Adaptive Reprogramming of Industrial Robots and Toolpath Validation in the Cloud

Visitor Weblog By Sebastian Andraos, Chief Government Officer, HAL Robotics Ltd.

At HAL Robotics, we allow the automation of variable, complicated, and unusual duties with robots. Over the previous 8 years, our software program has powered innovation for multi-nationals and subject material consultants in aerospace, building, transportation, and meals & beverage.

The HAL Robotics Framework gives a modular, extensible, and reconfigurable workflow. This contains connections to information sources, cell modelling, toolpath era and validation all through to robotic code era, add, execution and information acquisition for analytics.

The framework is extremely transportable. It runs on desktops, within the cloud, in embedded methods, and is even constructed into CAD software program. The HAL Robotics Framework can be vendor agnostic with simulation and programming assist for nearly 1000 robots from numerous producers together with ABB, KUKA, Common Robots, Yaskawa, Kawasaki and FANUC amongst others.

Hyper-Customization and Variable Manufacturing

From automotive elements to surfboards by means of kitchen cabinetry and aerospace elements – manufacturing firms are having to take care of growing product variation of their pipelines. This is actually because they’re beginning to automate duties that are being executed manually or they plan to supply extra customization and specialization to their clients. The variability in manufacturing isn’t accommodated by “conventional” automation strategies and that’s the place adaptive reprogramming comes into play.

By producing toolpaths for robots utilizing information somewhat than handbook programming, we are able to inherently deal with variation. That information could be geometric CAD fashions, textual content in a database, or another information format – so long as the patron of the info is ready to course of it correctly and generate a toolpath from it.

This strategy expands the Trade 4.0 concentrate on connectivity of units, information acquisition and monitoring, with new command and management paradigms. Whether or not it’s by means of a really made-to-measure service or modular assemblies, flexibility in automation is essential to retaining economies of scale. Particularly as we proceed transferring in the direction of extra human-centric design – eradicating boring, soiled, and harmful jobs from people and reducing the entry barrier for robotic use.

Course of Optimization

For all the advantages of adaptive robotic reprogramming to be realized, we nonetheless want to make sure the method we run is possible, secure and environment friendly. There are two alternative ways to deal with this. Both manually set secure parameters for all elements, such because the robotic’s working space, speeds, and gear choice, or nice tune our course of for every new half that comes by means of the pipeline. Simulation can validate variants without having to make use of an actual robotic. Nevertheless, testing completely different parameters and configurations on a part-by-part foundation is time consuming and, if executed manually, defeats the aim of adaptive programming.

That is the place a full robotics programming and simulation stack within the cloud pays dividends. By leveraging the compute energy of the cloud to run high-performance digital twin situations, we are able to take a look at completely different choices in parallel and make knowledgeable, automated choices about how an element must be processed. These choices could possibly be as broad as which robotic cell can deal with the half all the best way all the way down to the subtleties of toolpath era technique. For instance, in a robotic cell with a big and a small sander, we are able to decide whether or not, for a particular half, it’s extra environment friendly to make use of the bigger sander wherever potential and alter to the smaller one just for the main points, or whether or not the price of altering instruments outweigh the advantages of protecting bigger areas with the bigger device.

Automated Design Validation PoC

In September 2022, HAL Robotics participated in a Robotics Start-up Accelerator by AWS and MassRobotics throughout which we developed a PoC for a brand new cloud-based design manufacturing validation device with assist from AWS Principal Options Architect, Jeremy Wallace.

The answer (diagram above) primarily based on AWS providers, creates a closed loop between designing elements and validating its environment friendly producibility within the cloud. That is demonstrated with a simplified sprucing course of for a automobile door. The workflow begins with a designer making modifications to the half they need to produce in a CAD software program. As soon as they’re proud of this model of their half, they commit these modifications to a change monitoring system together with a set of parameters required for the manufacturing of the half. Within the demonstration, parameters embody sprucing power vary, sample choices, and the robotic cells that may course of the half.  In a real-world state of affairs, the design information and manufacturing parameters might come from two completely different individuals or straight from a buyer with little to no information of the manufacturing course of.

As soon as the design and parameters have been pushed to an Amazon Simple Storage Service (Amazon S3) bucket and Amazon DynamoDB desk respectively, the consumer calls an API to start out the validation of the method. This triggers an AWS Step Functions state machine which creates the batch of parameter configurations for the take a look at. Within the state machine, an AWS Fargate job is executed for every configuration utilizing a container picture saved in Amazon Elastic Container registry (Amazon ECR) that features the HAL Robotics Framework and a few customized code to generate a toolpath from the incoming parameters and geometric half information.

This container picture generates a toolpath for a particular robotic cell and solves the toolpath to make sure feasibility, determine any potential points and calculate quite a few metrics, e.g. length or vitality utilization, which can be utilized to attain completely different legitimate choices. These metrics are then pushed to DynamoDB alongside the parameters used, any errors, and a digital twin simulation of the method.

The consumer can log in to a portal to view their validation runs, drill into the metrics, and obtain the simulation. The simulation can then be considered in an interactive 3D viewer on their pc.

This workflow allows designers to make design modifications of their software program of selection – from making certain the brand new design could be produced to optimizing processing. They’ll do that without having to the touch a line of robotic code, manually change parameters, or watch for any simulations to run.

Conclusion

The answer proposed here’s a highly effective device to make product design modifications and study the impression of those modifications on manufacturing processes. It could actually additional enhance productiveness when mixed with the remainder of the HAL Robotics stack, the place you possibly can create an end-to-end workflow from design to environment friendly variable manufacturing. For instance, a 3D printing firm might permit customers to add their fashions to an internet site with a couple of user-accessible parameters, then set off the validation workflow above and push the optimum end result to an HMI for an operator to easily press play.

Taking it a step additional – this answer might run within the manufacturing loop. There are a selection of processes at HAL Robotics the place the elements getting into a cell aren’t recognized forward of time. In these instances, we use sensors comparable to 3D scanners to seize details about the half, then generate toolpaths with this information utilizing compute on the edge. While at all times purposeful and secure, these processes sacrifice a small quantity of effectivity for that flexibility. With the ability of the cloud and parallelization, you possibly can generate and validate 10s or 100s of various toolpaths in the identical period of time. This workflow is turning into significantly prevalent in incremental manufacturing traces which mix sensors and a number of processes to repair defects within the loop or create extraordinarily complicated elements by layering processes on prime of one another. Examples embody additive manufacturing and machining in alternating passes.

The HAL Robotics Framework has been enabling adaptive automation for years. Now, by demonstrating its portability into elastic cloud providers, we are able to unlock the scalable workflows wanted so as to add flexibility to even probably the most demanding manufacturing methods.

To obtain the HAL Robotics framework and study extra about this answer go to the Download page or on to the GitHub repository.