Archive 23.05.2023

A robotic bee that can fly fully in all directions has been developed by Washington State University researchers.

Perception Engine, provides mobile robots with advanced perception capabilities. It delivers a revolutionary way of learning an environment and performing localization, obstacle detection, mapping, free space detection and other, higher levels of perception.

There was a shortage of entries in the tablebot competition shortly before the registration window closed for RoboGames 2023. To make sure the contest would be held, I entered a robot. Then I had to build one.

What’s a tablebot?

A tablebot lives on the table. There are three “phases” to the competition:

• Phase I: Build a robot that goes from one end of a table to the other and back.
• Phase II: Have the robot push a block off the ledge of the table.
• Phase III: Have the robot push the block into a shoebox mounted at the end of the table.

There is also an unofficial Phase IV – which is to fall off the table and survive. I did not attempt this phase.

The majority of tablebots are quite simple – a couple of sonar or IR sensors and they kind of wander around the tabletop in hopes of completing the different phases. My tablebot is decidedly different – and it paid off as the robot won the gold medal at RoboGames 2023.

Robot build

The entire robot is built of 3D printed parts and random things I had on hand.

I’ve had one of those $99 LD-06 lidars sitting around for a while, and decided this was a great project to use it on. I used a Dynamixel AX-12 servo to tilt the laser so I can find the table, the cube, or the goal.

All of the code runs on an STM32, on my custom Etherbotix board which was designed for my Maxwell robot a number of years ago. The robot uses differential drive with some 30:1 12V gear motors, which were purchased from Lynxmotion in 2008 and used in various fire fighting robots over the years.

A set of small digital Sharp IR sensors are used as cliff sensors. These can be moved up or down to calibrate for different table surfaces using a pair of adjustment screws. While the sensors are very accurate and stop the robot, they don’t see far enough ahead when going at full speed, and so I also use the laser to detect when the table edge is approaching.

Phase 1 Software

Phase 1 is pretty straight forward – and mostly based on dead reckoning odometry:

• The laser is angled downwards looking for the table. This is done by projecting to the scan to 3D points, and filtering out anything not in front of the robot at roughly table height. When the table disappears (number of points drops too low), we reduce our maximum speed to something that is safe for the cliff sensors to detect.
• While the laser sensors look for the end of the table, the robot drives forward, and a simple feedback loop keeps the robot centered on the table using odometry.
• When the cliff sensors eventually trigger, the robot stops, backs up 15 centimeters, and then turns 180 degrees – all using dead reckoning odometry.
• The maximum speed is then reset and we take off to the other end of the table with the same behavior.

Phase 2 Software

The movements of Phase 2 are basically the same as Phase 1 – we drive forward, staying centered with odometry. The speed is a bit lower than Phase 1 because the laser is also looking for the block:

• The laser scan is projected to 3D, and we filter out any points that are part of the table based on height. These remaining points are then clustered and the clusters are analyzed for size.
• If a cluster is a good candidate for the block, the robot turn towards the block (using, you guessed it, dead reckoning from odometry).
• The robot then drives towards the block using a simple control loop to keep the heading.
• Once the block is arrived at, the robot drives straight until a cliff sensor trips.
• At that point, the robot stops the wheel on the side of the tripped cliff sensor and drives the other wheel very slowly forward so that we align the front of the robot with the edge of the table – ensuring the block has been pushed off the table.

Phase 3 Software

The final phase is the most complex, but not by much. As with the earlier phases, the robot moves down the table finding the block:

• Unlike in Phase 2, the robot actually approaches a pose just behind the block.
• Once that pose has been reached, the robot tilts the laser back to level and finds the goal.
• The robot then turns towards the goal in the same way it first turned towards the block.
• The robot then approaches the goal using the same simple control loop, and in the process ends up pushing the block to the goal.

All of the software for my Tablebot is availble on GitHub.

Robogames video

Jim Dinunzio, a member of the Homebrew Robotics Club, took a video during the actual competition at Robogames so you can actually see the winning set of runs:

Visualization

To make development easier, I also wrote a Python GUI that renders the table, the robot odometry trail, the laser data, and detected goals and cubes.

