No abstract

The reduction of energy consumption is today addressed with great effort in manufacturing industry. A previously presented method for robotic system scheduling, which exploits variable execution time for the individual robot operations, has shown promising results for energy optimization. The method introduces linear time scaling of the trajectories to slow down the manipulators movements. This paper improves the scheduling method by generating energy optimal trajectories 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 in a different execution time frame. With the 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. 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. Note to Practitioners-In robotic manufacturing systems, much energy is wasted due to an adopted minimum time policy for robot operations. This paper presents a method for producing energy efficient operations as well as an optimization model for scheduling these operations in robot cell. Implementation requires a flexible robot controller which allows manipulation of speed and acceleration profiles. The results are also of interest to industrial robot manufacturers, whom have full control over the robot controller and would like to offer energy efficient solutions to their customers.

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.

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

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.