Marcus Pettersson

Drive Train Optimization for Industrial Robots

This paper presents an optimization strategy for finding the trade-offs between cost, lifetime, and performance when designing the drive train, i.e., gearboxes and electric motors, for new robot concepts. The method is illustrated with an example in which the drive trains of two principal axes on a six-axis serial manipulator are designed. Drive train design for industrial robots is a complex task that requires a concurrent design approach. For instance, the mass properties of one motor affect the torque requirements for another, and the method needs to consider several drive trains simultaneously. Since the trajectory has a large impact on the load on the actuators when running a robot, the method also includes the trajectory generation itself in the design loop. It is shown how the design problem can be formalized as an optimization problem. A non-gradient-based optimization algorithm that can handle mixed variable problems is used to solve the highly nonlinear problem. The outcome from an industrial point of view is minimization of cost and the simulataneous balancing of the trade-off between lifetime and performance.

Methods for Discrete Design Optimization

One area in design optimization is component based design where the designer has to choose between many different discrete alternatives. These types of problems have discrete character and in order ...

Application Adapted Performance Optimization for Industrial Robots

Industrial robots are designed for a large spectrum of user scenarios. This implies that the robot cannot be tailor made for each situation and hence its full potential might not always be fully exploited. For further efficient use of 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, which the robot performs. Simultaneously the motion program itself i.e. the path planning can be optimized in order to get the most out of the robot. These ideas are tested on a six axis robot in a press tending application.

Adaptive Complex Method for Efficient Design Optimization

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.Copyright

On optimal drive train design in industrial robots

In this paper optimization is used to determine which gearboxes to use in an industrial robot. The paper also presents a procedure where tradeoff information is generated based on consecutive optimizations. Thereby the method provides the designer with information about critical tradeoffs between conflicting objectives. This type of information is very valuable when negotiating between different design alternatives. Generating these tradeoffs is traditionally a time consuming process, but by introducing optimization this process can be partly automated. The design variables studied are composed of continuous and discrete parameters, where the latter are associated with different gearbox alternatives and the continuous variables with the speed-torque limitation of the gearboxes. A non-gradient based optimization algorithm which can handle mixed variables problems is used to solve the highly non-linear problem. The outcome from an industrial point of view is minimization of cost and simultaneously balance the trade-off between lifetime and performance.

A Component Based Optimization Approach for Modular Robot Design

In this paper, an approach for modular design of industrial robots is presented. The approach is to introduce an objectoriented simulation model of the robot and combine this with a discrete optimization algorithm. The simulation model of the industrial robot is developed in Modelica, an object oriented modeling and simulation language, and simulated in the Dymola tool. The optimization algorithm used is a modification of the Complex method that has been developed in Matlab and connected to the simulation program. The optimization problem includes selecting components such as gearboxes and motors from a component catalogue and the objective function considers minimization of cost with constraints on gear box lifetime. Furthermore, the correctness of the model has been verified by comparison with an in-house simulation code with high accuracy.Copyright