Motion Simulation of Coal Gangue Sorting Robot

Coal is one of the most important energy sources in China, and coal gangue is a part of the waste material produced in the process of coal production, which has less carbon content, and will lead to insufficient combustion of coal in the process of mixed combustion with coal. When coal and gangue are mixed together, the combustion efficiency of coal will be significantly reduced, and there will be a significant waste of resources. Moreover, incomplete combustion produces a large amount of harmful gases, such as sulfur dioxide, nitrogen oxides, and particulate matter, which contribute to air pollution and pose serious threats to human health. Traditional methods of coal gangue sorting, such as manual sorting and basic visual recognition combined with robotic arms, are not only inefficient and labor-intensive but also often inaccurate. Manual sorting, in particular, exposes workers to harsh and potentially dangerous underground environments, where they face risks from poor visibility, harmful gases, and physical strain. The process is time-consuming and prone to errors due to the repetitive nature of the work and the difficulty in distinguishing between coal and gangue in low-light conditions. Moreover, the traditional visual recognition systems used in combination with robotic arms often require manual calibration and adjustment, which further reduces efficiency and increases labor costs. These limitations highlight the urgent need for an automated and intelligent solution that can improve the accuracy, efficiency, and safety of coal gangue sorting. The development of a coal gangue sorting robot based on advanced parallel mechanisms, such as the Delta parallel robot, offers a promising solution to these challenges.