Fun with math

Along the way I actually ran into a bug in the ARM CMSIS DSP library. I used the arm_sin_cos_f32() function to compute my odometry:

arm_sin_cos_f32(system_state.pose_th * 57.2958f, &sin_th, &cos_th);
system_state.pose_x += cos_th * d;
system_state.pose_y += sin_th * d;
system_state.pose_th = angle_wrap(system_state.pose_th + dth);

This function takes the angle (in degrees!) and returns the sine and cosine of the angle using a lookup table and some interesting interpolation. With the visualization of the robot path, I noticed the robot odometry would occasionally jump to the side and backwards – which made no sense.

Further investigation showed that for very small negative angles, arm_sin_cos_f32 returned huge values. I dug deeper into the code and found that there are several different versions out there:

• The version from my older STM32 library, had this particular issue at very small negative numbers. The same bug was still present in the official CMSIS-DSP on the arm account.
• The version in the current STM32 library had a fix for this spot – but that fix then broke the function for an entire quadrant!

The issue turned out to be quite simple:

• The code uses a 512 element lookup table.
• For a given angle, it has to interpolate between the previous and next entry in the table.
• If your angle fell between the 511th entry and the next (which would be the 0th entry due to wrap around) then you used a random value in the next memory slot to interpolate between (and to compute the interpolation). At one point, this resulted in sin(-1/512) returning outrageous values of like 30.

With that bug fixed, odometry worked flawlessly afterwards. As it turned out, I had this same function/bug existing in some brushless motor control code at work.

Robogames wrap up

It is awesome that RoboGames is back! This little robot won’t be making another appearance, but I am starting to work on a RoboMagellan robot for next year.

A group of computer scientists from the University of Toronto wants to make it easier to film how-to videos.

Labor shortages present new opportunities for robotic automation. AI and connected digital networks will make robots easier to use, enabling them to take on more tasks in new industries.

Increasingly, social robots are being used for support in educational contexts. But does the sound of a social robot affect how well they perform, especially when dealing with teams of humans? Teamwork is a key factor in human creativity, boosting collaboration and new ideas. Danish scientists set out to understand whether robots using a voice designed to sound charismatic would be more successful as team creativity facilitators.

The Spanish resort town of Benidorm is known for its sandy beaches with clear waters, a skyline dominated by towering hotels and tourists from northern Europe. But one day in February, it also served as a testing ground for European society’s future with drones.

Since the local economy depends on tourism during the summer, Benidorm is relatively empty in winter – and that’s a plus when it comes to safety while testing unmanned aerial vehicles (UAVs). The tall buildings that dominate the skyline also stand in nicely for those of a big city.

Sun, sea and…satellite signals

In sum, it’s an ideal place to try out new drone technology. And an EU-funded project called DELOREAN has done just that – testing new types of satellite tracking for drones on 9 February.

‘Benidorm’s skyline is quite similar to what you would find in larger cities like, say, New York,’ said Santiago Soley, the project coordinator who is also chief executive officer of Spanish aeronautics-engineering company Pildo Labs. ‘Generally, regulations limit drone flights over dense urban areas. It’s the first time in Europe we did these intense tests in a challenging city environment.’

Drones have been a hyped technology for years, during which the media popularised predictions that such aircraft would soon be used for all kinds of daily services including delivering packages to people’s doorsteps. Yet so far, widespread civilian use has failed to take off.

The bottleneck is safety and the need to demonstrate to city governments that drones can be operated in large numbers in populated areas without being a hazard. If a UAV crashes onto a busy street or into a plane that’s landing or taking off, the result could be severe damage or even deaths.

“Drone technology is getting there.”

– Santiago Soley, DELOREAN

Scientists and companies are now addressing these concerns – and the experiments in Benidorm might hold the key to the future success of drones.

‘Drone technology is getting there – it’s the least of our problems,’ said Soley. ‘What’s more important is to demonstrate how drones would safely be deployed over cities.’

DELOREAN is wrapping up after three years. The main goal was to develop navigation and positioning requirements for urban air services and show how the European Global Navigation Satellite System, or EGNSS, can help. 

Non-GPS options

Drones need to know exactly where they are at all times. For that, UAVs currently rely on satellites, mostly the US Global Positioning System, or GPS. Another alternative to GPS is Europe’s Galileo network.

DELOREAN is also testing Galileo’s potential for drones.

While led by Pildo Labs, the project has featured an international consortium whose members include France-based aircraft manufacturer Airbus, Spanish postal-servicer provider Correos and the European Organisation for the Safety of Air Navigation, or Eurocontrol, in Belgium. 

A challenge for satellite tracking in urban areas is that signals might be deflected or otherwise hindered by buildings. Galileo will help avoid such disruptions because of the waveform and structure of its signals, according to Soley.

