You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems 
Chapter Title:  Time Scaling 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  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.

Literature Review:
This method is based on the fact that, in most of the cases, an industrial robot has been programmed to realize the operation as fast as possible (i.e., shortest cycle time without considering the energy consumption) and with a consistent idle time, or wasted time, between two operations until the next operation is run. This approach implements the idea that a sensible energy-expenditure reduction could be achieved by slowing down as much as possible the speed (i.e., reduction of acceleration mean and peak values). The consequent increment in the operation time is compensated for by speeding up the slow movements and by reducing idle times; thus the cycle time is not affected. In BIB003 BIB008 , this approach is applied to optimize the motion of an anthropomorphic robotic cell (electro-mechanical model is needed) from the end of a process to the home position, in order not to affect the overall industrial process. Moreover, the activation of a mechanical brake during the robot idle state (also in short-time idle state) is considered to reduce the energy consumption. The works BIB001 BIB005 consider the scheduling optimization of a serial and a parallel manipulator, respectively, while performing a pick-and-place task. Also in this case, the optimization can be performed only on a selection of paths in order not to affect the manufacturing process' efficiency and robustness. In BIB002 BIB004 BIB006 BIB007 , an improved method, called dynamic time scaling, is considered. This exploits dynamic programming, instead of the linear assumption adopted in the previous method, to further reduce energy consumption. Wigstrom and Lennartson BIB002 prove the improvements that the new method can give when considering a two-degrees-of-freedom planar manipulator. In BIB004 BIB006 BIB007 , it is also applied to a generic robotic cell.