Imagine for a moment that a road is used only for a single car and driver. Everything is smooth and wonderful. Then you wake up from that utopian dream and remember that our road networks have multiple cars of varying sizes, from different manufacturers, each with a driver with unique behaviors behind the wheel. We quickly realize that traffic conventions and rules are in place to avoid complete and utter chaos. We believe with increasing robotic use cases in the public domain as we all do see, a similar parallel reality needs to be realized and we propose that RoMi-H, an open-source robot and infrastructure framework that simplifies cross fleet robot collaboration, is the way to achieve this coming reality!
Even before the onset of COVID-19, the number of robots and automation technologies introduced and tested in the healthcare industry has been skyrocketing. Service robots perform an ever increasing and diverse set of tasks; taking on smaller and more sensitive deliveries in some cases and relying more heavily on shared infrastructure such as elevators (lifts), doors, and passageways. No single robotics or automation provider can supply the breadth of solutions required in a modern healthcare facility and no facility can afford to operate siloed systems requiring dedicated infrastructure and operating unique user interfaces. Therein lies the challenge.
First announced in July 2018, Robotic Middleware for Healthcare (RoMi-H) is a unique open-source system built on ROS 2 and simulated using the Gazebo simulator. It allows for uniform communication and monitoring across robot platforms, sensors, and enterprise information systems. A brief explanation of this initiative can be found in WIRED Magazine: As Robots Fill the Workplace, They Must Learn to Get Along. In tomorrow’s reality, interoperability must be front and center for every developer, manufacturer, systems integrator, and end-user.
As we have written before:
We need food-delivery robots from one vendor to communicate with drug-delivery robots from another vendor. We need a unified approach to command and control for all the robots in a facility. We need a reliable way to develop and test multi-vendor systems in software simulation prior to deployment. And for it to succeed we need this critical interoperability infrastructure to be open source.
Under the leadership of Singapore’s Centre for Healthcare Assistive and Robotics Technology (CHART) and with collaborators such as IHiS, Hope Technik, GovTech and other solution providers, Open Robotics has been working since 2018 to develop an open-source software solution. Its goal is to realize the potential of a vendor agnostic and interoperable communication system for heterogeneous robots, sensors, and information systems in the healthcare space. To accelerate its development we are encouraging contributions to the open-source codebase to accelerate the development of a robust and sustainable system.
In order to understand the underlying mechanics of RoMi-H, we encourage you to take a look at Programming Multiple Robots with ROS 2. It is being continuously updated and will provide you with a thorough explanation of ROS 2 — upon which RoMi-H is built — and the core Robotics Middleware Framework (RMF) that serves to power RoMi-H. The book also features a tutorial on how one might build a web application that can interface with RoMi-H to create useful applications for robot operators or user-facing tools for the robotics industry.
RoMi-H is able to apply the same software across the different robotic systems while ROS 2 manages the communication and data routing from machine to machine; allowing for real-time, dependable and high-performance data exchanges via a publish-subscribe pattern. Publishers group their messages into different classes and subscribers receive information from the classes of messages they have indicated an interest in. This allows RMF to provide a common platform for integrating heterogeneous robotic systems.
We see the RoMi-H project as a significant step, encouraging an open and integrated approach to robotics development and digitising healthcare. We are looking forward to receiving feedback and contributions from interested parties.
Learn More
A public webinar that introduced RMF and featured a live demonstration took place on 18 August 2020 in the CHART Lab. The presentations and recordings can be viewed here. At the same time, if you are interested in finding out more and viewing the source code, do check out the ROS 2 book and the following repositories:
ROS 2 Book:
Github Repositories:
We would like to acknowledge the Singapore government for their vision and support to start this ambitious research and development project, “Development of Standardised Robotics Middleware Framework – RMF detailed design and common services, large-scale virtual test farm infrastructure, and simulation modelling”. The project is supported by the Ministry of Health (MOH) and the National Robotics Program (NRP).
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the NR2PO, MOH or other parties.
Combining drone imagery with weather data and planting schemes to forecast how much fresh vegetables a harvest is going to yield; that’s what predictive modelling intern Berend Klaver from TU Delft is sweating on at VanBoven, while his bosses are entertaining the American west coast.
VanBoven is one of the ten winners of the Academic Startup Competition 2020, currently on tour in Silicon Valley for a 4-week incubator programme.
“The market of fresh vegetables is one of constant shortages and surpluses. VanBoven predicts the harvest of fresh produce to perfectly align supply and demand. The result is decreased food waste, a resilient value chain and fair prices,” says the startup on its website.
The result is decreased food waste, a resilient value chain and fair prices.
