Energy model for motion planning of 2D-belt press line tending robots

A current trend in production is to reduce energy consumption where possible not only to lower the cost but also to be a more energy efficient entity. This paper presents an energy model to estimate the electrical energy consumption of 2D-belt robots used for material handling in multi-stage sheet metal press lines. An estimation of the energy consumption is computed by the proposed energy model based on the robot components’ specifications, the robot path and trajectory. The proposed model can predict the energy consumption offline by simulation, and thus, before installation, avoiding the need for physical experiments. It is demonstrated that it can be used for predicting potential energy reductions achieved by optimising the motion planning. Additionally, it is also shown how to investigate the energy saving achieved by using mechanical brakes when the robot is idle. This effectively illustrates the usefulness of the proposed energy model

Optimization of timed automata models using mixed-integer programming

Abstract. Research on optimization of timed systems, as e.g. for computing optimal schedules of manufacturing processes, has lead to approaches that mainly fall into the following two categories: On one side, mixed integer programming (MIP) techniques have been developed to successfully solve scheduling problems of moderate to medium size. On the other side, reachability algorithms extended by the evaluation of performance criteria have been employed to optimize the behavior of systems modeled as timed automata (TA). While some successful applications to real-world examples have been reported for both approaches, industrial scale problems clearly call for more powerful techniques and tools. The work presented in this paper aims at combining the two types of approaches: The intention is to take advantage of the simplicity of modeling with timed automata (including modularity and synchronization), but also of the relaxation techniques and heuristics that are known from MIP. As a first step in this direction, the paper describes a translation procedure that automatically generates MIP representations of optimization problems formulated initially for TA. As a possible use of this translation, the paper suggests an iterative solution procedure, that combines a tree search for TA with the MIP solution of subproblems. The key idea is to use the relaxations in the MIP step to guide the tree search for TA in a branch-and-bound fashion