Lava Palaver - Constructing a flexible line follower - PiWars 2024

 Here's our first experimental line follower mount, designed to be able to handle the bump in the Lava Palaver challenge and still keep following the line. 

It's been drawn up in TinkerCAD and anyone who wants to play can copy it from here https://www.tinkercad.com/things/lRdxtf15kXL . 

It's only an experiment to mount the line follower sensors on a hinged board supported at the front by two ball bearing castors which will raise the sensor in line with the robot travelling over the bump.  A spring is fitted across the hinge to provide the means to keep the sensor in the right position as it hits the bumps and rides over the other side. A bracket is also fitted to support a microswitch fitting to detect when the robot is approaching the bump and also when it is just leaving. It's not shown in the picture above but is in the photographs. following.

This first picture shows the problem we need to get around, here the sensor is mounted on a low-cost chassis for testing. The sensor is very low and ahead of the front wheels meaning that when the robot encounters the bump in Lava Palaver it will collide with the bump and possibly damage the sensor. 

A hinged sensor support has been created for testing, the extra slots and holes are for more easily changing the layout without making a new support. Here it's mounted on the low cost chassis for testing.

This picture shows the centrally located spring to provide downforce to the sensor mount so that it will remain in contact with the course as the robot negotiates the bump.

This last photo shows the microswitch which will be activated when the sensor mount first encounters the bump so informing the robot that it may need to slow down to safely negotiate the obstacle. It also shows the damage to one of the ball bearing castors which would be unlikely to survive the Lava Palaver course in competition so realistically will be replaced with a rolling castor.

The purpose of this experiment is to investigate the viability of using this method of navigation and also provides both detection of the bump as well as steering. It will probably be necessary to mock up a 'speed bump' to tell if this is needed or not, or whether the robot can use a line following sensor mounted on the robot chassis itself with perhaps accelerometers determining if the robot is travelling over the bump. The microswitch and lever may still be retained as it provides simple and useful advanced indication of the bump. There'll be an update later as to how we get on.

Eco-Disaster PiWars 2024

 Something  to think about anyway, sorting 12 barrels by colour into two areas, autonomously, within 5 minutes. That's the PiWars 2024 Eco-Disaster Challenge detailed here Eco-Disaster – Pi Wars. 

Well we have to think about it, though we may never get to do it!!!

The Challenge

   The arena for this challenge is 2.2m square, with a central area of 1.6m square where the barrels are located, positions of which revealed on the day. 

    So, the challenge is to relocate the barrels, red for toxic, green for ok, to two areas, denoted as blue and yellow, scoring 80 points for the number of barrels relocated correctly, but loosing 40 points for incorrectly relocating barrels. There are extra points for part way completion, 50 for six barrels correctly sorted, and 150 for 12 barrels correctly sorted. Additionally, 250 points for completing the challenge autonomously, which as an 'advanced' team, we have to. As with other challenges where time matters, if all barrels are collected, additional points are awarded according to the formula system described here Formula Scoring System – Pi Wars. Thus, for completing the challenge correctly, and fastest, within 5 minutes this challenge scores 1510 points.

Approaches

   Here are three general approaches to the challenge, and the final one used may be one selected on the day depending on the layout of the barrels, or just a  mixture, but without a strategy then attachments can't be designed.

   As an alternative view, it's been pointed out that as an autonomous challenge earns 250 points, simply letting the robot stand still or spin on the spot might earn the points, as incorrect barrel placement, however accidental, will loose points.

   Assuming we're actually going to attempt the challenges, the first hurdle is identifying the colour of the barrels to be placed. An immediate option is a colour camera which has associated code to identify the colour correctly. If this is used then it's likely that this camera will also be used for navigation to the clean and contaminated zones, and preferably avoiding knocking over any of the other barrels.

      A less code intensive option is to use a colour sensor such as a TCS230, which can be tuned to identify the correct zone and barrel colours on the day. This would have to be used in association with a separate navigation system, such as an ultrasonic, laser or IR system. 

1. Individual Barrel Placement

    This requires that the robot locate each barrel, identify the colour, and then navigate across the arena to the correct location and place the barrel there. 

    At it's most basic, the robot could be fitted with a 'pusher', drive up to the barrel, push it around the arena and into its correct zone, then repeat 11 times. This does imply that the route to the zone is navigable and also that the barrel is in a position to be pushed, and hasn't been stuck up against a wall.

