Fig. 1. The Monospinner in flight.

The Monospinner (Fig.1), developed at the Institute for Dynamic Systems and Control at ETH Zurich, is the mechanically simplest, controllable, flying machine in existence. It has only one moving part (the rotating propeller), but can still fully control its position in space. The vehicle features no additional actuators or aerodynamic surfaces. 

Our research group aims at pushing the boundaries of flying machines by asking fundamental questions, such as “what is the minimum number of moving parts necessary for controlled flight?” All common flying vehicles require multiple moving parts: conventional quadrocopters, for example, have four moving parts (the four fixed-pitch propellers).  A helicopter’s main rotor requires a complicated swashplate mechanism with many moving parts, and a typical fixed-wing airplane requires one moving part for each of the ailerons, rudder, elevator, and main propulsion. Samara-type vehicles look like maple seeds (or samaras), and rotate while flying. These vehicles typically need two actuators to be controllable in position. Some toys have only one moving part, but are not controllable in the horizontal direction.

We set out to create a vehicle that has only a single moving part. The theoretical basis for this followed from previous work on quadrocopters, where we showed that a quadrocopter can maintain flight despite the complete loss of one, two, or three propellers. To do this, we redefined the definition of “to hover”: now the vehicle may rotate at a constant angular velocity as long as it remains approximately at the same point in space. Of course, the vehicle then has to be controlled near this hover solution, to allow it to track trajectories and reject disturbances.

This is tricky for the Monospinner, as it has only a single input (the thrust force) to control its states (compared to a conventional quadrocopter that has four inputs). We designed a cascaded controller (Fig. 2): the faster inner loop controls the thrust direction, while the slower outer loop controls the vehicle’s acceleration and thereby position. Roughly speaking, the single control input (the thrust magnitude) is decomposed into two parts, the average part of the thrust (calculated by the outer loop) which determines the acceleration of the Monospinner and the deviation from the average thrust (calculated from the inner loop) which controls its orientation.

Fig. 2. Cascaded control structure: the outer position controller defines a desired acceleration, where the inner attitude controller defines the vehicle’s attitude.

After the theoretical analysis, we proceeded to build a vehicle. However, there are a lot of uncertainties in the real world: one example is that we do not have a good aerodynamic model of such a complex rotating object. In addition, there are uncertainties such as the mass distribution or the position of the vehicle’s center of mass. We set out to design something that would fly even if we did not know the parameters exactly. We evaluated different designs using Monte Carlo simulations, where we sample different perturbations from the above-mentioned uncertainties and use simulations to test whether our controller would still work. We did this until we found a configuration that works in most situations: the resulting Y-shaped vehicle is shown in Fig. 3 below.

Fig. 3. Monte Carlo simulation results, showing how likely a vehicle is to be controllable (the color scale indicates the likelihood of a crash) as a function of where the vehicle’s electronics are placed. The vehicle’s approximate size and shape are based on existing vehicles.* The vehicle has three main components, which are approximately equally massive: the battery, electronics, and the motor/propeller. By fixing the positions of the propeller and the battery as two vertices of an equilateral triangle, a Monte Carlo analysis was conducted for different positions of the electronics in the vicinity of the third triangle vertex. The final electronics position was chosen as a compromise between low likelihood of failure (as determined by the Monte Carlo simulations) and other considerations (such as ease of mechanical construction). This location is plotted with a red dashed line.

*Link to research

The control strategy only works near hover, where the vehicle spins about 4 revolutions per second. To get the vehicle near the operating angular velocity, we built a passive platform (Fig. 4 below), which has a vertically oriented bearing allowing the Monospinner to rotate freely. The rotation is achieved through the reaction torque of the propeller, and the thrust is slowly ramped up from zero. Once the vehicle is sufficiently near the hover angular velocity, the controller is switched on and the vehicle jumps off the platform. The platform ensures that the take-off condition is near the hover solution for each experiment.  Once we were confident that the vehicle would reliably work from such a controlled starting point, we pushed our luck, and we found out that the system is robust enough to recover after it is thrown into the air like a Frisbee (see the video).

There are still open questions, which we will explore in future work: for example, we plan to refine the control strategy to allow the Monospinnner to recover from a larger range of initial conditions.

Fig. 4. The Monospinner resting on the takeoff platform, which allows the vehicle to freely rotate about its axis of rotation, so that the vehicle can take off near its hover angular velocity and orientation. We have since shown that the Monospinner can also be hand-launched, by throwing it like a Frisbee.

If you liked this article, you may also be interested in:

• Three new quadrotor videos demonstrate agile control and the power of machine learning
• Raffaello D’Andrea at TED2016: Novel flying machines and swarms of tiny flying robots
• Behind the scenes at TED Global: Raffaello D’Andrea and team demo amazing quadrotor “athletes”
• Quadrocopters learn from prior experience to improve slalom flying

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This Robotics By Invitation contribution is part of Robohub’s Jobs Focus.

