piwars – Neave Engineering Ltd

PiWars 2024: The Challenges and Their Challenges

There are seven challenges at the in-person event for PiWars 2024, each with their own problems to solve. They are split in to three categories: Autonomous only, Autonomous/Remote, Remote Only. For the second of those, if you are in the Advanced/Professional category you have to attempt the challenge autonomously.

State of the Onion

NE-Five exists as a robot, the base hardware is all there and everything has met the minimum requirements as I’ve set them, the devil is in the detail though and integration hell is totally a thing. Also, perfect is the enemy of done and I really need to get a focus on what needs doing now vs what would be nice to have.

Motor control

Motor control has been overhauled with the switch from Red Robotics RedBoard hat to the Pimoroni Yukon. The big difference is the latter has support for encoders, but it also has on board processing which means that processing load is taken away from the overhead on the Raspberry Pi. I also have a ROS node for the Yukon so it can send and receive ROS messages such as motor velocity commands and odometry.

Servo Control

I’ve implemented a new ROS node that not only takes commands to move the robots arms but also provides joint state feedback to the wider ROS system, this also includes the neck servos as they are the same Dynamixel smart servos used in the wrists. The linear actuator that adjusts how high the robot is standing has also been hooked up to the Yukon, it has a feedback line that is factored in too. Each of the smart servos is independent of the Pi too as you give them a command and they do it, handling any PID loops and monitoring internally as needed.

Camera System

I’m still using the Luxonis FFC-3P camera system, with two wide angle global shutter cameras and a narrower field rolling shutter camera in the centre position. Luxonis have recently released a big update that includes on-device pointcloud generation. Previously I was trying to do this on the Pi and it was basically taking all the resources just to do that. Having this board run tasks itself and only providing the data the Pi needs is a big win for sure.

I’ve also been playing around with object recognition, it works and you can run custom neural networks on their which, again, means the Pi doesn’t have to do anything but use the data it produces.

Back to the Challenges…

The robot works great in remote control mode, it’s currently the only mode however which isn’t ideal. There are five challenges I’ll have to tackle autonomously so I’ll concentrate on those for now.

Lava Palava

This is a line following drag race, there is a course with a black floor and white strip down the middle which the robot has to follow as quickly as possible. The course has a chicane in it from previous years but this year will also have a speedhump.

With the motor encoders providing feedback with regards to distance travelled and the camera system able to detect objects, I intend to combine the two to have it aim for a goal that’s X meters in front of the robot and have it follow the line until it’s travelled that far. Or until I push the estop button if it tries to run away…

Eco-Disaster

In this challenge you have to sort a number of red and green barrels in to blue or yellow areas of the arena. Starting in one corner of the arena, NE-Five should be tall enough to be able to detect all the objects it needs to look out for, the start position and two sorting zones are also known locations which helps. Using a similar setup to Lava Palava I should be able to use the detected barrels to position the robot relative to them so it can pick them up. Using odometry and being able to see the coloured sorting zones, it should be able to navigate to them to drop them as needed.

Escape Route

This challenge has to be run without the robot’s operator being able to see the arena directly. For remote control this means using cameras or have someone shouting out commands, for autonomous the operator needs to be behind a screen still but only press “go” and hope for the best.

The arena will be in a randomly selected configuration out of six possible layouts. There are three coloured blocks, each having known dimensions. The plan is that as soon as the challenge begins, the robot scans to see which block is closest and add a waypoint to get passed it. After it gets there, or while en route, it can look for the next block and figure out its next steps there too.

Similar to Lava Palava, where it’s aiming for a point a certain distance ahead, the end goal will be passed the yellow line with intermediate steps to get around each block. The depth camera already has an option to convert a depth image to laser scan so should be relatively easy to detect a clear path.

Minesweeper

For this one, the robot will have to look for an illuminated red square and move to it. Once it has visited that square another will light up, the process repeats. As with Escape Route and Lava Palava I’ll be looking for the specific colour of the square but this time I’ll be setting it as the waypoint to move towards. Once it detects it’s on top of a red square, it’ll stop. Once the red switches off it’ll start looking around for anything of the same colour, with the wide angle stereo cameras this should give a good field of view for this and the odometry once again comes in to play.

Zombie Apocalypse

This one is currently the biggest unknown as I don’t have a projectile launcher for the robot yet, I do have a pile of parts however… Sample designs for the zombie targets have been released though, so I’m planning on trying to detect those to use for the coordinates. I have parts from an electrically fired Nerf gun so will mount that on a pair of servos for pan/tilt and use them to aim at the target. I also have a green laser for this, so hopefully will be able to detect when the laser is within an area in the centre of the target before firing.

If’s, But’s, And Maybe’s…

Other than for the last challenge, I pretty much have everything in place. The devil’s in the details with these things but I’m in a considerably better position than any previous competition which is a great feeling. What are the priorities though?

