Tag Archives: motors
#438553 New Drone Software Handles Motor ...
Good as some drones are becoming at obstacle avoidance, accidents do still happen. And as far as robots go, drones are very much on the fragile side of things. Any sort of significant contact between a drone and almost anything else usually results in a catastrophic, out-of-control spin followed by a death plunge to the ground. Bad times. Bad, expensive times.
A few years ago, we saw some interesting research into software that can keep the most common drone form factor, the quadrotor, aloft and controllable even after the failure of one motor. The big caveat to that software was that it relied on GPS for state estimation, meaning that without a GPS signal, the drone is unable to get the information it needs to keep itself under control. In a paper recently accepted to RA-L, researchers at the University of Zurich report that they have developed a vision-based system that brings state estimation completely on-board. The upshot: potentially any drone with some software and a camera can keep itself safe even under the most challenging conditions.
A few years ago, we wrote about first author Sihao Sun’s work on high speed controlled flight of a quadrotor with a non-functional motor. But that innovation relied on an external motion capture system. Since then, Sun has moved from Tu Delft to Davide Scaramuzza’s lab at UZH, and it looks like he’s been able to combine his work on controlled spinning flight with the Robotics and Perception Group’s expertise in vision. Now, a downward-facing camera is all it takes for a spinning drone to remain stable and controllable:
Remember, this software isn’t just about guarding against motor failure. Drone motors themselves don’t just up and fail all that often, either with respect to their software or hardware. But they do represent the most likely point of failure for any drone, usually because when you run into something, what ultimately causes your drone to crash is damage to a motor or a propeller that causes loss of control.
The reason that earlier solutions relied on GPS was because the spinning drone needs a method of state estimation—that is, in order to be closed-loop controllable, the drone needs to have a reasonable understanding of what its position is and how that position is changing over time. GPS is an easy way to take care of this, but GPS is also an external system that doesn’t work everywhere. Having a state estimation system that’s completely internal to the drone itself is much more fail safe, and Sun got his onboard system to work through visual feature tracking with a downward-facing camera, even as the drone is spinning at over 20 rad/s.
While the system works well enough with a regular downward-facing camera—something that many consumer drones are equipped with for stabilization purposes—replacing it with an event camera (you remember event cameras, right?) makes the performance even better, especially in low light.
For more details on this, including what you’re supposed to do with a rapidly spinning partially disabled quadrotor (as well as what it’ll take to make this a standard feature on consumer hardware), we spoke with Sihao Sun via email.
IEEE Spectrum: what usually happens when a drone spinning this fast lands? Is there any way to do it safely?
Sihao Sun: Our experience shows that we can safely land the drone while it is spinning. When the range sensor measurements are lower than a threshold (around 10 cm, indicating that the drone is close to the ground), we switch off the rotors. During the landing procedure, despite the fast spinning motion, the thrust direction oscillates around the gravity vector, thus the drone touches the ground with its legs without damaging other components.
Can your system handle more than one motor failure?
Yes, the system can also handle the failure of two opposing rotors. However, if two adjacent rotors or more than two rotors fail, our method cannot save the quadrotor. Some research has shown that it is possible to control a quadrotor with only one remaining rotor. But the drone requires a very special inertial property, which is hard to satisfy in real applications.
How different is your system's performance from a similar system that relies on GPS, in a favorable environment?
In a favorable environment, our system outperforms those relying on GPS signals because it obtains better position estimates. Since a damaged quadrotor spins fast, the accelerometer readings are largely affected by centrifugal forces. When the GPS signal is lost or degraded, a drone relying on GPS needs to integrate these biased accelerometer measurements for position estimation, leading to large position estimation errors. Feeding these erroneous estimates to the flight controller can easily crash the drone.
When you say that your solution requires “only onboard sensors and computation,” are those requirements specialized, or would they be generally compatible with the current generation of recreational and commercial quadrotors?
We use an NVIDIA Jetson TX2 to run our solution, which includes two parts: the control algorithm and the vision-based state estimation algorithm. The control algorithm is lightweight; thus, we believe that it is compatible with the current generation of quadrotors. On the other hand, the vision-based state estimation requires relatively more computational resources, which may not be affordable for cheap recreational platforms. But this is not an issue for commercial quadrotors because many of them have more powerful processors than a TX2.
