Generation and experimental verification of time and energy optimal coverage motion for industrial machines using a modified S-curve trajectory

Improving motion accuracy with energy saving in industrial machines is essential to reduce production costs and meet the demand for precise products. Industrial motion planning is one of the effective methods used to save energy and enhance the production efficiency. This study proposes optimal motion planning by simultaneous path and velocity optimization to achieve the trade-off between time and energy consumption. The multi-objective optimization model for minimizing time and energy consumption is solved by the non-dominated sorting genetic algorithm II. Harmonic motion is introduced to the jerk-limited acceleration profile to attain smooth changes in the jerk profile and increase motion accuracy. The proposed method can be used for machine operations such as milling, laser cutting, inspection, gluing, and polishing. To validate the effectiveness of the proposed approach, simulation and experiments are carried out using a two-axis feed drive system, and the motion accuracy is compared to that of jerk-limited acceleration profile. Experimental results reveal that the best trade-off trajectory of the proposed approach achieves respectively 13.9% and 3.5% of time reduction and energy saving. The mean tracking error is reduced by 16.2% and 14.9% for the x and y axes, respectively, compared to the jerk-limited acceleration profile.