A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations 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> Operations 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> Operations Scheduling <s> Minimization of energy consumption is today an issue of utmost importance in manufacturing industry. A previously presented technique for scheduling of robot cells, which exploits variable execution time for the individual robot operations, has shown promising results in energy minimization. In order to slow down a manipulator's movement the method utilizes a linear time scaling of the time optimal trajectory. This paper attempts to improve the scheduling method by generating energy optimal data using dynamic time scaling. Dynamic programming can be applied to an existing trajectory and generate a new energy optimal trajectory that follows the same path but with another execution time. With the new method, it is possible to solve the optimization problem for a range of execution times in one run. A simple two-joint planar example is presented in which energy optimal dynamic time scaling is compared to linear time scaling. The results show a small decrease in energy usage for minor scaling, but a significant reduction for longer execution times. <s> BIB003 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations Scheduling <s> The interest in novel engineering methods and tools for optimizing the energy consumption in robotic systems is currently increasing. In particular, from an industry point of view, it is desirable to develop energy saving strategies applicable also to established manufacturing systems, being liable of small possibilities for adjustments. Within this scenario, an engineering method is reported for reducing the total energy consumption of pick-and-place manipulators for given end-effector trajectory. Firstly, an electromechanical model of parallel/serial manipulators is derived. Then, an energy-optimal trajectory is calculated, by means of time scaling, starting from a pre-scheduled trajectory performed at maximum speed (i.e. compatible with actuators limitations). A simulation case study finally shows the effectiveness of the proposed procedure. <s> BIB004 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations Scheduling <s> In this paper we study a subset of hybrid systems and present a generalized method for their optimization. We outline Hybrid Cost Automata (HCA), an extension to Hybrid Automata, where discrete and continuous cost expressions are added. The class of hybrid systems with known spatial paths is dened in the context of HCA. This type of system is common in industry where for example AGVs transport goods from one location to another, or manipulators move between joint coordinates. The optimization is performed using Dynamic Programming as a preprocessing step, whereafter Mixed Integer Nonlinear Programming is used for scheduling. A case study of a four robot cell is presented with energy consumption used as a minimization criterion. <s> BIB005 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations Scheduling <s> This paper quantitatively reports about potential energy savings on robotic assembly lines for the automotive industry. The key aspect of the proposed approach is that both cell production rate and robot hardware limitations are considered as strict constraints, so that no plant revision is needed. The methodology relies on: a) calculation of energy-optimal trajectories, by means of time scaling, concerning the robots' motion from the last process point to the home positions; b) reduction of the energy consumption via earlier release of the actuator brake when the robots are kept stationary. Simulation results are presented, which are based on the production timing characteristics measured on a real plant. <s> BIB006 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations 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> BIB007 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations Scheduling <s> The reduction of energy consumption is today addressed with great effort in manufacturing industry. In this paper, we improve upon a previously presented method for robotic system scheduling. By applying dynamic programming to existing trajectories, we generate new energy optimal trajectories that follow the same path but in a different execution time frame. With this new method, it is possible to solve the optimization problem for a range of execution times for the individual operations, based on one simulation only. The minimum energy trajectories can then be used to derive a globally energy optimal schedule. A case study of a cell comprised of four six-link manipulators is presented, in which energy optimal dynamic time scaling is compared to linear time scaling. The results show that a significant decrease in energy consumption can be achieved for any given cycle time. <s> BIB008 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations Scheduling <s> This paper concerns the reduction of energy use in manufacturing industry. If individual robot movements in a system are preprocessed using Dynamic Programming, one can attain a Mixed Integer Nonlinear Program (MINLP) which models the energy consumption of the complete system. This model can then be solved to optimality using mathematical programming. We have previously shown proof of concept for this energy reduction method. In this paper, we apply state of the art MINLP methods to a number of problems in order benchmark their effectiveness. Algorithms used are Nonlinear Programming based Branch and Bound (NLP-BB), Outer Approximation (OA), LP/NLP based Branch and Bound (LP/NLP-BB) and Extended Cutting Plane (ECP). Benchmarks show that the NLP-BB does not perform well for nonlinear scheduling problems. This is due to the weak lower bounds of the integer relaxations. For scheduling problems with nonlinear costs, ECP and in particular LP/NLP-BB are shown to outperform both NLP-BB and OA. The resulting energy optimal schedules for the examples show a significant decrease in energy consumption. <s> BIB009 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations Scheduling <s> Abstract The interest in novel methods and tools for optimizing the energy consumption in robotic systems is currently increasing. From an industrial point of view, it is desirable to develop energy saving strategies also applicable to established manufacturing systems with no need for either hardware substitution or further investments. Within this scenario, the present paper reports a method for reducing the total energy consumption of pick-and-place manipulators for given TCP position profiles. Firstly, electromechanical models of both serial and parallel manipulators are derived. Then, the energy-optimal trajectories are calculated, by means of constant time scaling, starting from pre-scheduled trajectories compatible with the actuation limits. In this manner, the robot work cycle can be energetically optimized also when the TCP position profiles have been already defined on the basis of technological constraints and/or design choices aimed at guaranteeing manufacturing process efficacy/robustness. The effectiveness of the proposed procedure is finally evaluated on two simulation case studies. <s> BIB010 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations Scheduling <s> This paper quantitatively reports about potential energy savings on robotic assembly lines for the automotive industry. At first, a detailed system model is described, which improves previously published results by explicitly considering both manipulator and electrical drive dynamics. The model closely captures experimental data in terms of actuation torques and servodrive voltages, which are directly used to derive the plant input power. Two practical methods are then evaluated for reducing the overall energy consumption. The methods rely on: 1) implementation of energy-optimal trajectories obtained by means of time scaling, concerning the robots' motion from the last process point to the home positions and 2) reduction of energy consumption by releasing the actuator brakes earlier when the robots are kept stationary. Simulation results, based on the production timing characteristics measured at a real plant, clearly shows that the system energy consumption can be effectively reduced without negative effects on the production rate. Note to Practitioners - The global industry trend towards sustainability demands energy optimization as a primary goal. Currently, industrial multirobot systems are not efficiently programmed as long as effective simulation tools are mostly lacking. In this context, a detailed model of the production line is proposed, which can be readily integrated into commercial tools for robot programming. Furthermore, the impact of practical optimization methods which are readily applicable to existing equipment is quantitatively highlighted. The key aspect of the proposed approach is that both the production rate of each cell and the robot hardware limitations are considered as strict constraints, so that no significant changes to the plant are required. <s> BIB011 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations 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> BIB012 </s> A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems <s> Operations 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> BIB013
Operations scheduling is a further approach related to the motion planning modification that can be found in literature, which saves energy by reducing the energy consumption of a single robotic system or a robotic cell BIB003 BIB005 BIB008 BIB009 BIB004 BIB012 BIB013 BIB006 BIB011 BIB010 BIB001  BIB007 BIB002 . In a robotic cell, a robot works synchronously with other robots or mechatronic systems, and the whole work has to be balanced in order to avoid queues. Moreover, in some operations, robots have to share the same working area. Thus, a robot waits for the completion of the previous robotic operation before executing its task in order to avoid collisions. The scheduling approach focuses on the optimization of the robot cell operations as a whole, and two main methods can be found: time scaling BIB003 BIB005 BIB008 BIB009 BIB004 BIB006 BIB011 BIB010 and sequence scheduling BIB012 BIB013 BIB001  BIB007 .