Category robots in business

Page 291 of 433
1 289 290 291 292 293 433

Cutting surgical robots down to size

By Lindsay Brownell

Minimally invasive laparoscopic surgery, in which a surgeon uses tools and a tiny camera inserted into small incisions to perform operations, has made surgical procedures safer for both patients and doctors over the last half-century. Recently, surgical robots have started to appear in operating rooms to further assist surgeons by allowing them to manipulate multiple tools at once with greater precision, flexibility, and control than is possible with traditional techniques. However, these robotic systems are extremely large, often taking up an entire room, and their tools can be much larger than the delicate tissues and structures on which they operate.

The mini-RCM is controlled by three linear actuators (mini-LAs) that allow it to move in multiple dimensions and help correct hand tremors and other disturbances during teleoperation. Credit: Wyss Institute at Harvard University

A collaboration between Wyss Associate Faculty member Robert Wood, Ph.D. and Robotics Engineer Hiroyuki Suzuki of Sony Corporation has brought surgical robotics down to the microscale by creating a new, origami-inspired miniature remote center of motion manipulator (the “mini-RCM”). The robot is the size of a tennis ball, weighs about as much as a penny, and successfully performed a difficult mock surgical task, as described in a recent issue of Nature Machine Intelligence.

“The Wood lab’s unique technical capabilities for making micro-robots have led to a number of impressive inventions over the last few years, and I was convinced that it also had the potential to make a breakthrough in the field of medical manipulators as well,” said Suzuki, who began working with Wood on the mini-RCM in 2018 as part of a Harvard-Sony collaboration. “This project has been a great success.”

A mini robot for micro tasks

To create their miniature surgical robot, Suzuki and Wood turned to the Pop-Up MEMS manufacturing technique developed in Wood’s lab, in which materials are deposited on top of each other in layers that are bonded together, then laser-cut in a specific pattern that allows the desired three-dimensional shape to “pop up,” as in a children’s pop-up picture book. This technique greatly simplifies the mass-production of small, complex structures that would otherwise have to be painstakingly constructed by hand.

The team created a parallelogram shape to serve as the main structure of the robot, then fabricated three linear actuators (mini-LAs) to control the robot’s movement: one parallel to the bottom of the parallelogram that raises and lowers it, one perpendicular to the parallelogram that rotates it, and one at the tip of the parallelogram that extends and retracts the tool in use. The result was a robot that is much smaller and lighter than other microsurgical devices previously developed in academia.

The mini-LAs are themselves marvels in miniature, built around a piezoelectric ceramic material that changes shape when an electrical field is applied. The shape change pushes the mini-LA’s “runner unit” along its “rail unit” like a train on train tracks, and that linear motion is harnessed to move the robot. Because piezoelectric materials inherently deform as they change shape, the team also integrated LED-based optical sensors into the mini-LA to detect and correct any deviations from the desired movement, such as those caused by hand tremors.

Steadier than a surgeon’s hands

To mimic the conditions of a teleoperated surgery, the team connected the mini-RCM to a Phantom Omni device, which manipulated the mini-RCM in response to the movements of a user’s hand controlling a pen-like tool. Their first test evaluated a human’s ability to trace a tiny square smaller than the tip of a ballpoint pen, looking through a microscope and either tracing it by hand, or tracing it using the mini-RCM. The mini-RCM tests dramatically improved user accuracy, reducing error by 68% compared to manual operation – an especially important quality given the precision required to repair small and delicate structures in the human body.

After the mini-RCM’s success on the tracing test, the researchers then created a mock version of a surgical procedure called retinal vein cannulation, in which a surgeon must carefully insert a needle through the eye to inject therapeutics into the tiny veins at the back of the eyeball. They fabricated a silicone tube the same size as the retinal vein (about twice the thickness of a human hair), and successfully punctured it with a needle attached to the end of the mini-RCM without causing local damage or disruption.

In addition to its efficacy in performing delicate surgical maneuvers, the mini-RCM’s small size provides another important benefit: it is easy to set up and install and, in the case of a complication or electrical outage, the robot can be easily removed from a patient’s body by hand.

“The Pop-Up MEMS method is proving to be a valuable approach in a number of areas that require small yet sophisticated machines, and it was very satisfying to know that it has the potential to improve the safety and efficiency of surgeries to make them even less invasive for patients,” said Wood, who is also the Charles River Professor of Engineering and Applied Sciences at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS).

The researchers aim to increase the force of the robot’s actuators to cover the maximum forces experienced during an operation, and improve its positioning precision. They are also investigating using a laser with a shorter pulse during the machining process, to improve the mini-LAs’ sensing resolution.

“This unique collaboration between the Wood lab and Sony illustrates the benefits that can arise from combining the real-world focus of industry with the innovative spirit of academia, and we look forward to seeing the impact this work will have on surgical robotics in the near future,” said Wyss Institute Founding Director Don Ingber, M.D., Ph.D., who is also the the Judah Folkman Professor of Vascular Biology at Harvard Medical School and Boston Children’s Hospital, and Professor of Bioengineering at SEAS.