The Toad List

What needs doing?

Nerf gun and mounting hardware
Camera to provide coordinates of:
- A white line, it’ll have length rather than being point data so probably just “make sure white line is in the middle of the view”
- A zombie, there will be multiple at different heights, the higher ones having more points available. Primarily we’ll need the X,Y coordinates but detecting distance will help ensure were detecting the right things as they’ll all be on a plane.
- Coloured boxes, these will be used as signposts and will need to be avoided. Depth to laserscan for obstacle detection.
- Coloured barrels to pick up and navigate around, this will need pose estimation.
- Coloured flooring, for both mine sweeper and Eco Disaster
Arm control to ensure coordinates are in the same system as the camera, this is for picking up the barrels
Waypoint system, hook in to odometry to have the robot follow a path.
Robot pose estimation, where is the robot and which way is it pointing?

There is a common theme in that a lot of the challenges have overlapping needs but there’s a lot of work to do.

Load’s of time though, right?

NE-Five Mk4 – It’s All About That Base…

An update on the design of NE-Five Mk4

The road to PiWars continues! Most important of all is a solid base to build from so that’s where we’re starting.

A render of the new design for the robot base. It has a mecanum wheel at each corner, these wheels have rollers around their circumference rather than a solid rim.

This is the first iteration of the base design, it’s the same width but slightly longer to give more room inside the enclosure. Another big improvement, I hope I least, is that I’ve added suspension to each motor.

As this robot uses mecanum wheels it’s incredibly important that all four always have contact with the ground as all four wheels work together to allow the robot to move in any direction, if one isn’t in contact then the effect that wheel would have won’t be present and it’ll veer off course. I’ve added a hinge at the bottom of each mount and the black part will be printed in flexible filament. By varying the wall thickness and infill I should be able to control how much travel each wheel has. That’s the hope at least…

Another improvement is for quality of life more than anything and that’s the method by which the upper part of the base (not pictured, or designed yet…) attaches. On the previous iterations of NE-Five these parts have been attached using tabs that are simply screwed in place, this makes working on the robot tricky as if I need to work on the wiring it’s not designed for it.

I’ve also made the switch from Red Robotics RedBoard to the Pimoroni Yukon, the RedBoard has served me very well but the lack of encoder support is a problem. There’s ways around it, like using the Pi to Pico adapter that Neil developed, but the Yukon has a motor controller and encoder module all in one. It’ll also allow me to control the torso actuator and LED lights which is another issue on the Pi.

The NeoPixel library on the Pi requires you to run it as root, this makes running it as part of a ROS launch file a bit of a pain. By handing off control of this to the Yukon that problem goes away.

The other big benefit of switching to the Yokun is that I can send it messages to do something and it’ll do it rather than using CPU cycles on the Pi. Splitting hardware up between real-time and scheduled systems like this is very common and should work a treat here. The Yukon runs MicroPython too so I should be able to use ROS Serial to connect to and have it act like a ROS node, which it will be but running on the hardware.

All of this is theory at the minute and there’s always little problems I miss until at least the third iteration, stay tuned to find out what mistakes I’ve made this time! 😅

NE-Five (Re)design, Lessons Learned

One common questions I get asked about NE-Five is “is it open source” and I always give the answer “mostly…”, there’s a reason for that. The code has been available for a long while, along with a basic simulation that you can run in Gazebo, but the CAD model has never been shared. That’s becuase it’s a mess, to say the least! I hadn’t used Fusion 360 in anger until I started working on MacFeegle Prime, the v1 of NE-Five. As such, it’s an absolute mess of deadends, redundant designs, and terrible usage of the component system…

The Road to NE-Five Mk4

There have been various versions of NE-Five over the years, the biggest changes being from MacFeegle Prime to NE-Five, in the early days they had tank treads for the true Johnny Five aesthetic. Treads were a bit unreliable, at least the way I made them, so I switched to wheels for a later version rather than getting bogged down trying to make them work, all the while the rest of the robot being a mess. I think, bizarrely, that the first NE-Five was actually the Mk2, I guess I considered NE-Five the class of robot and MacFeegle Prime was just the name of the first one? Sound’s plausible, it’ll do as an explanation at least.

The Mk1 introduced an aesthetic which we’ve pretty much stuck with ever since, it also moved the motor and servo control from the head to an enclosure on his caboose. You can see the mess of cables coming out of the back of his head in the photo below. The bundle was that tight that if he tried to look up too far, his head would pop off as the servo couldn’t move the wires…

The rear of the old robot, showing how messy the wiring was. There is a circuit board on it's back, but there is a large bundle of wires coming out of the back of his head which was the big problem.

Mk2 still used a Stereo Pi board in his head, but the RedBoard was moved to the caboose and hosted on a PiZero connected over USB. This meant that there was only a USB cable and wire for the NeoPixels between head and the rest of the body which was much less stressful on the neck mechanism.