In addition, Galileo is pioneering new services that could pinpoint drones’ locations with higher accuracy – something DELOREAN tested in Benidorm.

Furthermore, Galileo adds a layer of security. An authentication service that allows the drone to verify whether the satellite signal is real would counter any future efforts by criminal groups to misdirect UAVs and steal their contents through fake signals, according to Soley.

Airborne parcel deliveries

If experiments of the kinds conducted by DELOREAN prove successful, many applications could open up.

“Before businesses like urban air delivery can develop, we first need safety.”

– Professor Luis Moreno Lorente, LABYRINTH

While drones are already in use over cities, it is often in small-scale operations by local authorities. Police departments, for one, use them to monitor crowds or track speeding cars.

‘There are limitations on drone flights and you need to close the area,’ said Soley. ‘At the technical level, however, the flights are quite easy to handle.’

The next step could be mass urban air delivery. No more vans zigzagging through city streets with all the congestion and pollution.

Instead, fleets of drones would drop off packages across town. Companies like Amazon are already rolling out these services in limited areas.

‘Logistics will, I think, be one of the most promising uses of drones,’ said Soley.

Self-flying craft

An EU-funded project called LABYRINTH is tackling the challenge of ensuring that autonomous drones keep track of each other.

Autonomous drones require no ground-based human pilots, who are generally needed for the current generation of UAVs. 

‘In the future, those drones will be operated autonomously – they will fly themselves,’ said Luis Moreno Lorente, the project coordinator and a professor of systems engineering and automation at the University Carlos III of Madrid in Spain. ‘But if you want to do that safely, you need to know exactly where each one of them is located.’

LABYRINTH, which is due to end in May after three years, is developing software that acts as an air traffic control system for drones. The 3D position of each is tracked and the aircraft then relays this information to other drones in the vicinity so they don’t crash into each other.

Similarly, if a drone faces technical troubles – say one of its motors fails – it needs to be able to direct other UAVs away from it.

‘Before businesses like urban air delivery can develop, we first need safety,’ said Moreno Lorente. ‘That’s what we’re building now.’

Together, LABYRINTH and DELOREAN are helping to clear the way for a future in which large numbers of drones fly over cities.

‘It’s just a matter of time before they do,’ said Moreno Lorente.

Watch the video

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This article was originally published in Horizon, the EU Research and Innovation magazine.

Australian government agencies' use of Chinese-made technology has been making headlines again. This time, the potential threat comes from DJI drones produced by China-headquartered company Da Jiang Innovations.

Quadrupedal robots may be able to step directly over obstacles in their paths thanks to the efforts of a trio of Georgia Tech Ph.D. students.

Researchers at Stanford University have developed digital skin that can convert sensations such as heat and pressure to electrical signals that can be read by electrodes implanted in the human brain.

Claire chatted to Nick Hawes from the University of Oxford all about robot decision-making, long-term autonomy, and artificial intelligence.

Nick Hawes is a Professor of AI and Robotics at the University of Oxford, where he directs the Oxford Robotics Institute (ORI). Within the ORI he leads the Goal-Oriented Autonomous Long-Lived Systems (GOALS) group which researches decision-making for autonomous systems under uncertainty, including robots and human-robot teams. Recent highlights include the deployment of an autonomous mission planning stack on a quadruped at an active nuclear site, and on an autonomous underwater vehicle harvesting data from a sensor network.

This innovative solution integrates AI-powered robots with self-ordering and self-checkout devices, KDS, table trackers, and robot call buttons, enabling the robots to perform multiple roles, from order takers and food runners to table bussers.

A team of Korean engineering researchers has developed a quadrupedal robot technology that can climb up and down the steps and moves without falling over in uneven environments such as tree roots without the help of visual or tactile sensors even in disastrous situations in which visual confirmation is impeded due to darkness or thick smoke from the flames.

Machine tools are as much as 50 times stiffer than robotic arms. This lack of stiffness in robots makes it challenging to produce the desired performance. Minimizing chatter is a frequent concern and should not be overlooked to ensure the right results the first time.

Soft robotics have several key advantages over rigid counterparts, including their inherent safety features—soft materials with motions powered by inflating and deflating air chambers can safely be used in fragile environments or in proximity with humans—as well as their flexibility that enables them to fit into tight spaces. Textiles have become a choice material for constructing many types of soft robots, especially wearables, but the traditional "cut and sew" methods of manufacturing have left much to be desired.