There may be some regional effects that result in an immediate increase in jobs (for example, setting up a new manufacturing plant and hiring workers to maintain the machines), but the global effect is indisputable: overall, robots replace human workers.

What is also true, however, is that robots create jobs as well.  This is simply Economics 101: there is a redistribution of labor from low skilled jobs – what robots can do now, and the foreseeable future – to higher skilled jobs. An analogy from the North Carolina Department of Agriculture: “In 1790, 93% of the population of the United States was rural, most of them farmers. By 1990, only 200 years later, barely 2% of our population are farmers.”  What is also true is that there are many more software engineers now than there were in 1790; or mechanics; or physiotherapists; or professional athletes; or artists.

So the debate about robots replacing human workers is, for the most part, a tired and old one; just replace the word ‘robot’ with any productivity-enhancing tool or development. And as long as the process is gradual, one can reasonably argue that society benefits as a whole.

But the question does have merit, because human workers are at an artificial disadvantage relative to their robot counterparts, and the culprit is artificially low interest rates.  Large companies such as Procter and Gamble can issue 10 year corporate bonds that have astronomically low yields of 2.3%.  With money so cheap, productivity tools – such as robots – that would not be economically viable under normal interest rates and yields are now a bargain.  Why should a company ‘rent’ labor (a human worker) when it can ‘buy’ it (a robot)?  Have we not seen this storyline before?

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[Photo credit: Petr Kratochvil.]

In my answer I have tried (as much as is humanly possible) to put myself in the role of an alien dispassionately analyzing the situation.  And when I do, I keep returning to the following conclusion: the best possible outcome for humanity would be for robots to not play any part (with the possible exception of purely defensive roles such as defusing mines) in warfare whatsoever.

If I were an alien, this is what I would first observe:

1. The ability for the average human to create tools that can harm others is rapidly increasing. This is especially the case for robots and intelligent machines: sensors, actuators, computing platforms, power systems, and other enabling technologies are continually becoming less expensive, more powerful, and more widely available. The result is that increasingly large numbers of people are becoming capable of delivering payloads accurately and over great distances.
2. The knowledge required to create robots, intelligent machines, and the algorithms that bring them to life are widely accessible and impossible to suppress; knowledge wants to be free in the same way that entropy wants to increase.
3. Humans have an incredibly strong sense of fairness, and their own research supports this. One of their greatest triumphs as a species is their ability to channel traits such as tribalism, aggression, and competitiveness – the same traits that lead to warfare – to a benign medium that results in their great joy and entertainment, not to mention a significant economic activity: organized sports. Central to this incredible accomplishment is the establishment of well-defined rules and regulations that strive to ensure a fair competition.

And this is what I would then conclude:

Dominant powers are being seduced by the advantages that robots can bring to the battlefield.  In the short term, this is a perfectly rational strategy. In the long term, however, this leads to an arms race. Even though a dominant power may be able to maintain its lead by continually developing robotic weapons, the capabilities of its adversaries, while inferior, will co-develop and eventually reach levels that will allow them to inflict catastrophic damage. 

Furthermore, asymmetric warfare insidiously erodes the sense of fairness outlined in point 3, with detrimental consequences for both sides.  The losing side is disenfranchised, which coupled with points 1 and 2 above, is extremely destabilizing. The winning side loses its moral compass and the fabric that holds its society together begins to unravel, leading to home-grown disenfranchisement and destabilization there as well. 

The net result of this robotic arms race will be a high-volatility stalemate, with dangerous weapons available to the masses and a lack of social restraint to prevent their indiscriminate use.    

If I were an alien, and thus immune to personal and economic factors that could influence my impartiality (such as having a loved one in combat, or being employed by a weapons dealer or manufacturer), I could only conclude that humanity would greatly benefit from imposing strict and far-reaching bans on the use of robotic technology in warfare.

As a human, not only am I skeptical that this will happen, I admit that my personal views are situation dependent: if my daughter were in combat, I wouldn’t care about asymmetry or fairness, I would want her to be as safe as possible. I don’t think that this makes me a hypocrite, it just makes me human.

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1. Continue to fund robotics competitions.  Not only is this a great way to educate researchers on how to build real systems, but the approaches adopted by the winning teams are highly correlated to what is feasible with today’s technology. Also, the challenges these teams encounter in a one-year competition cycle are similar to those faced by a young startup in its first year of existence. This funding approach could be expanded to include team projects that create large-scale public installations because, like startups, they too must be reliable and robust to succeed in a public, real-world context.  Finally, one must also make sure that entrepreneurs know about these competitions and team projects, and that they have the right incentives to attend.
2. Provide funding that allows freshly-minted PhDs to transition from fundamental research to applied research with a specific business focus, and at the same time provide support from business mentors and entrepreneurs.  A pre-startup phase, if you will. This not only brings the research closer to application, but it also gives individuals an appreciation of what it takes to commercialize their research, without the immediacy and constraints of being a startup.  A good example of this kind of funding scheme is the Pioneer Fellowships program established at ETH Zurich.

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