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Abstract In contemporary manufacturing processes, reliable but efficient pick-and-place robots are frequently used. The automation and optimization of the pick and place procedures utilizing various path-planning approaches thereby support the expansion of application areas. Yet, the design of a controller faces significant difficulties due to the nonlinearities inherent in robotic manipulators and the unpredictable nature of the ambient factors. In place of the classic model predictive control (MPC), this paper presents the application of the Nonlinear Model Predictive Controller (NLMPC) as an acceptable control mechanism for real-time optimization and robust stability of the KINOVA Gen3 robotic arm. The developed NLMPC-based method ensures that the robotic arm does not run into obstacles in the workplace or with itself while reaching, gripping, selecting, and placing the necessary items. To acquire the control input trajectory, the optimization in NLMPC is solved repeatedly. When input constraints are available, the modeled system tracks reference trajectories to achieve the aim of recognizing and organizing distinct objects. After the NLMPC is successfully developed, a simulation environment is built and finally brought to life by combining all the processes into one using a MATLAB Stateflow chart.