What else can event cameras be used for, in recreational or commercial applications?
Many drone applications can benefit from event cameras, especially those in high-speed or low-light conditions, such as autonomous drone racing, cave exploration, drone delivery during night time, etc. Event cameras also consume very little power, which is a significant advantage for energy-critical missions, such as planetary aerial vehicles for Mars explorations. Regarding space applications, we are currently collaborating with JPL to explore the use of event cameras to address the key limitations of standard cameras for the next Mars helicopter.
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#437747 High Performance Ornithopter Drone Is ...
The vast majority of drones are rotary-wing systems (like quadrotors), and for good reason: They’re cheap, they’re easy, they scale up and down well, and we’re getting quite good at controlling them, even in very challenging environments. For most applications, though, drones lose out to birds and their flapping wings in almost every way—flapping wings are very efficient, enable astonishing agility, and are much safer, able to make compliant contact with surfaces rather than shredding them like a rotor system does. But flapping wing have their challenges too: Making flapping-wing robots is so much more difficult than just duct taping spinning motors to a frame that, with a few exceptions, we haven’t seen nearly as much improvement as we have in more conventional drones.
In Science Robotics last week, a group of roboticists from Singapore, Australia, China, and Taiwan described a new design for a flapping-wing robot that offers enough thrust and control authority to make stable transitions between aggressive flight modes—like flipping and diving—while also being able to efficiently glide and gently land. While still more complex than a quadrotor in both hardware and software, this ornithopter’s advantages might make it worthwhile.
One reason that making a flapping-wing robot is difficult is because the wings have to move back and forth at high speed while electric motors spin around and around at high speed. This requires a relatively complex transmission system, which (if you don’t do it carefully), leads to weight penalties and a significant loss of efficiency. One particular challenge is that the reciprocating mass of the wings tends to cause the entire robot to flex back and forth, which alternately binds and disengages elements in the transmission system.
The researchers’ new ornithopter design mitigates the flexing problem using hinges and bearings in pairs. Elastic elements also help improve efficiency, and the ornithopter is in fact more efficient with its flapping wings than it would be with a rotary propeller-based propulsion system. Its thrust exceeds its 26-gram mass by 40 percent, which is where much of the aerobatic capability comes from. And one of the most surprising findings of this paper was that flapping-wing robots can actually be more efficient than propeller-based aircraft.
One of the most surprising findings of this paper was that flapping-wing robots can actually be more efficient than propeller-based aircraft
It’s not just thrust that’s a challenge for ornithopters: Control is much more complex as well. Like birds, ornithopters have tails, but unlike birds, they have to rely almost entirely on tail control authority, not having that bird-level of control over fine wing movements. To make an acrobatic level of control possible, the tail control surfaces on this ornithopter are huge—the tail plane area is 35 percent of the wing area. The wings can also provide some assistance in specific circumstances, as by combining tail control inputs with a deliberate stall of the things to allow the ornithopter to execute rapid flips.
With the ability to take off, hover, glide, land softly, maneuver acrobatically, fly quietly, and interact with its environment in a way that’s not (immediately) catastrophic, flapping-wing drones easily offer enough advantages to keep them interesting. Now that ornithopters been shown to be even more efficient than rotorcraft, the researchers plan to focus on autonomy with the goal of moving their robot toward real-world usefulness.
“Efficient flapping wing drone arrests high-speed flight using post-stall soaring,” by Yao-Wei Chin, Jia Ming Kok, Yong-Qiang Zhu, Woei-Leong Chan, Javaan S. Chahl, Boo Cheong Khoo, and Gih-Keong Lau from from Nanyang Technological University in Singapore, National University of Singapore, Defence Science and Technology Group in Canberra, Australia, Qingdao University of Technology in Shandong, China, University of South Australia in Mawson Lakes, and National Chiao Tung University in Hsinchu, Taiwan, was published in Science Robotics. Continue reading
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