    The next step to this would be to have a barrel handler which will grip the barrel in some way allowing the barrel to be positioned where required. Attachment design ideas are in a later section.

   The arena design has allowed for free movement around the outside initially and a simpler strategy might be to drive the robot to one of the corners adjacent to the target zones and collect the barrels from that position where the other barrels would be unlikely to get in the way of a robot repositioning one.

2. Sweep Then Sort

   In this strategy, the robot makes two passes at barrel placement. 

   In the first pass, the robot uses a pusher, possibly of maximum width 325mm, to push all the barrels collectively  to the side of the arena containing the zones. This will immediately place some barrels in the correct zone and of course some in the incorrect zone, as well as a few outside a zone. This may appear to be a quick solution but it would probably take five passes over the arena to guarantee collecting all in this way which might reasonably take 1 minute. 

   In the second pass, the robot then begins to sort the barrels, now against  one arena wall, either moving an incorrectly placed barrel to the correct zone, or simply passing over a barrel if already correctly placed. A basic system would be to work from right to left, or left to right, checking each barrel and moving each errant barrel when found. Moving barrels at this stage could be using a similar push/grab attachment as in approach 1, or a specialised sideways grabber to allow a robot to drive back and forth along the line of barrels. A slightly more advanced approach would be to scan all the barrels for position, possibly while relocating one, and then shuffle a barrel in error from the other zone back until all barrels are correctly placed. Barrels not in a zone must be identified and moved into a zone, as well as the correct zone.

   On a points basis, if a robot autonomously ploughed all barrels into one zone, it would score 540 points, so as a default position this strategy has some advantages. From then on, every barrel moved to it's correct zone effectively gains the robot 120 points, plus the bonus if all barrels correctly positioned.

3. Collect and Deliver

   This approach requires that the robot be capable of sorting and moving all or many, barrels at once.

   The robot drives around the arena collecting barrels, and once it's collecting capacity is full, drives to the zones to deposit the collected barrels. This could take one of several routes.

1. The robot collects all, or several, of one colour and takes them to the appropriate zone.
2. The robot collects all, or several, of any colour and takes them to the appropriate zones, sorting them as they are deposited.
3. The robot collects and sorts all, or several, barrels of any colour and deposits them in the appropriate zones.

   This could obviously require an attachment not much more complicated than that used in approaches 1 and 2, or could involve something more complicated where up to 12 barrels are sorted and moved by a robot at once.

Attachment Ideas

    At it's most basic, a successful pushing attachment only has to sufficiently enclose and support a barrel at a low level to enable it to be positioned into a zone. This attachment could also include a colour identification sensor. 

Basic Pushing Attachment

    A pushing attachment has its limitations not being able to pull for which some sort of grabbing/gripping mechanism is needed. This can take the form of a passive grab (say a simple push doorway mechanism), and an active release, or an active grab and release, but given the low weight of the barrels, a passive grab may have limited use.

Upgrading the basic idea with a servo and a hinged flap...

   This now offers both push and pull from a simple grab, the servo can also be used to open the flap to give a bigger area to capture a barrel. 

Extended capture position, rounding off the edges a bit will probably help with capture.

   To handle the collection of multiple barrels and herding them to one place or another, as per approaches 2 and 3, something a little larger would be needed. No attempts here at loading a hopper or any other container, but that would definitely be a good option for anyone with design imagination and lots of time.  

   The flaps extend to accommodate and shepherd more barrels into the holder. they would fold flat at the beginning of the challenge to be extended. While they couldn't go wider than 325mm, they could extend to say 150mm forward so accommodating three rows of barrels.

With all barrels present

And this view begins to look like a server at Oktoberfest

These last two are simple horns to collect the barrels in two sections

The robot would have to plot its course to ensure it could easily collect and deposit mixed loads.

 

   We'll have to see what approach we'll take, we've built approach 2 before, but we may have a combined idea completely different. 

 

  Ideas we haven't developed further yet are 

• cranes/arms, to both carry and position barrels, 
• vacuums, especially to lift, 
• barrel sorters, to mechanically sort barrels on the arena
• barrel rollers to deliberately handle fallen barrels or even those that fall accidentally
• blowers, to position barrels remotely
• telescopic cameras, to give a better arena view
• harvester, uses a rolling cage to sweep up and sort barrels into a rotating bowl to be deposited later

   One idea we did think odd, is that the green barrels turn red when contaminated by the contents, but not by touching the red barrels, maybe they shouldn't be allowed to touch the red barrels at all!!!