So this firm from The Hague does not favour any particular party in the value chain, even though we do get a sense that it may have special empathy for farmers. What’s unique about the robot-powered predictions made by Klaver and his colleagues, is their cooperative deployment. The models are being used to foster symbiotic relations between all players in the system: growers of fresh vegetables, distributors and processors, agricultural service providers and retailers too. These parties can now all work together to anticipate fluctuations and coordinate a joint response to the unpredictability of nature and markets.
A refreshing proposition that could point towards a more positive future of work.
As an alternative to cutthroat, winner-takes-all capitalism, it seems a refreshing proposition that could point towards a more positive future of work. Maybe today’s tech startups from The Netherlands are not just soaking up insights during their missions to Silicon Valley, but are dishing them out too. ‘How Dutch-style Cosiness Breeds Resilience and Wellbeing’; we can already picture that headline in WIRED.
The Academic Startup Competition is an initiative of the Association of Universities in the Netherlands (VSNU), the Netherlands Academy of Technology and Innovation (AcTI), the Netherlands Federation of University Medical Centres (NFU) and Techleap.nl. It is also supported by the Ministry of Economic Affairs and Climate Policy.
We can already picture that headline in WIRED.
Current winners are Kaminari Medical, IamFluidics, VanBoven, Lusoco, BIMINI, taylor, UCrowds, NC Biomatrix, digi.bio and DeNoize.
The competition aims to highlight the importance of valorisation in the academic world. In addition to a Silicon Valley tour hosted by Holland in the Valley, an ecosystem for Dutch entrepreneurs in the San Francisco Bay Area, the winners are also being showered with perks such as introductions to networks, coaching programmes and the right to carry the title ‘Best Academic Startup of 2020’.
The post What Holland can teach Silicon Valley: a joint response to unpredictability appeared first on RoboValley.
By Rachel Gordon | MIT CSAIL
From Star Trek’s replicators to Richie Rich’s wishing machine, popular culture has a long history of parading flashy machines that can instantly output any item to a user’s delight.
While 3D printers have now made it possible to produce a range of objects that include product models, jewelry, and novelty toys, we still lack the ability to fabricate more complex devices that are essentially ready-to-go right out of the printer.
A group from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) recently developed a new system to print functional, custom-made devices and robots, without human intervention. Their single system uses a three-ingredient recipe that lets users create structural geometry, print traces, and assemble electronic components like sensors and actuators.
“LaserFactory” has two parts that work in harmony: a software toolkit that allows users to design custom devices, and a hardware platform that fabricates them.
CSAIL PhD student Martin Nisser says that this type of “one-stop shop” could be beneficial for product developers, makers, researchers, and educators looking to rapidly prototype things like wearables, robots, and printed electronics.
“Making fabrication inexpensive, fast, and accessible to a layman remains a challenge,” says Nisser, lead author on a paper about LaserFactory that will appear in the ACM Conference on Human Factors in Computing Systems in May. “By leveraging widely available manufacturing platforms like 3D printers and laser cutters, LaserFactory is the first system that integrates these capabilities and automates the full pipeline for making functional devices in one system.”
Let’s say a user has aspirations to create their own drone. They’d first design their device by placing components on it from a parts library, and then draw on circuit traces, which are the copper or aluminum lines on a printed circuit board that allow electricity to flow between electronic components. They’d then finalize the drone’s geometry in the 2D editor. In this case, they’d use propellers and batteries on the canvas, wire them up to make electrical connections, and draw the perimeter to define the quadcopter’s shape.
The user can then preview their design before the software translates their custom blueprint into machine instructions. The commands are embedded into a single fabrication file for LaserFactory to make the device in one go, aided by the standard laser cutter software. On the hardware side, an add-on that prints circuit traces and assembles components is clipped onto the laser cutter.
Similar to a chef, LaserFactory automatically cuts the geometry, dispenses silver for circuit traces, picks and places components, and finally cures the silver to make the traces conductive, securing the components in place to complete fabrication.
The device is then fully functional, and in the case of the drone, it can immediately take off to begin a task — a feature that could in theory be used for diverse jobs such as delivery or search-and-rescue operations.
As a future avenue, the team hopes to increase the quality and resolution of the circuit traces, which would allow for denser and more complex electronics.
As well as fine-tuning the current system, the researchers hope to build on this technology by exploring how to create a fuller range of 3D geometries, potentially through integrating traditional 3D printing into the process.
“Beyond engineering, we’re also thinking about how this kind of one-stop shop for fabrication devices could be optimally integrated into today’s existing supply chains for manufacturing, and what challenges we may need to solve to allow for that to happen,” says Nisser. “In the future, people shouldn’t be expected to have an engineering degree to build robots, any more than they should have a computer science degree to install software.”
This research is based upon work supported by the National Science Foundation. The work was also supported by a Microsoft Research Faculty Fellowship and The Royal Swedish Academy of Sciences.