A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Sequence Scheduling <s> Practical incentives motivate the development of optimal working schedules in manufacturing environments. Most often, such development is focused on time optimality issues, without concern for how the resulting schedules are executed. In practice, this often leads to an unnecessarily high amount of acceleration during a production cycle, which affects negatively the productive life of the manufacturing equipment. In this paper, we present and compare several methods to reduce the acceleration load in a production cell by processing a given working schedule, without compromising such features of the schedule as cycle time optimality, collision- and deadlock avoidance. <s> BIB001 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Sequence Scheduling <s> Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modelled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system is presented. It shows that there exists a possibility for a significant reduction of the energy consumption, in comparison to state-of-the-art scheduling approaches. <s> BIB002 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Sequence Scheduling <s> Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modeled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system and an experiment on an industrial robot are presented. They show that there exists a real possibility for a significant reduction of the energy consumption in comparison to state-of-the-art scheduling approaches. <s> BIB003 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Sequence Scheduling <s> This paper presents an optimization algorithm that has been able to save up to 45% of the energy consumption of an industrial multi-robot system. The tool Sequence Planner now includes these algorithms, focusing on minimizing energy consumption. The goal has been to reduce the energy consumption of individual robots and robots interacting in a work station, without changing the original paths or the total cycle time. The presented algorithms are based on an efficient and rapid nonlinear model for optimizing the sequences of operations and the motion trajectories. Smart simplification of the optimization problem together with innovative tools for logging data and executing the optimized trajectories on real robots has resulted in 18% to 45% saving of the energy consumption in the presented test scenarios. <s> BIB004 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Sequence Scheduling <s> This paper, as an outcome of the EU project AREUS, heralds an optimization procedure that reduces up to 30% of energy consumption and up to 60% in peak power for the trajectories that have been tested on real industrial robots. We have evaluated a number of cost functions and tested our algorithm for a variety of scenarios such as varying cycle times, payloads, and single/multirobot cases in both ac- and dc-operated robot cells. The significance of our work is not only in the impressive savings, simplicity of implementation, and preserving path and cycle time, but also in the variety of test scenarios that include different kinds of KUKA robots. We have carried out the optimization and experiments in as realistic conditions as possible. <s> BIB005
Other approaches deal with searching a more energy-efficient sequence of operations by keeping the cycle time fixed. In BIB001 , the authors aim to reduce the acceleration load in a production cell by processing and optimizing a given working schedule, without compromising on the cycle time, and warranting the collision and deadlock avoidance. The idle time between different operations is then used to reduce velocities and accelerations for reducing the energy consumption. With the same purpose, in BIB003 BIB002 , the consumption of the robots into a scheduling model of the overall system is evaluated: the energy requested to perform each operation is modeled and parametrized as a function of the operation's execution time. The optimal energy schedule is then derived by solving a mixed-integer non-linear programming problem. The goal of the sequence planning approach BIB004 BIB005 is to reduce the energy consumption of individual and interacting robots in a working station without changing the original paths or the total cycle time. The optimization problem is formulated as the minimization of the weighted squared angular accelerations for all joints (approximate measure of the energy consumption). The weight factor is meant to tune the optimization on the basis of the influence the acceleration of a joint has on the overall energy consumption. Constraints on the shared zone are defined when a group of robots have to move in and out of shared zones (a robot may therefore need to wait for another robot before entering the zone). This method does not rely on a dynamic model, and thus it does not require system identification or confidential robot data. Instead, it is based on an initial sampling of trajectories (i.e., joints displacement, velocity, and acceleration) and torque (i.e., derived from current) performed during the execution of a movement in real time. From this logged data, the optimization process retrieves an optimized trajectory in which idle times are reduced and waiting times are eliminated.