Tag Archives: walking robot

#438755 Soft Legged Robot Uses Pneumatic ...

Soft robots are inherently safe, highly resilient, and potentially very cheap, making them promising for a wide array of applications. But development on them has been a bit slow relative to other areas of robotics, at least partially because soft robots can’t directly benefit from the massive increase in computing power and sensor and actuator availability that we’ve seen over the last few decades. Instead, roboticists have had to get creative to find ways of achieving the functionality of conventional robotics components using soft materials and compatible power sources.

In the current issue of Science Robotics, researchers from UC San Diego demonstrate a soft walking robot with four legs that moves with a turtle-like gait controlled by a pneumatic circuit system made from tubes and valves. This air-powered nervous system can actuate multiple degrees of freedom in sequence from a single source of pressurized air, offering a huge reduction in complexity and bringing a very basic form of decision making onto the robot itself.

Generally, when people talk about soft robots, the robots are only mostly soft. There are some components that are very difficult to make soft, including pressure sources and the necessary electronics to direct that pressure between different soft actuators in a way that can be used for propulsion. What’s really cool about this robot is that researchers have managed to take a pressure source (either a single tether or an onboard CO2 cartridge) and direct it to four different legs, each with three different air chambers, using an oscillating three valve circuit made entirely of soft materials.

Photo: UCSD

The pneumatic circuit that powers and controls the soft quadruped.

The inspiration for this can be found in biology—natural organisms, including quadrupeds, use nervous system components called central pattern generators (CPGs) to prompt repetitive motions with limbs that are used for walking, flying, and swimming. This is obviously more complicated in some organisms than in others, and is typically mediated by sensory feedback, but the underlying structure of a CPG is basically just a repeating circuit that drives muscles in sequence to produce a stable, continuous gait. In this case, we’ve got pneumatic muscles being driven in opposing pairs, resulting in a diagonal couplet gait, where diagonally opposed limbs rotate forwards and backwards at the same time.

Diagram: Science Robotics

(J) Pneumatic logic circuit for rhythmic leg motion. A constant positive pressure source (P+) applied to three inverter components causes a high-pressure state to propagate around the circuit, with a delay at each inverter. While the input to one inverter is high, the attached actuator (i.e., A1, A2, or A3) is inflated. This sequence of high-pressure states causes each pair of legs of the robot to rotate in a direction determined by the pneumatic connections. (K) By reversing the sequence of activation of the pneumatic oscillator circuit, the attached actuators inflate in a new sequence (A1, A3, and A2), causing (L) the legs of the robot to rotate in reverse. (M) Schematic bottom view of the robot with the directions of leg motions indicated for forward walking.

Diagram: Science Robotics

Each of the valves acts as an inverter by switching the normally closed half (top) to open and the normally open half (bottom) to closed.

The circuit itself is made up of three bistable pneumatic valves connected by tubing that acts as a delay by providing resistance to the gas moving through it that can be adjusted by altering the tube’s length and inner diameter. Within the circuit, the movement of the pressurized gas acts as both a source of energy and as a signal, since wherever the pressure is in the circuit is where the legs are moving. The simplest circuit uses only three valves, and can keep the robot walking in one single direction, but more valves can add more complex leg control options. For example, the researchers were able to use seven valves to tune the phase offset of the gait, and even just one additional valve (albeit of a slightly more complex design) could enable reversal of the system, causing the robot to walk backwards in response to input from a soft sensor. And with another complex valve, a manual (tethered) controller could be used for omnidirectional movement.

This work has some similarities to the rover that JPL is developing to explore Venus—that rover isn’t a soft robot, of course, but it operates under similar constraints in that it can’t rely on conventional electronic systems for autonomous navigation or control. It turns out that there are plenty of clever ways to use mechanical (or in this case, pneumatic) intelligence to make robots with relatively complex autonomous behaviors, meaning that in the future, soft (or soft-ish) robots could find valuable roles in situations where using a non-compliant system is not a good option.

For more on why we should be so excited about soft robots and just how soft a soft robot needs to be, we spoke with Michael Tolley, who runs the Bioinspired Robotics and Design Lab at UCSD, and Dylan Drotman, the paper’s first author.

IEEE Spectrum: What can soft robots do for us that more rigid robotic designs can’t?

Michael Tolley: At the very highest level, one of the fundamental assumptions of robotics is that you have rigid bodies connected at joints, and all your motion happens at these joints. That's a really nice approach because it makes the math easy, frankly, and it simplifies control. But when you look around us in nature, even though animals do have bones and joints, the way we interact with the world is much more complicated than that simple story. I’m interested in where we can take advantage of material properties in robotics. If you look at robots that have to operate in very unknown environments, I think you can build in some of the intelligence for how to deal with those environments into the body of the robot itself. And that’s the category this work really falls under—it's about navigating the world.

