A parametric approach to designing an industrial pick-and-place robot
09 September 2016
Designing complex industrial machines is a challenging process. Engineers need to ensure that the machine they design meets different performance objectives, for productivity, workspace, manoeuvrability, payload, and so on.
At the same time, they also need to develop a design that will minimise both production and maintenance costs, such as using the smallest possible motors and the shortest links for robot arms, and minimising loading to reduce the wear and tear that leads to expensive repairs and downtime.
A provider of packaging machines approached the Maplesoft Engineering Solutions team in the early stages of the design of a new product that incorporates a pick-and-place robot. They turned to Maplesoft to help them answer questions about the design.
The Maplesoft Engineering Solutions team applied a parametric physical modeling approach to answer these questions. They used MapleSim, the multidomain system-level modeling and simulation platform, to develop a high-fidelity parameterised model of the desired robot type.
Then they used the advanced computation capabilities of Maple to develop analysis tools that examine the operation of the system and its dynamic behaviour with different sets of parameter values. These analysis tools, together with the high-fidelity model, provided the client with the insight required to determine how to optimise their design, and provided them with a toolset they could easily configure for use in the design of similar products.
Model development using MapleSim
The robot model is mounted on a reference base, to which three links that form the robot arm are connected. The links are actuated by three servo motors, which provide the rotational motion and control with three degrees of freedom. The end effector consists of a translational component attached to the third link, allowing for the desired pick-and-place action.
Each of the link structures includes sensor components to provide force and torque information, which can later be used to determine radial force, axial force, and bending moment at each bearing. The model also includes numerous probes embedded at strategic locations within the design, to monitor performance characteristics such as required motor speed and torque, along with joint angle and constraints.
Initial simulations were run in MapleSim, to observe the behaviour of the system, with the probe information presented in various plots. The model was then loaded into Maple for in-depth analysis.
Design analysis using Maple
The Maplesoft Engineering Solutions team created a set of analysis tools in Maple, to provide the client with insight into different areas of their design that will help them make design decisions for the final product. Taking advantage of the fully parametric nature of the high-fidelity model developed using MapleSim, and Maple's symbolic computation engine, the tools enable the client to perform multiple iterations of simulations, to determine the best combinations of parameters.
The first design tool developed by Maplesoft enabled the client to perform kinematic analysis. The kinematic analysis allowed them to check the robot's workspace, visualise its motion, and determine any path offsets if required. The robot motion is affected by whether the robot's elbow is configured to be on the right side or the left side. One of the features of the kinematic analysis tool was to perform the inverse kinematics calculations, and evaluate for both elbow positions. By observing its behaviour in both cases, the client was able to make an informed decision about which side to place the elbow – a decision which was then carried forward and applied to all further analyses.
The next step was to determine whether the robot was operating within the range of allowable motion, and whether any of the joint angles were exceeding the desired limit.
For each joint, multiple variables including joint angle, angular velocity, and angular acceleration were determined, based on the desired path of the end effector motion. The results showed that the initial end effector design path resulted in large angular acceleration spikes, indicating that the client needed to make some modifications in order to smooth out the motion used to actuate the joints. The adjustment would not only decrease the magnitude of the acceleration spikes, but would also result in reduced joint load, and reduced motor and bearing operating requirements.
While the client naturally wanted to use the smallest motors possible, they also had to ensure that the motors they selected would still meet the robot's performance goals. The Maplesoft Engineering Solutions team developed an analysis tool to assist the client with motor sizing.
The speed, torque, and energy of the motors were determined and plotted, then overlaid on the manufacturer's performance curves for the targeted motors. The motor performance curves were selected from a list of possible motor data imported into Maple. For each of the motors, the client could then compare simulated results with data for different motors from the manufacturer's specifications. Using the analysis tool, the client was able to consider different motor configurations capable of performing within the desired range. A similar approach of overlaying the manufacturer's data on simulated data was taken to explore the gearbox limits and the selection of different gear ratios.
Another analysis tool developed by Maplesoft was a parameter sweep to observe the effects of different link lengths on the operation of the robot. Simulating the model with different link length configurations within a pre-determined permissible range enabled the client to observe the corresponding effects on performance characteristics such as motor speed, torque, load requirements, and workspace variations. Maple automatically makes use of parallel processing, allowing the user's computer to simultaneously run multiple simulations using different parameter values, and then presents the results overlaid in a visualisation window for quick and easy comparison.
Using these, and numerous other tools, the client was able to apply a comprehensive approach to analysing their design decisions, and arrived at an ideal design for their industrial pick-and-place robot.
Developing a fully parametric system model in MapleSim provided access to all the system parameters required to analyse and optimise the behaviour of the system. Maple's symbolic computation engine enabled the development of a wealth of analysis tools that explored the relationships between system parameters, and their effects on the overall performance. This parametric approach meant that not only was the packaging company able to make informed design choices and arrive at the optimum configuration for their targeted application, but that they could reuse the same tools in other contexts.
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