Still, onward, we have a chassis and some arms so time to play.

Lava Palaver - PiWars 2024

Introduction 

We aren't actually in PiWars 2024, but just a reserve team for the advanced category, which doesn't mean we don't have to do anything! Assessing the challenges and thinking about what's involved has to be done. 

We've all been involved with a robot workshop so haven't had much time to look at these things in depth but here's a view on the first of the challenges listed, Lava Palaver. The official description of the challenge is here Lava Palava – Pi Wars

The Challenge

This is a black painted course 7 metres long and 55cm wide, with walls 7cm high and part way along is a double chicane where the robot has to turn right then left, followed by a left and then right. A white line 19mm wide is positioned along the centre of the course. Without attachments, the maximum width of a robot is 225mm, or half the width of the course.

A course like this has been used in previous years, but as a change to the layout, a 'speed bump' will be inserted onto the course on the day of the competition, dimensions shown below.

This one feature does introduce a range of considerations also applicable to the obstacle course challenge covered later. The overhang on a robot, that part of the chassis ahead of the front wheels or tracks, will need to be able to clear the leading edge of the 'bump' and also once traversed, be able to avoid colliding with the level part of the course when coming off the 'bump'. A robot could, of course, be made sufficiently robust to collide with this and continue on, either with strengthened chassis, the addition of a skid or with a leading idler roller or wheel. The table below lists a range of overhang lengths and clearances required.

The course is to be navigated autonomously and 225 points are awarded to each of three runs completed within 5 minutes and additional 100 points for each of these three runs where the robot doesn't collide with the sides of the course. The combined run times of three runs are compared to the other robots and up to 150 points can be awarded on a decreasing basis for the fastest robot times according to the PiWars formula, described here Formula Scoring System – Pi Wars. Finally, for the fastest individual run of a robot, 275 points are awarded. The maximum points awardable are therefore 1400. From a strategic viewpoint, completing the course three times without touching the sides and within the time limit gains 975 points, 70% of the maximum, so from an effort perspective is a worthwhile target in itself. Once a successful run navigation strategy is achieved then the speed could be increased to competitively gain the extra points.

Navigation strategies

There are several strategies which come to mind, and may be adopted either individually or combined. These are dead-reckoning, wall following, line following and for the second and third runs, memorised tracks. Collision avoidance, while not a navigation strategy, is desirable so will be included!

Dead-reckoning

As the shape of the course is known, separate routines could be incorporated into the robot navigation code to drive the robot  in different ways depending on the assumed position of the robot. Therefore, the routines could be for example.

Drive-forward-2.5-metres

Turn-right-45-degrees

Drive-forward-700mm

Turn-left-45-degrees

Drive-forward-1-metre

Turn-left-45 degrees

Drive forward-700mm

Turn-right-45-degrees

Drive forward-3-metres

These descriptive routines could be taken to successfully navigate the centre of a course, without reference to the 'speed bump'. This is not the shortest route of course and starting the robot already close to the right-side wall and navigating close to the left hand wall through the chicane, returning to near the right side wall on the lead out would be shorter and thus faster for a robot with similar speed capabilities. Robots with a chassis width less than the maximum would be able to take best advantage of this strategy.

Dead-reckoning has been done successfully with timed robot runtimes, but works more reliably when the wheel dimensions are combined with measured rotations of the wheel to calculate the distance accurately. Similarly, robot direction can be estimated by relative wheel rotations (combined with wheel orientation depending on steering method). A robot using mecanum wheels could simply move at a required angle without changing orientation. Gyroscope/accelerometer circuits can be incorporated to provide even more orientation information.

On a simple course such as Lava Palaver, dead-reckoning can provide an effective method of autonomous completion. The assumed measurements in the example could be confirmed on the day of competition, and corrected by physical measurement of the course. This could also include the position of the 'speed bump'  to accommodate any speed/power variations which might be needed. Including collision avoidance to improve the usefulness of the estimations input will aid the navigation.

Wall following

The course has a wall on either side, and while colliding with either wall might reduce the score, and time, available, it does offer a consistent guidance reference throughout. Detecting walls can be done with a variety of non-contact technologies, such as ultra sonics, laser and infrared (IR) distance measurements. 