Dylan Drotman: Walking through confined spaces is a good example. With the rigid legged robot, you would have to completely change the way that the legs move to walk through a confined space, while if you have flexible legs, like the robot in our paper, you can use relatively simple control strategies to squeeze through an area you wouldn’t be able to get through with a rigid system.

How smart can a soft robot get?

Drotman: Right now we have a sensor on the front that's connected through a fluidic transmission to a bistable valve that causes the robot to reverse. We could add other sensors around the robot to allow it to change direction whenever it runs into an obstacle to effectively make an electronics-free version of a Roomba.

Tolley: Stepping back a little bit from that, one could make an argument that we’re using basic memory elements to generate very basic signals. There’s nothing in principle that would stop someone from making a pneumatic computer—it’s just very complicated to make something that complex. I think you could build on this and do more intelligent decision making, but using this specific design and the components we’re using, it’s likely to be things that are more direct responses to the environment.

How well would robots like these scale down?

Drotman: At the moment we’re manufacturing these components by hand, so the idea would be to make something more like a printed circuit board instead, and looking at how the channel sizes and the valve design would affect the actuation properties. We’ll also be coming up with new circuits, and different designs for the circuits themselves.

Tolley: Down to centimeter or millimeter scale, I don’t think you’d have fundamental fluid flow problems. I think you’re going to be limited more by system design constraints. You’ll have to be able to locomote while carrying around your pressure source, and possibly some other components that are also still rigid. When you start to talk about really small scales, though, it's not as clear to me that you really need an intrinsically soft robot. If you think about insects, their structural geometry can make them behave like they’re soft, but they’re not intrinsically soft.

Should we be thinking about soft robots and compliant robots in the same way, or are they fundamentally different?

Tolley: There’s certainly a connection between the two. You could have a compliant robot that behaves in a very similar way to an intrinsically soft robot, or a robot made of intrinsically soft materials. At that point, it comes down to design and manufacturing and practical limitations on what you can make. I think when you get down to small scales, the two sort of get connected.

There was some interesting work several years ago on using explosions to power soft robots. Is that still a thing?

Tolley: One of the opportunities with soft robots is that with material compliance, you have the potential to store energy. I think there’s exciting potential there for rapid motion with a soft body. Combustion is one way of doing that with power coming from a chemical source all at once, but you could also use a relatively weak muscle that over time stores up energy in a soft body and then releases it.

Is it realistic to expect complete softness from soft robots, or will they likely always have rigid components because they have to store or generate and move pressurized gas somehow?

Tolley: If you look in nature, you do have soft pumps like the heart, but although it’s soft, it’s still relatively stiff. Like, if you grab a heart, it’s not totally squishy. I haven’t done it, but I’d imagine. If you have a container that you’re pressurizing, it has to be stiff enough to not just blow up like a balloon. Certainly pneumatics or hydraulics are not the only way to go for soft actuators; there has been some really nice work on smart muscles and smart materials like hydraulic electrostatic (HASEL) actuators. They seem promising, but all of these actuators have challenges. We’ve chosen to stick with pressurized pneumatics in the near term; longer term, I think you’ll start to see more of these smart material actuators become more practical.

Personally, I don’t have any problem with soft robots having some rigid components. Most animals on land have some rigid components, but they can still take advantage of being soft, so it’s probably going to be a combination. But I do also like the vision of making an entirely soft, squishy thing. Continue reading

Posted in Human Robots

#438012 Video Friday: These Robots Have Made 1 ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
Let us know if you have suggestions for next week, and enjoy today's videos.

We're proud to announce Starship Delivery Robots have now completed 1,000,000 autonomous deliveries around the world. We were unsure where the one millionth delivery was going to take place, as there are around 15-20 service areas open globally, all with robots doing deliveries every minute. In the end it took place at Bowling Green, Ohio, to a student called Annika Keeton who is a freshman studying pre-health Biology at BGSU. Annika is now part of Starship’s history!

[ Starship ]

I adore this little DIY walking robot- with modular feet and little dials to let you easily adjust the walking parameters, it's an affordable kit that's way more nuanced than most.

It's called Bakiwi, and it costs €95. A squee cover made from feathers or fur is an extra €17. Here's a more serious look at what it can do:

[ Bakiwi ]

Thanks Oswald!

Savva Morozov, an AeroAstro junior, works on autonomous navigation for the MIT mini cheetah robot and reflects on the value of a crowded Infinite Corridor.

[ MIT ]

The world's most advanced haptic feedback gloves just got a huge upgrade! HaptX Gloves DK2 achieves a level of realism that other haptic devices can't match. Whether you’re training your workforce, designing a new product, or controlling robots from a distance, HaptX Gloves make it feel real.

They're the only gloves with true-contact haptics, with patented technology that displace your skin the same way a real object would. With 133 points of tactile feedback per hand, for full palm and fingertip coverage. HaptX Gloves DK2 feature the industry's most powerful force feedback, ~2X the strength of other force feedback gloves. They're also the most accurate motion tracking gloves, with 30 tracked degrees of freedom, sub-millimeter precision, no perceivable latency, and no occlusion.

