurate odometry data, essential for precise localization and navigation. Extensive simulations were conducted to refine these algorithms before real-world implementation. The final prototype underwent rigorous testing in both virtual and live production environments. Key performance metrics, such as carrying capacity, speed, accuracy, and reliability, were evaluated. The robot achieved an average transport speed of 0.5 m/s and a positioning accuracy within 5 cm, demonstrating its capability to navigate and avoid obstacles autonomously. It handled an average payload of 100 kg with a maximum deviation of less than 2% in path accuracy. Additionally, the robot's battery life supported continuous operation for up to 8 hours, proving its suitability for long shifts in manufacturing settings. The results of this study demonstrate that the developed mobile robot significantly improves operational efficiency and accuracy in material transport within manufacturing plants. Its ability to autonomously navigate and execute transport tasks with minimal human intervention represents a substantial advancement in industrial automation. This thesis not only highlights the robot's potential to optimize material handling processes but also provides groundbreaking insights into the practical application of robotics and automation technologies. The findings serve as a vital resource for researchers and industry professionals dedicated to enhancing industrial process efficiency and embracing the future of automated manufacturing.