Planning and designing a new robot work cell can be a challenging project for even the most experienced engineers. There are lots of moving pieces, trade-offs that need to be considered, and usually tight deadlines.
Offline programming (OLP) software is becoming an increasingly essential tool for manufacturing professionals charged with planning new robot work cells; and it’s being used for much more than developing the robot program. OLP software helps with planning and optimizing the work cell design, virtually commissioning the robot, and accelerating the time to production. It uses 3D CAD data to create a virtual model of the robot and work cell, and simulate the processes and workflows inside and outside the cell – a powerful tool for engineers and planners to evaluate trade-offs and make better decisions. OLP software provides a meaningful return on investment for many types of automation projects, by saving time, improving productivity, and helping manufacturers to identify opportunities for cost savings.
There are many approaches and strategies to OLP, and we’re not advocating for one approach over another. In this article, we’ll give you a quick and general overview of the steps in the OLP process, particularly as it applies to the planning and design of a new robot work cell. The process we’re describing is adapted from a paper published in 2012, titled “Recent progress on programming methods for industrial robots” by Z Pan, et al (1).
1. Generation of Models
The first step to OLP is to create a virtual model of the work cell. This involves creating or obtaining 3D CAD models of the equipment, work pieces, enclosure, tools, and other resources and fixtures that will be in the work cell; and importing them into your OLP software. There may be extra steps in order to simulate resources and processes, depending on the OLP software being used. Accuracy of the models and process related information used is critical to generating a reliable simulation of the process and error-free offline program for the robots.
2. Tool Path Generation
Tool path generation involves extracting robot positions from 3D CAD data with a specific tool center point – the point in relation to which all robot positioning is defined. Many OLP software packages can do this automatically, and have built-in functions to automatically generate paths from features of the CAD models, such as corners, edges, or other geometry features.
3. Process Optimization
Process optimization incorporates trajectory planning, process planning, and tooling design. It’s an iterative design loop with a number of factors and tradeoffs that need to be considered, so simulation helps significantly with this process.
- Trajectory planning involves determining the best route for the robot to make, from Point A to Point B. While it might sound simple, this isn’t a trivial task; and it’s not always about planning the fastest or shortest route. Robot work cells are typically designed in compact configurations, so motions and trajectories have to be planned carefully to avoid unintended interactions between the robot and other objects in the cell. There are several factors that need to be considered, such as motion type, joint configuration / speed / acceleration, reachability, and collision detection and avoidance.
- Process planning involves planning the processes and workflow in the work cell. The major constraints for this step are budget, productivity, and quality, but there are several factors and tradeoffs that need to be considered. This step includes layout design, resource selection (including robots and other equipment), maintenance considerations, and sequence optimization.
- Tooling design involves the selection, modification, and placement of tools. This includes robotic end of arm tooling and other tools that touch the work piece, such as positioner faceplates and clamps.