[ HaptX ]

Yardroid is an outdoor robot “guided by computer vision and artificial intelligence” that seems like it can do almost everything.

These are a lot of autonomous capabilities, but so far, we've only seen the video. So, best not to get too excited until we know more about how it works.

[ Yardroid ]

Thanks Dan!

Since as far as we know, Pepper can't spread COVID, it had a busy year.

I somehow missed seeing that chimpanzee magic show, but here it is:

[ Simon Pierro ] via [ SoftBank Robotics ]

In spite of the pandemic, Professor Hod Lipson’s Robotics Studio persevered and even thrived— learning to work on global teams, to develop protocols for sharing blueprints and code, and to test, evaluate, and refine their designs remotely. Equipped with a 3D printer and a kit of electronics prototyping equipment, our students engineered bipedal robots that were conceptualized, fabricated, programmed, and endlessly iterated around the globe in bedrooms, kitchens, backyards, and any other makeshift laboratory you can imagine.

[ Hod Lipson ]

Thanks Fan!

We all know how much quadrupeds love ice!

[ Ghost Robotics ]

We took the opportunity of the last storm to put the Warthog in the snow of Université Laval. Enjoy!

[ Norlab ]

They've got a long way to go, but autonomous indoor firefighting drones seem like a fantastic idea.

[ CTU ]

Individual manipulators are limited by their vertical total load capacity. This places a fundamental limit on the weight of loads that a single manipulator can move. Cooperative manipulation with two arms has the potential to increase the net weight capacity of the overall system. However, it is critical that proper load sharing takes place between the two arms. In this work, we outline a method that utilizes mechanical intelligence in the form of a whiffletree.

And your word of the day is whiffletree, which is “a mechanism to distribute force evenly through linkages.”

[ DART Lab ]

Thanks Raymond!

Some highlights of robotic projects at FZI in 2020, all using ROS.

[ FZI ]

Thanks Fan!

iRobot CEO Colin Angle threatens my job by sharing some cool robots.

[ iRobot ]

A fascinating new talk from Henry Evans on robotic caregivers.

[ HRL ]

The ANA Avatar XPRIZE semifinals selection submission for Team AVATRINA. The setting is a mock clinic, with the patient sitting on a wheelchair and nurse having completed an initial intake. Avatar enters the room controlled by operator (Doctor). A rolling tray table with medical supplies (stethoscope, pulse oximeter, digital thermometer, oxygen mask, oxygen tube) is by the patient’s side. Demonstrates head tracking, stereo vision, fine manipulation, bimanual manipulation, safe impedance control, and navigation.

[ Team AVATRINA ]

This five year old talk from Mikell Taylor, who wrote for us a while back and is now at Amazon Robotics, is entitled “Nobody Cares About Your Robot.” For better or worse, it really doesn't sound like it was written five years ago.

Robotics for the consumer market – Mikell Taylor from Scott Handsaker on Vimeo.

[ Mikell Taylor ]

Fall River Community Media presents this wonderful guy talking about his love of antique robot toys.

If you enjoy this kind of slow media, Fall River also has weekly Hot Dogs Cool Cats adoption profiles that are super relaxing to watch.

[ YouTube ] Continue reading

Posted in Human Robots

#437745 Video Friday: Japan’s Giant Gundam ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

AWS Cloud Robotics Summit – August 18-19, 2020 – [Online Conference]
CLAWAR 2020 – August 24-26, 2020 – [Virtual Conference]
ICUAS 2020 – September 1-4, 2020 – Athens, Greece
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online Conference]
IROS 2020 – October 25-29, 2020 – Las Vegas, Nev., USA
ICSR 2020 – November 14-16, 2020 – Golden, Co., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

It’s coming together—literally! Japan’s giant Gundam appears nearly finished and ready for its first steps. In a recent video, Gundam Factory Yokohama, which is constructing the 18-meter-tall, 25-ton walking robot, provided an update on the project. The video shows the Gundam getting its head attached—after being blessed by Shinto priests.

In the video update, they say the project is “steadily progressing” and further details will be announced around the end of September.

[ Gundam Factory Yokohama ]

Creating robots with emotional personalities will transform the usability of robots in the real-world. As previous emotive social robots are mostly based on statically stable robots whose mobility is limited, this work develops an animation to real-world pipeline that enables dynamic bipedal robots that can twist, wiggle, and walk to behave with emotions.

So that’s where Cassie’s eyes go.

[ Berkeley ]

Now that the DARPA SubT Cave Circuit is all virtual, here’s a good reminder of how it’ll work.