A model for autonomous navigation and obstacle avoidance in UAVs

Autonomous unmanned aerial vehicles (UAVs) have shown great potential for a wide range of applications, including automated package delivery and the monitoring of large geographical areas. To complete missions in real-world environments, however, UAVs need to be able to navigate efficiently and avoid obstacles in their surroundings.

#317: Environmental Monitoring with the SlothBot, with Gennaro Notomista


In this episode, Lauren Klein interviews Gennaro Notimista, a robotics PhD student in the Georgia Robotics and InTelligent Systems Laboratory at Georgia Tech. Gennaro discusses the SlothBot, a solar-powered robot that slowly traverses wires, like its animal namesake, to monitor the environment.

Gennaro Notomista

Photo from news.gatech.edu

Gennaro Notomista is a robotics PhD student in the Georgia Robotics and InTelligent Systems Laboratory at Georgia Tech. Gennaro studies control frameworks, with the goal of making robots robust against a changing environment so they can handle long-duration deployments. Toward this goal, he explores constraints-driven control and approaches to coverage control, or enabling robots to traverse closed environments.  In addition to the SlothBot, Gennaro has applied his research to areas such as autonomous driving and swarm robotics.

Links

Origami-inspired miniature manipulator improves precision and control of teleoperated surgical procedures

Minimally invasive laparoscopic surgery, in which a surgeon uses tools and a tiny camera inserted into small incisions to perform operations, has made surgical procedures safer for both patients and doctors over the last half-century. Recently, surgical robots have started to appear in operating rooms to further assist surgeons by allowing them to manipulate multiple tools at once with greater precision, flexibility, and control than is possible with traditional techniques. However, these robotic systems are extremely large, often taking up an entire room, and their tools can be much larger than the delicate tissues and structures on which they operate.

Locust swarm could improve collision avoidance

Plagues of locusts, containing millions of insects, fly across the sky to attack crops, but the individual insects do not collide with each other within these massive swarms. Now a team of engineers is creating a low-power collision detector that mimics the locust avoidance response and could help robots, drones and even self-driving cars avoid collisions.

How to build an AI business case

I recently surveyed danish CIO’s(Chief information officers) about their relationship with AI and I had some interesting results. One of the results was that one of the biggest barriers to get started on AI projects is that building the business case is difficult. I completely understand the issue and I agree with the CIO’s. Building an AI business case is difficult and if you try to build it as a traditionnel IT business case it’s down right impossible. 

Building a business case is all about understanding the cost and revenue drivers well enough to work them into a model that yields a profit with high certainty within an agreed timeline. When building AI solutions or even buying them off-the-shelf that whole process turns out to be way more challenging than what you will experience with traditional IT-projects. In my experience this is for many a lesson hard-learned by many in the IT business that naturally grabs their well-known tools and methods but quickly fails. This often results in AI being disregarded as being a too immature technology. With the right approach, that I’m going to show you here, you can actually build a business case that makes sense. The technology is ready and at a stage where most businesses can successfully utilize it. New technology just requires new approaches.

Before moving on to how you build an AI business case, let’s understand why this is such a difficult task. The reason is simply, that everything in AI is experimental in its natural form and as a result nothing is predictable. How much data you need, what algorithmic approach will work and how good the result will be is very difficult to know beforehand. You can look at a similar project but small differences in the problem, the data or the environment will often to much surprise make a big difference. So knowing the exact costs, results and the road there is just not possible.

The cost side

What will it cost to an AI? You just can’t know. In traditional IT we try to break down the project into smaller and smaller pieces until each piece is such a size that we can easily estimate the time and costs that go into it. In AI the process is experimental and we can’t even know the pieces in advance. 

To combat this problem there is a set of strategies that will make it a lot easier to control the cost side. On purpose I’m writing, controlling and not predicting the costs. In the AI paradigm, predicting costs should not be the goal. The goal should instead be to control it. I’ll get back to why that makes sense a little later.

The cost control strategies are the following:

Iterations

For years now we have been talking about the agile approaches in IT. Some have used it with success others have steered clear and stayed with traditional methods and some unfortunately have used it as an excuse not to have a plan at all. The agile approach suggests iterating through projects several times to account for new learnings during the project and changes in demand. Similarly AI projects should use an iterative approach to get a set of important learnings. Just by doing one very quick simply iteration you should get these learnings:

  1. You understand the data better. You understand how much effort it requires to attain, how to attain it and get a sense of how much you will need. 

  2. You can get a sense of how the users react to a certain quality and how difficult it will be to deploy

  3. You get a good idea of potentially attainably quality.

Last point here should be seen as a stop-test. If you don’t see a close to acceptable quality in the very first iteration it’s very unlikely that you will see much better results in the near future with either attaining much much more data or putting a significant larger investment into the algorithm work. So many AI projects should be abandoned if the first iteration is not close to a useful solution. In some cases though this can be just a wrong algorithmic approach. This is where you have to rely on the tech people's judgement.

