The ongoing revolution in robotic automation presents unparalleled opportunities and complex challenges for manufacturing industries. This thesis investigates optimization strategies for a fundamental robotic action: pick-and-place operations. Utilizing MATLAB's Robotics Toolbox, we devise detailed simulations, ranging from dual-robot static workflows (Scenario 1) to conveyor-integrated dynamism (Scenario 2). This research employs diverse optimization techniques that leverage both kinematic modeling and an analysis of robot dynamics for trajectory planning.
The outcome? Significantly shortened task execution times through iterative trajectory refinement and tailored adjustments to the simulated work environment. Results not only underscore the tangible impact of simulation-driven optimization, but also point towards conveyor speed control as a key avenue for driving further efficiency gains. These findings offer a framework for continued improvement, and aim at facilitating real-world implementation of these optimized, robotic motion-planning strategies across future manufacturing contexts.