[ SubT ]

Since July 20, anyone 11+ years of age must wear a mask in closed public places in France. This measure also is highly recommended in many European, African and Persian Gulf countries. To support businesses and public places, SoftBank Robotics Europe unveils a new feature with Pepper: AI Face Mask Detection.

[ Softbank ]

University of Michigan researchers are developing new origami inspired methods for designing, fabricating and actuating micro-robots using heat.These improvements will expand the mechanical capabilities of the tiny bots, allowing them to fold into more complex shapes.

[ University of Michigan ]

Suzumori Endo Lab, Tokyo Tech has created various types of IPMC robots. Those robots are fabricated by novel 3D fabrication methods.

[ Suzimori Endo Lab ]

The most explode-y of drones manages not to explode this time.

[ SpaceX ]

At Amazon, we’re constantly innovating to support our employees, customers, and communities as effectively as possible. As our fulfillment and delivery teams have been hard at work supplying customers with items during the pandemic, Amazon’s robotics team has been working behind the scenes to re-engineer bots and processes to increase safety in our fulfillment centers.

While some folks are able to do their jobs at home with just a laptop and internet connection, it’s not that simple for other employees at Amazon, including those who spend their days building and testing robots. Some engineers have turned their homes into R&D labs to continue building these new technologies to better serve our customers and employees. Their creativity and resourcefulness to keep our important programs going is inspiring.

[ Amazon ]

Australian Army soldiers from 2nd/14th Light Horse Regiment (Queensland Mounted Infantry) demonstrated the PD-100 Black Hornet Nano unmanned aircraft vehicle during a training exercise at Shoalwater Bay Training Area, Queensland, on 4 May 2018.

This robot has been around for a long time—maybe 10 years or more? It makes you wonder what the next generation will look like, and if they can manage to make it even smaller.

[ FLIR ]

Event-based cameras are bio-inspired vision sensors whose pixels work independently from each other and respond asynchronously to brightness changes, with microsecond resolution. Their advantages make it possible to tackle challenging scenarios in robotics, such as high-speed and high dynamic range scenes. We present a solution to the problem of visual odometry from the data acquired by a stereo event-based camera rig.

[ Paper ] via [ HKUST ]

Emys can help keep kindergarteners sitting still for a long time, which is not small feat!

[ Emys ]

Introducing the RoboMaster EP Core, an advanced educational robot that was built to take learning to the next level and provides an all-in-one solution for STEAM-based classrooms everywhere, offering AI and programming projects for students of all ages and experience levels.

[ DJI ]

This Dutch food company Heemskerk uses ABB robots to automate their order picking. Their new solution reduces the amount of time the fresh produce spends in the supply chain, extending its shelf life, minimizing wastage, and creating a more sustainable solution for the fresh food industry.

[ ABB ]

This week’s episode of Pass the Torque features NASA’s Satellite Servicing Projects Division (NExIS) Robotics Engineer, Zakiya Tomlinson.

[ NASA ]

Massachusetts has been challenging Silicon Valley as the robotics capital of the United States. They’re not winning, yet. But they’re catching up.

[ MassTech ]

San Francisco-based Formant is letting anyone remotely take its Spot robot for a walk. Watch The Robot Report editors, based in Boston, take Spot for a walk around Golden Gate Park.

You can apply for this experience through Formant at the link below.

[ Formant ] via [ TRR ]

Thanks Steve!

An Institute for Advanced Study Seminar on “Theoretical Machine Learning,” featuring Peter Stone from UT Austin.

For autonomous robots to operate in the open, dynamically changing world, they will need to be able to learn a robust set of skills from relatively little experience. This talk begins by introducing Grounded Simulation Learning as a way to bridge the so-called reality gap between simulators and the real world in order to enable transfer learning from simulation to a real robot. It then introduces two new algorithms for imitation learning from observation that enable a robot to mimic demonstrated skills from state-only trajectories, without any knowledge of the actions selected by the demonstrator. Connections to theoretical advances in off-policy reinforcement learning will be highlighted throughout.

[ IAS ] Continue reading

Posted in Human Robots

#436186 Video Friday: Invasion of the Mini ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

DARPA SubT Urban Circuit – February 18-27, 2020 – Olympia, Wash., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

There will be a Mini-Cheetah Workshop (sponsored by Naver Labs) a year from now at IROS 2020 in Las Vegas. Mini-Cheetahs for everyone!

That’s just a rendering, of course, but this isn’t:

[ MCW ]

I was like 95 percent sure that the Urban Circuit of the DARPA SubT Challenge was going to be in something very subway station-y. Oops!

In the Subterranean (SubT) Challenge, teams deploy autonomous ground and aerial systems to attempt to map, identify, and report artifacts along competition courses in underground environments. The artifacts represent items a first responder or service member may encounter in unknown underground sites. This video provides a preview of the Urban Circuit event location. The Urban Circuit is scheduled for February 18-27, 2020, at Satsop Business Park west of Olympia, Washington.