For the first iteration you can in my opinion start very small by using AutoML solutions. AutoML is AI without coding that can be trained and deployed within hours just needing only data. There are pros and cons here to be aware of. I wrote another blogpost about this here.

Milestone funding 

I preach a lot about milestone funding in AI. This is a very effective strategy to control costs. In AI the milestones are natural and project funding should only be released for each milestone once a set of agreed criteria is successfully met. The milestone naturally would look like this:

Collect data

First step is to collect a certain amount of data at a certain quality at a certain cost. Collecting, cleaning and preparing data is almost always the most underestimated cost in relation to AI projects so making these first steps success criteria very specific is not a bad idea at all. 

An important aspect of data collection is the frequency you need in updating the data. Some projects require only initial or rare data collection and for others it’s required to build an entire data operation that in itself should be a good business case. Many projects die from costly data operations so take it into account early. The trick here is to measure a lot in the process.


Building models

Next step is building models. This is in the business case not that complicated. This is where tech people have to estimate but they will do so with great uncertainty and that is just how it is. As mentioned before the first iteration should be as quick as possible and if you don’t see potential for good results after this, you should be willing to stop the project or change technical strategies.


Deploying 

You should also try to deploy the AI models into a test or staging environment already in the early iterations. It might seem like overcomplicating the problem, but AI models are just more clumsy to work with in my experience than other code bases. The amounts of data also makes the dev ops challenge a bit more interesting.

Studies have also shown that up to 99% of the code in AI projects are all the “glue code” around the actual AI that makes it work in the given environment. So getting a sense of this early is also a good idea.

For deploying the model there is also very often a human aspect that should be a part of the criteria for success. People respond differently to AI solutions than other solutions since AI is harder to understand as a layman. 

Bundle your AI projects

My last advice for controlling costs in AI projects is to bundle more projects in one business case. As you can see, the risk of an AI project early on turns out to be too costly or not good enough, is there. There’s a tendency in IT to keep working on projects that have already shown signs of being a failure since we as people overestimate our abilities to improve the situation and we just want to deliver something. If we deliver nothing we feel as complete failures. 

To avoid this, put more projects in one business case so you can let the bad one die and the good once flourish. You might argue that people should just be better at calling it quit when failure is inevitable but to me changing the structures and letting people be people is a much superior strategy.

AI business culture

Before moving on to the revenue side I wanted to add some notes on AI and company culture. As I mentioned the possible is that some AI projects should be shut down early. It can look like a failure but with the right culutre this can be seen as a successful null-result. Collecting a ceratin amount of null-results is very valuable to a business, especially if done at a low cost. By knowing for sure what does not work a business can much easier navigate and plan ahead. The only problem is that null-results are not always cultural acceptable. Untill it is a lot of the AI business case strategies to control cost won’t be very easy to implement. So management has a very important responsibility to make sure that the company culture supports these approaches.

The same goes for the cost control. If there’s not a culture for controlling cost instead of predicting AI will hardly be good experience. AI ironacly doesn’t offer predicability. So a culture that instead supports budget or time boxing is much more effecient for AI.

The revenue side

When someone asks me if a certain problem can be solved with AI I answer “probably yes” since people are usually on the right track. The natural followup question it “how good will the AI be then?”. The right answer here is “I don’t know”. This is to many a hard to shallow pill. The people that demand answer here will rarely succeed with AI. Those that can work around that lack of information will be much more likely to successeed so naturally that should also go for the business case.

If the revenue, value or profit is based on the quality then you can’t calculate the expected profit since you can’t know the results. Even if the AI is sold at a predetermined prices it’s hard to predict since adoption among users is often based on quality. 

Who are you trying to beat? 

Besides utilizing the very quick interations to get an idea about the quality to expect you should also be clear on what to expect from your AI. Don’t try to make a business case for a perfect AI. Make one for a good enough AI that solves the problem at hand. Very often new technology is held to golden standards and the expected results will be out of this world. Be very specific here in your communication to avoid this. I also wrote about it here.

Presenting your business case

Now that you know that you can’t build a business case on AI projects as you would classical IT projects you all set right? Not quite. The last challenge is when you have to present the business case. Your peers that might need to review or accept the business case usually expect classical IT paradigm business cases. So my last piece of advice here is simple - Start by presenting the core principles of AI and how that makes the business case different. If you get a buying on your new approach everything will  be much more smooth.

Study examines robotic exoskeletons and bodily fit

A shoddily tailored suit or a shrunken T-shirt may not be the most stylish, but wearing them is unlikely to hurt more than your reputation. An ill-fitting robotic exoskeleton on the battlefield or factory floor, however, could be a much bigger problem than a fashion faux pas.
Page 291 of 433
1 289 290 291 292 293 433