Optimizing robotic motion in dynamic manufacturing environments

Abstract

The field of robotics has been a trending technology over the years due to its ability to revolutionize industries. This study highlights the role of optimized robotic motion in enhancing productivity in dynamic manufacturing environments using MATLAB simulations. By modeling the arrival of manufactured parts in batches via a conveyor system governed by a negative exponential distribution in a Poisson process, MATLAB is employed to design optimal robotic trajectories for pick-and-place operations. The research carefully analyzes parameters such as arrival rates and cycle times to manage the stochastic nature of part delivery. The result reveals a significant improvement in operational efficiency, with throughput increasing by up to 20% due to real-time optimization of robotic motion. The non-linear relationship between throughput and arrival rates highlights the system’s complexity, with optimal conditions observed at specific arrival rates, such as 0.16 s for peak efficiency. MATLAB’s Polynomial Trajectory Planning tool generates smooth, continuous paths, ensuring that robotic operations dynamically adapt to changing conditions. This foundation supports future innovations in robotic system integration and automated production lines, offering a significant step forward in the application of advanced simulation tools an advanced manufacturing environment.