[ SubT ]

Researchers at SEAS and the Wyss Institute for Biologically Inspired Engineering have developed a resilient RoboBee powered by soft artificial muscles that can crash into walls, fall onto the floor, and collide with other RoboBees without being damaged. It is the first microrobot powered by soft actuators to achieve controlled flight.

To solve the problem of power density, the researchers built upon the electrically-driven soft actuators developed in the lab of David Clarke, the Extended Tarr Family Professor of Materials. These soft actuators are made using dielectric elastomers, soft materials with good insulating properties, that deform when an electric field is applied. By improving the electrode conductivity, the researchers were able to operate the actuator at 500 Hertz, on par with the rigid actuators used previously in similar robots.

Next, the researchers aim to increase the efficiency of the soft-powered robot, which still lags far behind more traditional flying robots.

[ Harvard ]

We present a system for fast and robust handovers with a robot character, together with a user study investigating the effect of robot speed and reaction time on perceived interaction quality. The system can match and exceed human speeds and confirms that users prefer human-level timing.

In a 3×3 user study, we vary the speed of the robot and add variable sensorimotor delays. We evaluate the social perception of the robot using the Robot Social Attribute Scale (RoSAS). Inclusion of a small delay, mimicking the delay of the human sensorimotor system, leads to an improvement in perceived qualities over both no delay and long delay conditions. Specifically, with no delay the robot is perceived as more discomforting and with a long delay, it is perceived as less warm.

[ Disney Research ]

When cars are autonomous, they’re not going to be able to pump themselves full of gas. Or, more likely, electrons. Kuka has the solution.

[ Kuka ]

This looks like fun, right?

[ Robocoaster ]

NASA is leading the way in the use of On-orbit Servicing, Assembly, and Manufacturing to enable large, persistent, upgradable, and maintainable spacecraft. This video was developed by the Advanced Concepts Lab (ACL) at NASA Langley Research Center.

[ NASA ]

The noisiest workshop by far at Humanoids last month (by far) was Musical Interactions With Humanoids, the end result of which was this:

[ Workshop ]

IROS is an IEEE event, and in furthering the IEEE mission to benefit humanity through technological innovation, IROS is doing a great job. But don’t take it from us – we are joined by IEEE President-Elect Professor Toshio Fukuda to find out a bit more about the impact events like IROS can have, as well as examine some of the issues around intelligent robotics and systems – from privacy to transparency of the systems at play.

[ IROS ]

Speaking of IROS, we hope you’ve been enjoying our coverage. We have already featured Harvard’s strange sea-urchin-inspired robot and a Japanese quadruped that can climb vertical ladders, with more stories to come over the next several weeks.

In the mean time, enjoy these 10 videos from the conference (as usual, we’re including the title, authors, and abstract for each—if you’d like more details about any of these projects, let us know and we’ll find out more for you).

“A Passive Closing, Tendon Driven, Adaptive Robot Hand for Ultra-Fast, Aerial Grasping and Perching,” by Andrew McLaren, Zak Fitzgerald, Geng Gao, and Minas Liarokapis from the University of Auckland, New Zealand.

Current grasping methods for aerial vehicles are slow, inaccurate and they cannot adapt to any target object. Thus, they do not allow for on-the-fly, ultra-fast grasping. In this paper, we present a passive closing, adaptive robot hand design that offers ultra-fast, aerial grasping for a wide range of everyday objects. We investigate alternative uses of structural compliance for the development of simple, adaptive robot grippers and hands and we propose an appropriate quick release mechanism that facilitates an instantaneous grasping execution. The quick release mechanism is triggered by a simple distance sensor. The proposed hand utilizes only two actuators to control multiple degrees of freedom over three fingers and it retains the superior grasping capabilities of adaptive grasping mechanisms, even under significant object pose or other environmental uncertainties. The hand achieves a grasping time of 96 ms, a maximum grasping force of 56 N and it is able to secure objects of various shapes at high speeds. The proposed hand can serve as the end-effector of grasping capable Unmanned Aerial Vehicle (UAV) platforms and it can offer perching capabilities, facilitating autonomous docking.

“Unstructured Terrain Navigation and Topographic Mapping With a Low-Cost Mobile Cuboid Robot,” by Andrew S. Morgan, Robert L. Baines, Hayley McClintock, and Brian Scassellati from Yale University, USA.

Current robotic terrain mapping techniques require expensive sensor suites to construct an environmental representation. In this work, we present a cube-shaped robot that can roll through unstructured terrain and construct a detailed topographic map of the surface that it traverses in real time with low computational and monetary expense. Our approach devolves many of the complexities of locomotion and mapping to passive mechanical features. Namely, rolling movement is achieved by sequentially inflating latex bladders that are located on four sides of the robot to destabilize and tip it. Sensing is achieved via arrays of fine plastic pins that passively conform to the geometry of underlying terrain, retracting into the cube. We developed a topography by shade algorithm to process images of the displaced pins to reconstruct terrain contours and elevation. We experimentally validated the efficacy of the proposed robot through object mapping and terrain locomotion tasks.

