Time and energy optimal trajectory generation for coverage motion in industrial machines

The increase in computer numerical control machine efficiency highly contributes to environmental emission reduction and energy-savings. Path and trajectory optimizations are used to improve machine efficiency in a coverage motion such as pocket milling, polishing, inspection, gluing, and additive manufacturing. Several studies have proposed coverage motion optimization in improving machine efficiency for time and energy consumption. Ensuring the smoothness and satisfaction of the machine constraints in coverage motion is necessary. This paper proposes a multi-objective path and trajectory optimization to obtain a trade-off between time and energy consumption for coverage motion. Jerk limited acceleration profiles describe the trajectory where velocity profiles generated for each linear segment attain desirable velocities. The energy model of an industrial two-axis feed drive system is used in finding solutions to the optimization problem. The non-dominated sorting genetic algorithm II generates a Pareto front for trade-off time and energy consumption solutions. Simulation results of the proposed method are validated through experiments using the industrial two-axis feed drive system. Experimental results show the effectiveness of the proposed approach where time reduction and energy savings are 10.05% and 2.10%, respectively. In addition, the optimized path has a lower maximum error of 76.6% compared to the constantly commanded velocity optimized path.