Both of these designs still used hobby servos for the arms and neck mechanisms, along with the Stereo Pi for control and vision, so the Mk3 was the biggest leap from a technology perspective.

NE-Five Mk3

Aesthetically the Mk2 and Mk3 look very similar, but there is an awful lot changed internally.

The overall form-factor has been retained but significant upgrades were made in the arms and vision system. The Stereo Pi was replaced with a Luxonis FFC-3P and the Pi Zero in the caboose replaced with a Raspberry Pi 4, the RedBoard is retained for power supply, NeoPixel, and motor control. The “hobby” servos were replaced with Robotis Dynamixels, the most obvious benefit of which is that they daisy chain together so the wiring is much tidier. More importantly though, they give position and status feedback so you can detect if they’ve stalled for example, they are more expensive but they’re worth it purely in savings compared to the number of servos I burned out without realising it!

The Luxonis FFC-3P vision board is also a game changer, where with the StereoPi I was able to generate a depthmap at 320×240 at around 18fps, the 3P can run at around 900x600px and 30 fps. This is as well as running neural networks for object recognition using it’s three cameras as well, it has dedicated hardware for the task, it also compresses images for preview on the Pi or over wifi to massively reduce bandwidth needed. This takes basically all the stress of computer vision away from the Pi and only sends over the data we need over USB, rather than swamping the Pi with frames that will mostly be discarded.

A screenshot showing three different images from the same point in time. Top left is a wide angle view of the room with two cats eating the treats I bribed them with. Top right is a depth image where close objects are red and change to blue as they get futher away. Bottom middle is a closer view of one of the cats from the narrow field camera. — An example of the output from the Luxonis FFC-3P. Top left, a view from one of the two wide-angle cameras, top right is a coloured disparity image showing the distance of various objects, and bottom middle is the view from the narrower-fov centre camera.

The 3P is configured with three cameras: Left and right are both AR0234 sensors with 110degree wide angle lenses, these also have global shutter sensors so ideal for reducing shutter effects when moving. The centre camera is an IMX378 with auto-focus, this has a 69 degree FOV so much narrower. It also has a rolling shutter so more for use to get closer views of objects when stationary. Imagery from all three can be used simultaneously, I’ve barely scratched the surface of what these can do.

NE-Five Mk4, and Future

So, with all those lessons learned and improvements in mind, I’m going to design the Mk4 from scratch in Fusion 360. The reason for this is to get rid of all the baggage from the last four years of designs in the files so that they will be much easier to maintain, and more importantly to share. There’s also some cool scripts for Fusion 360 that can generate URDF (universal robot definition files) but they only work if it’s laid out in a certain way.

Internally and externally it’ll look much like the Mk3 does now, but with what I hope to be a more “production” quality of finish. There will also be a new motor controller board I’m developing that will include reading encoders and IMU to generate odometry.

The future part is an aspiration, I’m planning on developing this in to an actual product and sell as kits or preassembled, I am going to be releasing the CAD models and BOM still though in case people want to build their own. For those in teaching or research who just want one that comes assembled and with a warranty though, buying one may be preferrable. It’s very early days on that at the minute but I’m hoping it’ll be ready for sale towards the end of 2024. I was always intending for this to be a portfolio peice for the company to help me get robotics work but that hasn’t worked out too well. A few folks have said it’s almost a product already so I figured I’d try and get it the rest of the way there.

Stay tuned for more info on this, this is the precursor to me documenting the build of the Mk4 so a lot more info will be coming soon.

Making the RedBoard Work on the Raspberry Pi 5

I’ve been using Red Robotics excellent RedBoard for years now, and while upgrading NE-Five to use a Raspberry Pi 5 I discovered breaking changes which means the available libraries wont work.

Basically, the way that GPIO works on the Raspberry Pi 5 is different as it uses its shiny new RP1 chip to wrangle its devices. As such, legacy code that used the old way needs updating. In our case that’s pigpiod, and the develop has said it’s non-trivial and will take time. This is entirely reasonable, it’s an open source project and depends on the good will and free time of the developer.

I’ve been using Approximate Engineering’s RedBoard library which abstracts a lot of the underlying bits away, so I thought I’d take a stab at updating it to use gpiozero instead. I’ve managed to get enough features working for NE-Five, the rest I will revisit when I’ve recovered a few spoons from the effort…

Long story short, you need to specify the pin factory for gpiozero to work in a virtual environment, and for whatever reason lgpio doesn’t work if you install it in a venv from pip. Instead, you need to install it from source, ignoring their instructions… This took an awful lot of trial and error to figure out, hence the spoon defecit.

If you go to my fork, there are instructions on installing everything which hopefully will get you working. Just make sure you’re on the develop branch!