“Toward a Ballbot for Physically Leading People: A Human-Centered Approach,” by Zhongyu Li and Ralph Hollis from Carnegie Mellon University, USA.

This work presents a new human-centered method for indoor service robots to provide people with physical assistance and active guidance while traveling through congested and narrow spaces. As most previous work is robot-centered, this paper develops an end-to-end framework which includes a feedback path of the measured human positions. The framework combines a planning algorithm and a human-robot interaction module to guide the led person to a specified planned position. The approach is deployed on a person-size dynamically stable mobile robot, the CMU ballbot. Trials were conducted where the ballbot physically led a blindfolded person to safely navigate in a cluttered environment.

“Achievement of Online Agile Manipulation Task for Aerial Transformable Multilink Robot,” by Fan Shi, Moju Zhao, Tomoki Anzai, Keita Ito, Xiangyu Chen, Kei Okada, and Masayuki Inaba from the University of Tokyo, Japan.

Transformable aerial robots are favorable in aerial manipulation tasks for their flexible ability to change configuration during the flight. By assuming robot keeping in the mild motion, the previous researches sacrifice aerial agility to simplify the complex non-linear system into a single rigid body with a linear controller. In this paper, we present a framework towards agile swing motion for the transformable multi-links aerial robot. We introduce a computational-efficient non-linear model predictive controller and joints motion primitive frame-work to achieve agile transforming motions and validate with a novel robot named HYRURS-X. Finally, we implement our framework under a table tennis task to validate the online and agile performance.

“Small-Scale Compliant Dual Arm With Tail for Winged Aerial Robots,” by Alejandro Suarez, Manuel Perez, Guillermo Heredia, and Anibal Ollero from the University of Seville, Spain.

Winged aerial robots represent an evolution of aerial manipulation robots, replacing the multirotor vehicles by fixed or flapping wing platforms. The development of this morphology is motivated in terms of efficiency, endurance and safety in some inspection operations where multirotor platforms may not be suitable. This paper presents a first prototype of compliant dual arm as preliminary step towards the realization of a winged aerial robot capable of perching and manipulating with the wings folded. The dual arm provides 6 DOF (degrees of freedom) for end effector positioning in a human-like kinematic configuration, with a reach of 25 cm (half-scale w.r.t. the human arm), and 0.2 kg weight. The prototype is built with micro metal gear motors, measuring the joint angles and the deflection with small potentiometers. The paper covers the design, electronics, modeling and control of the arms. Experimental results in test-bench validate the developed prototype and its functionalities, including joint position and torque control, bimanual grasping, the dynamic equilibrium with the tail, and the generation of 3D maps with laser sensors attached at the arms.

“A Novel Small-Scale Turtle-inspired Amphibious Spherical Robot,” by Huiming Xing, Shuxiang Guo, Liwei Shi, Xihuan Hou, Yu Liu, Huikang Liu, Yao Hu, Debin Xia, and Zan Li from Beijing Institute of Technology, China.

This paper describes a novel small-scale turtle-inspired Amphibious Spherical Robot (ASRobot) to accomplish exploration tasks in the restricted environment, such as amphibious areas and narrow underwater cave. A Legged, Multi-Vectored Water-Jet Composite Propulsion Mechanism (LMVWCPM) is designed with four legs, one of which contains three connecting rod parts, one water-jet thruster and three joints driven by digital servos. Using this mechanism, the robot is able to walk like amphibious turtles on various terrains and swim flexibly in submarine environment. A simplified kinematic model is established to analyze crawling gaits. With simulation of the crawling gait, the driving torques of different joints contributed to the choice of servos and the size of links of legs. Then we also modeled the robot in water and proposed several underwater locomotion. In order to assess the performance of the proposed robot, a series of experiments were carried out in the lab pool and on flat ground using the prototype robot. Experiments results verified the effectiveness of LMVWCPM and the amphibious control approaches.

“Advanced Autonomy on a Low-Cost Educational Drone Platform,” by Luke Eller, Theo Guerin, Baichuan Huang, Garrett Warren, Sophie Yang, Josh Roy, and Stefanie Tellex from Brown University, USA.

PiDrone is a quadrotor platform created to accompany an introductory robotics course. Students build an autonomous flying robot from scratch and learn to program it through assignments and projects. Existing educational robots do not have significant autonomous capabilities, such as high-level planning and mapping. We present a hardware and software framework for an autonomous aerial robot, in which all software for autonomy can run onboard the drone, implemented in Python. We present an Unscented Kalman Filter (UKF) for accurate state estimation. Next, we present an implementation of Monte Carlo (MC) Localization and Fast-SLAM for Simultaneous Localization and Mapping (SLAM). The performance of UKF, localization, and SLAM is tested and compared to ground truth, provided by a motion-capture system. Our evaluation demonstrates that our autonomous educational framework runs quickly and accurately on a Raspberry Pi in Python, making it ideal for use in educational settings.

