A Component Based Optimization Approach for Robot Modular Design

Robot manufacturers, like many other manufacturers, are experiencing increasing competition in a global market where one way to confront the challenge is by making the development process more efficient. One way to speed up the time to market for new products is to take advantage of design optimization based on simulation models. By optimizing performance with the help of dynamic simulation, an immense amount of both time and money may be saved. In this thesis, design optimization strategies for industrial robot design are studied. Often, the trade-offs between performance, cost and quality are essential for design decisions. These tradeoffs can be investigated with the help of simulation models. Generating the trade-offs can be both cumbersome and time-consuming, but the process may be partly automated with the help of optimization algorithms. How the optimization problem needs to be formulated to generate the trade-off is discussed in this thesis. Robot design problems usually consist of a mixture of deciding continuous parameters as well as selecting components from catalogs and databases. Hence, there is a need for optimization algorithms which can handle variables of both a discrete and a continuous nature. A new method has been developed to address this problem. The method has also been improved by adding adaptive characteristics for further efficient design optimization. The ideas in this thesis have been applied to both simulation models of conceptual degrees of elaboration as well as simulation models of complete robot systems. An optimization procedure which shows how optimization can be used in the early phases of a development process is developed. The objective of the optimization is to determine optimal gearboxes and arm lengths from an acceleration capability perspective. An optimization based design method for robot drive trains is also presented. For further efficient use of already installed robots the concept of application adapted performance optimization is introduced. This means that the robot control is optimized with respect to thermal and fatigue load for the specific program that the robot performs. The motion program itself, i.e. the path planning, can be optimized at the same time in order to get the most out of the robot.