Installing RedBoard+ Library on Ubuntu

Macfeegle Prime is heading back to PiWars!

A lot has happened in that time, ignoring the whole *gestures broadly at the world*, a lot has moved on in software terms. We now have Ubuntu 20.04 for the Pi, ROS Noetic has been released for it, and Approximate Engineering has rewritten the RedBoard+ library from scratch.

This library also includes support for the servo board I’m using meaning that all the servos can be controlled from the same library, it’ll be one less thing for the Teensy to do so I thought I’d start here.

Notes

Installation of the library using “pip install redboard” works a treat, it includes a GUI that lets you tweak the configuration. If you try using “redboard-gui” on Ubuntu however you’ll find the pigpio daemon isn’t included, I had to install it from source so follow the instructions here.

Next issue I hit was that I didn’t have permission to access i2c, with Raspbian you can run raspi-config to enable and disable access to various interfaces but that isn’t available on Ubuntu. I followed these steps to get it working:

First create a new file using nano:

sudo nano /lib/udev/rules.d/60-i2c-tools.rules

Then add the following lines:

KERNEL=="i2c-0" , GROUP="i2c", MODE="0660" 
KERNEL=="i2c-[1-9]*", GROUP="i2c", MODE="0666"

Reboot, then run “sudo pigpiod” followed by “redboard-gui” and you should get the following:

You’ll need to run “sudo pigpiod” on startup, to do that run “sudo crontab -e” and add the following to the end of the file:

@reboot /usr/local/bin/pigpiod

Reboot and you’re good to go!

How Not To Build A Robot

I’ve gained a lot of experience over the last few months with regards to Fusion 360, 3d printing, electronics and more besides. I thought I’d share some of those lessons.

As Complex As You Make It

The most important lesson, as with any project, is to have an idea of what you’re building from the start and how long you have to build it. If it’s a relatively simple design, there will still be a lot of issues you’ll come across that will take added time to figure out, doubly so if you’re learning as you go. My robot concept was complex to start with, more so than I expected, and I had a lot more to learn than I realised too. However long you think you need, add more and if possible simplify your design.

In retrospect, more of a plan than a quick sketch wouldn’t have gone amiss…

I had a bunch or early wins, I used existing parts from an RC car to make early proof of concepts which sped things up, and this gave me a little too much confidence. I was designing elements in Fusion 360 in isolation, assuming they’d work, and that burnt me a lot. I went through a number of different chassis designs as prototypes in the early steps and it wasn’t until I realised I needed to have more of a complete design done in CAD to see how they all fitted together that I could save an awful lot of time. I’m still not great at this but certainly getting better.

Longer term I need to learn how to do joints in Fusion 360 so that I can actually see how things fit together and what constraints there are.

A few of the prototypes, with the almost final form

I wasted a lot of time in what was designing seven different robots, I couldn’t have got to where I am without doing it though so a difficult balance to make.

Seriously, Make A List. Then Check it Again…

I had the vague idea that I’d have the Stereo Pi up top in the head for stereo vision, this would give a lot of opportunities for computer vision too. Around the chassis would be a ring of sensors, ultrasonics were what I had in mind to start with, but though simple to work with they’re quite large. I didn’t really know better so that’s that I went with. Later on I learned of the VL53L0X which is a really cheap lidar sensor and a lot smaller too. They had the quirk of having the same i2c address by default so you need to use i2c multiplexors or have them connected in such a way to reset their addresses on first boot… More complexity!

Again, we’ve all PHDs in hindsight but having a more solid plan and spending more time on research and planning in the early stages would’ve paid off in the long run.

Burnout

Look. After. Yourself.

As I mentioned earlier on I had lots of early successes which gave me an awful lot of false confidence, as soon as the easy wins came and went and the real struggle began the build got a lot more difficult, both technically and mentally. For those who know me or have been reading the blog for a while will know I suffer from Anxiety and Depression, they’re a bugger individually but when they join forces they’re truly evil. A few weeks before I applied to enter PiWars my beloved cat, Willow, passed away. To say this was hard on me is an understatement, coupled with the year tailing off, getting darker and colder, and things going from win after win to struggle after struggle, things got rough.

I tried to push through it, that was a big mistake, and I made the best decision for the project which is to take breath and start again. With a lot of support from my girlfriend, the rest of the PiWars community, friends, family, and colleagues alike I slowly got out of the funk while making slow but consistent progress. The Epic Rebuild Began.

Conclusions and Next Steps

I’ve learned a lot, come an awful long way in may regards and though I’ve still a lot to do I’m in a better place and so is the robot. The next steps are to get the controller up and running and the robot drivable again.

In the next blog post, I’ll talk about the plans for the challenges. As it stands I’ve almost one arm and only need to finish the hand, add a bunch of sensors and remote control. I have a minimum spec in sight and will at least be able to compete.