“FlightGoggles: Photorealistic Sensor Simulation for Perception-driven Robotics using Photogrammetry and Virtual Reality,” by Winter Guerra, Ezra Tal, Varun Murali, Gilhyun Ryou and Sertac Karaman from the Massachusetts Institute of Technology, USA.

FlightGoggles is a photorealistic sensor simulator for perception-driven robotic vehicles. The key contributions of FlightGoggles are twofold. First, FlightGoggles provides photorealistic exteroceptive sensor simulation using graphics assets generated with photogrammetry. Second, it provides the ability to combine (i) synthetic exteroceptive measurements generated in silico in real time and (ii) vehicle dynamics and proprioceptive measurements generated in motio by vehicle(s) in flight in a motion-capture facility. FlightGoggles is capable of simulating a virtual-reality environment around autonomous vehicle(s) in flight. While a vehicle is in flight in the FlightGoggles virtual reality environment, exteroceptive sensors are rendered synthetically in real time while all complex dynamics are generated organically through natural interactions of the vehicle. The FlightGoggles framework allows for researchers to accelerate development by circumventing the need to estimate complex and hard-to-model interactions such as aerodynamics, motor mechanics, battery electrochemistry, and behavior of other agents. The ability to perform vehicle-in-the-loop experiments with photorealistic exteroceptive sensor simulation facilitates novel research directions involving, e.g., fast and agile autonomous flight in obstacle-rich environments, safe human interaction, and flexible sensor selection. FlightGoggles has been utilized as the main test for selecting nine teams that will advance in the AlphaPilot autonomous drone racing challenge. We survey approaches and results from the top AlphaPilot teams, which may be of independent interest. FlightGoggles is distributed as open-source software along with the photorealistic graphics assets for several simulation environments, under the MIT license at http://flightgoggles.mit.edu.

“An Autonomous Quadrotor System for Robust High-Speed Flight Through Cluttered Environments Without GPS,” by Marc Rigter, Benjamin Morrell, Robert G. Reid, Gene B. Merewether, Theodore Tzanetos, Vinay Rajur, KC Wong, and Larry H. Matthies from University of Sydney, Australia; NASA Jet Propulsion Laboratory, California Institute of Technology, USA; and Georgia Institute of Technology, USA.

Robust autonomous flight without GPS is key to many emerging drone applications, such as delivery, search and rescue, and warehouse inspection. These and other appli- cations require accurate trajectory tracking through cluttered static environments, where GPS can be unreliable, while high- speed, agile, flight can increase efficiency. We describe the hardware and software of a quadrotor system that meets these requirements with onboard processing: a custom 300 mm wide quadrotor that uses two wide-field-of-view cameras for visual- inertial motion tracking and relocalization to a prior map. Collision-free trajectories are planned offline and tracked online with a custom tracking controller. This controller includes compensation for drag and variability in propeller performance, enabling accurate trajectory tracking, even at high speeds where aerodynamic effects are significant. We describe a system identification approach that identifies quadrotor-specific parameters via maximum likelihood estimation from flight data. Results from flight experiments are presented, which 1) validate the system identification method, 2) show that our controller with aerodynamic compensation reduces tracking error by more than 50% in both horizontal flights at up to 8.5 m/s and vertical flights at up to 3.1 m/s compared to the state-of-the-art, and 3) demonstrate our system tracking complex, aggressive, trajectories.

“Morphing Structure for Changing Hydrodynamic Characteristics of a Soft Underwater Walking Robot,” by Michael Ishida, Dylan Drotman, Benjamin Shih, Mark Hermes, Mitul Luhar, and Michael T. Tolley from the University of California, San Diego (UCSD) and University of Southern California, USA.

Existing platforms for underwater exploration and inspection are often limited to traversing open water and must expend large amounts of energy to maintain a position in flow for long periods of time. Many benthic animals overcome these limitations using legged locomotion and have different hydrodynamic profiles dictated by different body morphologies. This work presents an underwater legged robot with soft legs and a soft inflatable morphing body that can change shape to influence its hydrodynamic characteristics. Flow over the morphing body separates behind the trailing edge of the inflated shape, so whether the protrusion is at the front, center, or back of the robot influences the amount of drag and lift. When the legged robot (2.87 N underwater weight) needs to remain stationary in flow, an asymmetrically inflated body resists sliding by reducing lift on the body by 40% (from 0.52 N to 0.31 N) at the highest flow rate tested while only increasing drag by 5.5% (from 1.75 N to 1.85 N). When the legged robot needs to walk with flow, a large inflated body is pushed along by the flow, causing the robot to walk 16% faster than it would with an uninflated body. The body shape significantly affects the ability of the robot to walk against flow as it is able to walk against 0.09 m/s flow with the uninflated body, but is pushed backwards with a large inflated body. We demonstrate that the robot can detect changes in flow velocity with a commercial force sensor and respond by morphing into a hydrodynamically preferable shape. Continue reading

Posted in Human Robots

#436094 Agility Robotics Unveils Upgraded Digit ...

