Small flying robots can perch and move objects 40 times their weight

via Stanford University

Small flying robots can perch and move objects 40 times their weight with the help of powerful winches and two previous inventions – gecko adhesives and microspines.

A closed door is just one of many obstacles that poses no barrier to a new type of flying, micro, tugging robot called a FlyCroTug. Outfitted with advanced gripping technologies and the ability to move and pull on objects around it, two FlyCroTugs can jointly lasso the door handle and heave the door open.

Developed in the labs of Mark Cutkosky, the Fletcher Jones Chair in the School of Engineering at Stanford University, and Dario Floreano at the École Polytechnique Fédérale de Lausanne in Switzerland, FlyCroTugs are micro air vehicles that the researchers have modified so the vehicles can anchor themselves to various surfaces using adhesives inspired by the feet of geckos and insects, previously developed in Cutkosky’s lab.

With these attachment mechanisms, FlyCroTugs can pull objects up to 40 times their weight, like door handles in one scenario, or cameras and water bottles in a rescue situation. Similar vehicles can only lift objects about twice their own weight using aerodynamic forces.

“When you’re a small robot, the world is full of large obstacles,” said Matthew Estrada, a graduate student at Stanford and lead author of a paper on FlyCroTugs, published Oct. 25 in Science Robotics. “Combining the aerodynamic forces of our aerial vehicle along with interaction forces that we generate with the attachment mechanisms resulted in something that was very mobile, very forceful and micro as well.”

The researchers say the FlyCroTugs’ small size means they can navigate through snug spaces and fairly close to people, making them useful for search and rescue. Holding tightly to surfaces as they tug, the tiny robots could potentially move pieces of debris or position a camera to evaluate a treacherous area.

Taking a cue from nature

As with most projects in Cutkosky’s lab, the FlyCroTugs were inspired by the natural world. Hoping to have an air vehicle that was fast, small and highly maneuverable but also able to move large loads, the researchers looked to wasps.

“Wasps can fly rapidly to a piece of food, and then if the thing’s too heavy to take off with, they drag it along the ground. So this was sort of the beginning inspiration for the approach we took,” said Cutkosky, who is a co-author of the paper.

The researchers read studies on wasp prey capture and transport, which identify the ratio of flight-related muscle to total mass that determines whether a wasp flies with its prey or drags it. They also followed the lead of the wasp in having different attachment options depending on where the FlyCroTugs land.

For smooth surfaces, the robots have gecko grippers, non-sticky adhesives that mimic a gecko’s intricate toe structures and hold on by creating intermolecular forces between the adhesive and the surface. For rough surfaces, these robots are equipped with 32 microspines, a series of fishhook-like metal spines that can individually latch onto small pits in a surface.

Each FlyCroTug has a winch with a cable and either microspines or gecko adhesive in order to tug. Beyond those fixed features they are otherwise highly modifiable. The location of the grippers can vary depending on the surface where they will be landing, and the researchers can also add parts for ground-based movement, such as wheels. Getting all of these features onto a small air vehicle with twice the weight of a golf ball was no small feat, according to the researchers.

“People tend to think of drones as machines that fly and observe the world, but flying insects do many other things – such as walking, climbing, grasping, building – and social insects can even cooperate to multiply forces,” said Floreano, who was senior author on the paper. “With this work, we show that small drones capable of anchoring to the environment and collaborating with fellow drones can perform tasks typically assigned to humanoid robots or much larger machines.”

Interacting with the world

Drones and other small flying robots may seem like all the rage these days but the FlyCroTugs – with their ability to navigate to remote locations, anchor and pull – fall into a more specific niche, according to Cutkosky.

“There are many laboratories around the world that are starting to work with small drones or air vehicles, but if you look at the ones that are also thinking about how these little vehicles can interact physically with the world, it’s a much smaller set,” he said.

