An optimal combo: robotics design and design automation
While the most obvious concept of robotics design is when humans manufacture robots, this article by Sam Holland considers how design automation has an invaluable role in ensuring that machines can also inform robot designers' decision making.
Defining the term ‘robotics design’
Traditionally of course, robotics design involves the planning and/or construction of a robot by a robotics designer. The pre-manufacturing stages of robotics design may constitute anything from the pitching of various concept images through to and including the more systematic principles such as the drawing of the relevant schematics and other component diagrams.
A robotics designer’s pre-manufacturing work is both technical (e.g. architecting the placement of the robot’s motors, battery, and CPU, etc.) and artistic (e.g. illustrating what the robot’s chassis and other design features may look like) and varies depending on their interests and areas of expertise. The usual order of events, however, is for robotics designers to prepare robots’ concept graphics, test each prototype, and ultimately assist in the manufacture of the end product.
But considering the value of humans in the design of robotics is only one side of the coin. Let’s now consider the reverse extreme: enter design automation, where machines influence humans in robotics design.
Bringing design automation to robotics design
Design automation is the collective term for using industrial software to reduce the amount of manual work that human engineers would otherwise need to perform. In this context, therefore, 'design automation' will be used to refer to the application of computing programming to optimise the ideation, design, and manufacturing processes of various robotics hardware.
To quote from Autodesk, the leading engineering software company, design automation “helps [designers] capture and reuse engineering knowledge”. Design automation is ‘rule-based design’, and accordingly, it can even bypass the need for designers to carry out complex codes themselves.
With the importance of rule-based design still in mind, the next section will cover the engineering practice, ‘generative design’, and accordingly, consider the value of algorithmic and other systematic approaches to robotics design.
Generative design is a type of 3D printing that sees the designer inputting into software their interpretation of an ideal product and its desirable properties. These target properties are known as the 'design goals', and they include such hardware considerations as the size, weight, and cost of production.
It is then the software’s task to run through each 'iteration', namely each attempt to reach the correct numerical values of those properties and calculate the best outcome accordingly. In common parlance, this iterative process could be called ‘trial and improvement’. This is where a computer (or person) finds a desired value by repeatedly ascertaining which calculations are too high and too low – until the process of elimination leads one to the answer (or otherwise the 'numerical solution', in those instances where there is no such thing as a unanimously 'correct' answer).
The industrial importance of generative design is rising as the growing demands for robotics design require manufacturers to consider an economical concept in industry known as ‘design for manufacturability (DFM)’. DFM is a principle of production that relates to the need to construct products that are high-quality but also made with affordability in mind. Generative design goes hand in hand with DFM as the former not only calls for the careful planning of materials production, but the outright calculation of how to optimally implement those materials.
More on the value of design automation
Again, algorithmic and other systematic approaches to robotics design are vital. Earlier, the focus was on the value of both the technical and artistic expertise required by the given robotics designer. But such designers are only human: they may not have the time and other resources to run through the potentially innumerable design iterations needed to ascertain the (hopefully) optimal properties of their robotics hardware.
Generative design and other areas of design automation offer a helping hand by providing the roboticist with points of comparison, namely 3D models on-screen. As weird and wonderful as those models may be, each calculation could help engineers to answer otherwise intractable questions in their own robotics design: should this robotic arm be more strong or more flexible? Can the chassis of this robot afford to be lightweight? Are there any parts of this technology that aren’t even needed?
Through the use of systematic approaches to design, roboticists are offered multiple 3D models on-screen before they print their desired product. Ultimately, robot designers and other roboticists will continue to benefit from generative design and other areas of design automation. This is particularly as the need for resilient, affordable chassis and other hardware becomes ever more apparent with the growing demands of the robotics market.
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