Last time we saw Agility Robotics’ Digit biped, it was picking up a box from a Ford delivery van and autonomously dropping it off on a porch, while at the same time managing to not trip over stairs, grass, or small children. As a demo, it was pretty impressive, but of course there’s an enormous gap between making a video of a robot doing a successful autonomous delivery and letting that robot out into the semi-structured world and expecting it to reliably do a good job.

Agility Robotics is aware of this, of course, and over the last six months they’ve been making substantial improvements to Digit to make it more capable and robust. A new video posted today shows what’s new with the latest version of Digit—Digit v2.

We appreciate Agility Robotics foregoing music in the video, which lets us hear exactly what Digit sounds like in operation. The most noticeable changes are in Digit’s feet, torso, and arms, and I was particularly impressed to see Digit reposition the box on the table before grasping it to make sure that it could get a good grip. Otherwise, it’s hard to tell what’s new, so we asked Agility Robotics’ CEO Damion Shelton to get us up to speed.

IEEE Spectrum: Can you summarize the differences between Digit v1 and v2? We’re particularly interested in the new feet.

Damion Shelton: The feet now include a roll degree of freedom, so that Digit can resist lateral forces without needing to side step. This allows Digit v2 to balance on one foot statically, which Digit v1 and Cassie could not do. The larger foot also dramatically decreases load per unit area, for improved performance on very soft surfaces like sand.

The perception stack includes four Intel RealSense cameras used for obstacle detection and pick/place, plus the lidar. In Digit v1, the perception systems were brought up incrementally over time for development purposes. In Digit v2, all perception systems are active from the beginning and tied to a dedicated computer. The perception system is used for a number of additional things beyond manipulation, which we’ll start to show in the next few weeks.

The torso changes are a bit more behind-the-scenes. All of the electronics in it are now fully custom, thermally managed, and environmentally sealed. We’ve also included power and ethernet to a payload bay that can fit either a NUC or Jetson module (or other customer payload).

What exactly are we seeing in the video in terms of Digit’s autonomous capabilities?

At the moment this is a demonstration of shared autonomy. Picking and placing the box is fully autonomous. Balance and footstep placement are fully autonomous, but guidance and obstacle avoidance are under local teleop. It’s no longer a radio controller as in early videos; we’re not ready to reveal our current controller design but it’s a reasonably significant upgrade. This is v2 hardware, so there’s one more full version in development prior to the 2020 launch, which will expand the autonomy envelope significantly.

“This is a demonstration of shared autonomy. Picking and placing the box is fully autonomous. Balance and footstep placement are fully autonomous, but guidance and obstacle avoidance are under local teleop. It’s no longer a radio controller as in early videos; we’re not ready to reveal our current controller design but it’s a reasonably significant upgrade”
—Damion Shelton, Agility Robotics

What are some unique features or capabilities of Digit v2 that might not be obvious from the video?

For those who’ve used Cassie robots, the power-up and power-down ergonomics are a lot more user friendly. Digit can be disassembled into carry-on luggage sized pieces (give or take) in under 5 minutes for easy transport. The battery charges in-situ using a normal laptop-style charger.

I’m curious about this “stompy” sort of gait that we see in Digit and many other bipedal robots—are there significant challenges or drawbacks to implementing a more human-like (and presumably quieter) heel-toe gait?

There are no drawbacks other than increased complexity in controls and foot design. With Digit v2, the larger surface area helps with the noise, and v2 has similar or better passive-dynamic performance as compared to Cassie or Digit v1. The foot design is brand new, and new behaviors like heel-toe are an active area of development.

How close is Digit v2 to a system that you’d be comfortable operating commercially?

We’re on track for a 2020 launch for Digit v3. Changes from v2 to v3 are mostly bug-fix in nature, with a few regulatory upgrades like full battery certification. Safety is a major concern for us, and we have launch customers that will be operating Digit in a safe environment, with a phased approach to relaxing operational constraints. Digit operates almost exclusively under force control (as with cobots more generally), but at the moment we’ll err on the side of caution during operation until we have the stats to back up safety and reliability. The legged robot industry has too much potential for us to screw it up by behaving irresponsibly.

It will be a while before Digit (or any other humanoid robot) is operating fully autonomously in crowds of people, but there are so many large market opportunities (think indoor factory/warehouse environments) to address prior to that point that we expect to mature the operational safety side of things well in advance of having saturated the more robot-tolerant markets.

[ Agility Robotics ] Continue reading

Posted in Human Robots