The researchers can successfully open a door with two FlyCroTugs. They also had one fly atop a crumbling structure and haul up a camera to see inside. Next, they hope to work on autonomous control and the logistics of flying several vehicles at once.

“The tools to create vehicles like this are becoming more accessible,” said Estrada. “I’m excited at the prospect of increasingly incorporating these attachment mechanisms into the designer’s tool belt, enabling robots to take advantage of interaction forces with their environment and put these to useful ends.”

Learn more: Stanford researchers modify small flying robots to anchor onto surfaces and pull heavy loads



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A new navigation algorithm will help micro aerial vehicles find their way home has exciting implications

via BU Blogs

In a paper to be published in Unmanned Systems, a group of researchers have discovered that a navigation algorithm proposed by Baddeley et al. is able to allow MAVs to find their way back to an earlier visited location fairly quickly and efficiently, allowing it to function more similar to a flying insect.

Here’s a riddle an unmanned systems engineer might ask you someday: how does a Micro Aerial Vehicle (also known as an MAV) resemble a flying insect? Well, you might say in response, both MAVs and flying insects must navigate unknown complex environments. But an insect’s brain is small, and an MAV cannot perform heavy computations and often does not have a good sensor system. As a result, both the insect and the MAV must rely on simple, efficient navigation mechanisms that do not overtax their capacities. When developing an MAV, the challenge is therefore to write a navigation algorithm that works well without requiring lots of computing power.

One such algorithm, proposed by Baddeley et al., uses cameras to determine if a view is familiar to an MAV. If the view is familiar, the MAV must have passed that way before. By evaluating many such views for familiarity, the MAV can determine the correct direction to an earlier visited location. A small neural network is also used to store and recapitulate a route so that the initial location can be found. Baddeley et al. assert that this algorithm would make it unnecessary for the MAV to construct a map of its surroundings-a process that is frequently power-intensive.

A team of scientists comprising Gerald J. J. van Dalen, Kimberly N. McGuire, and Guido C. H. E. de Croon have put this algorithm to the test by using it in more realistic environments than those created by Baddeley et al. for their own experiments with the algorithm. The team also tested the algorithm on different image representations (raw pixels, colours and spatially invariant information) to see the impact of different image parameters. In addition, two methods of view representation were tested to determine which one produced superior results: a stored set of image representations (referred to as perfect memory) or an unsupervised neural network (known as Infomax). The sensitivity of the algorithm was tested during both rotation and translation as well. vIn the rotation condition, the MAV was made to perform a 360¡ã turn at a fixed location in the environment, in steps of 5¡ã. The views ‘seen’ by the MAV during this exercise are compared to a previously stored image drawn from that location. The team’s hypothesis is that familiarity should improve as the current view begins to resemble the stored image.

In the translation condition, the MAV was made to move from a given point along a given path towards a location in the environment. Again, the views ‘seen’ by the MAV during this exercise are compared to a previously stored image drawn from that location. The team’s hypothesis is that familiarity should improve as the distance between the MAV and the view in the stored image gets smaller. To test the sensitivity of the algorithm, the team has experimented with increasing the distance between the MAV and the view in the stored image, as well as increasing the heading angle at which the MAV approaches said view.

The results of this study suggest that the algorithm is a promising one. When tested, the MAVs performed well in several ways: they could accurately find their way back to an earlier visited location, they could do so fairly quickly, and they did not use very much computing power to achieve this. This has exciting implications. As this algorithm is computationally efficient, it could probably be applied to most MAVs to give them homing capabilities. You’d be able to send your MAVs out to collect data, secure in the knowledge that wherever they go, they’ll be able to come straight back to you afterwards.

Learn more: Visual Homing for Micro Aerial Vehicles using Scene Familiarity


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The Latest on: Micro aerial vehicles

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Kestrel inspires unpowered, autonomous glider to climb higher



Researchers at the RMIT University, Melbourne, Australia have drawn inspiration from the way kestrels hover above their prey to develop an autonomous fixed-wing micro air vehicle (MAV) that can gain height from convenient updrafts.

The results are published today, Friday 18th December, in the journal Bioinspiration & Biomimetics.

“It’s long been known the birds take advantage of upward air currents to save energy when flying” explains Alex Fisher, a lead author of the paper. “This ‘boost’ of upward-moving air can be found when the wind hits a large obstacle, like a cliff or mountain range, and to a smaller extent close to man-made obstacles like buildings.”

“We envisage that in the future, MAVs will be used for many tasks in urban environments, such as delivering packages, performing surveillance, and search and rescue” he continues. “Using these updrafts would make them more efficient and therefore extend their working range.”

“If you’re familiar with the kestrel, you may know they’ve got a unique way of hunting – hovering over a location without flapping their wings. This allows them to keep their heads still with incredible precision, helping them spot prey on the ground. The preciseness at which they hold position led us to thinking we could try this ‘wind-hovering’ technique on our MAV.”

The researchers used a commercially available polystyrene foam sail-plane as their test platform.

“We were lucky in a sense that these were lightweight, as it allowed us to test the MAV easily in the field” continues Fisher. “This MAV we chose had a number of advantages, including the ability to fly well in light winds, and large control surfaces making it more nimble in the air.”

After developing a control algorithm and installing it on a 36 mm x 26 mm control board which was interfaced with a GPS and magnetometer, the MAV was flown at two test locations, near a hill and close to a building.

At the hill-side, the MAV was able to gain approximately 360 ft (120 m) in height, and could fly autonomously until the control batteries lost power.

Tests close to the building proved more difficult, with the MAV only capable of sustaining flight for around 20 seconds.

“The MAV has a relatively narrow range of wind speeds at which it can soar without power” concludes Fisher. “Birds are able to overcome this problem to some extent by changing the shape of their wings or moving their feathers.”

“Our human pilot was able to outperform our control algorithm under gusty conditions – but not by much! Long-lasting gusts and lulls were a particular problem, but we learnt a lot from these tests.”

Fisher and his colleagues are now working on ways to mimic the changing arrangement of the wing and feathers on hovering birds, to improve the soaring performance of their MAV.

Read more: Kestrel inspires unpowered, autonomous glider to climb higher



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TU Delft researchers design and build the world’s smallest autopilot for micro aircraft


The world’s smallest autopilot

Researcher Bart Remes and his team of the Micro Aerial Vehicle Laboratory at the TU Delft faculty of Aerospace Engineering have designed, built and tested the world’s smallest open source autopilot for small unmanned aircraft. A smaller – and lighter – autopilot allows these small flying robots to fly longer, fit into narrower spaces or carry more payloads, such as cameras. That makes them more suitable to be used in for example rescue operations. Remes: “Our aim? Make MAVs so small and light that every fireman can fit one in his pocket.”

The world’s smallest autopilot for micro aerial vehicles – small flying robots that can be used in safety and rescue operations – is called Lisa/S. It weighs 1.9 grams, more than 30 grams less than its predecessor. The autopilot measures 2 cm by 2 cm. Bart Remes, project manager at the Micro Aerial Vehicle Laboratory at TU Delft: “We programmed new software, Superbitrf, that keeps the autopilot connected to a ground station and a normal RC transmitter at the same time.” This combination of functions made it possible to miniaturize the autopilot. Making the autopilot smaller and lighter allows a micro aerial vehicle to stay up in the air longer and carry heavier cameras and sensors. This makes it easier to use MAVs in for example search and rescue operations.

Open source

The research team have chosen to develop Lisa/s open source to make it possible for users to test it and come up with suggestions for improvement. Making all the details available online also helps to make MAVs easily accessible for all. Remes: “Our aim is to make MAVs as commonplace as smartphones and laptops. Farmers can use MAVs to inspect crops for example. Our dream is that every fire fighter carries a MAV in his breast pocket to use for inspections of collapsed or burning buildings without having to go inside.”

Read more . . .


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