Ultrasonics

Detectors are mounted on the sides and front of the robot and provide a reading how long an ultrasonic pulse of sound takes to reflect from a surface. These can be either self contained, carrying out the measurement and providing measurement information, or controlled by the robots controller and the timing and subsequent distance measurement calculated directly. These detectors can be prone to errors  due to the angle of the surface they are facing and the level of reflectivity, hard surfaces working best. 

This is a very common low cost ultrasonic sensor, in this case, run from a controller.

Laser

In recent years, small laser equipped distance sensors have become available, such as the VL6180X or VS53L0X models, which can provide an accurate and fast measurement providing that the target surface is reflective enough. The surface of the course walls are painted black and this may significantly reduce the effectiveness of this type of sensor, but trying it may be a useful lesson. They also cost a bit more than the ultrasonic sensors, which may need to be taken into consideration when adding multiple sensors to a robot.

   LIDAR (LIght Detection And Ranging) sensors are not beyond the budget of many robots (both in cost and size) and can provide a detailed map of a robots surroundings, but do need to be able to 'see' the course walls which may prove difficult to engineer a robot to do in this case.

This picture of a low cost LIDAR sensor is driven by an electric motor to give an all round view. It costs in the region of a good serial servo which many roboteers use.

InfraRed(IR)

These sensors rely on the level of reflected IR light from a surface, which is illuminated by an associated IR source. These can be very effective measuring small distances where the ultrasonic detector would fail completely but may suffer the same problems as the laser sensors when observing the black sides of the course.

This is a pair of IR sensors with adjustment for triggering sensitivity.

The following is an example sensor layout.

The rectangles describe the locations of the sensors for both wall following and collision avoidance and could be either ultrasonic, IR, or both. One option for the front collision detector is to mount it on a servo to provide a sweep of the area in front of the robot for greater coverage. 

Line Following

The white line down the middle of the course provides an immediate point of focus for guidance being consistent throughout. Line following is a very common entrance subject to robotics and using error correction to establish a reliable guidance mechanism. Information about the position of the line relative to the robot and its directions is typically obtained via an array of point sensors or from a high resolution optical camera. There are also low resolution optical cameras available which provide a much simpler interface and image to analyse for guidance.

Point Sensor Arrays

These come in various types but are typically a light sensor and light source as adjacent pairs and provide a signal based on the reflectivity of the surface which can be used to detect a white or black line on a background black or white field. 

Individual sensor, this is a TCRT5000

Here, eight sensors have been soldered to a sensor bar and an I2C interface provides access.

Some colour sensors can detect coloured lines to enable multiple lines to be used for different guidance uses in the same plane. A basic array would be two such pairs a short distance apart and mounted across the robots chassis at right-angle to the direction of travel. These give basic information such that when the right sensor is over the line, turn right, when the left sensor is over the line, turn left. Adding more sensor pairs enables the robot to more accurately determine the position of a line and placing them closer together enables a greater degree of granularity of control. Using two or more lines of sensors enables more directional information to be gathered, and varying the shape of the sensor array ( an arc can be beneficial) , together with varying the spacing of sensors to give both coarse and fine positional sensing can be helpful.

Positioning the sensor array ahead of the robot gives more time to make corrections, as well as placing arrays further back on the robot chassis to reduce over correction situations. They can also be useful with providing initial alignment at the start of a line following run ensuring that the robot is positioned as straight as it can be. 

This is a basic two sensor layout which can be very successful but the line follower typically has low speed as it constantly has to hunt for the line it's following.

Adding a third central sensor provided focus, reduces hunting and increases the speed possible. 

As with the commercial example above, this is an eight sensor bar. If the line is wide enough then the two central sensors can be the focus, but if it is a narrow line, then ignoring one of the outlier sensors and using the fourth sensor as the focus can help performance.

A nine sensor commercial sensor bar is unusual, but automatically provides for a central focus sensor. The wide sensor bar provides for an increased sensor sweep area when negotiating corners or having to perform line finding.

This is an enhanced nine sensor arrangement with a dense focus in the centre, allowing the robot to line follow using multiple sensors, perhaps not necessarily in the centre, but also has outlier sensors for improving corner performance.

This curved sensor is common n competitive line follower robots, maintaining a focus area and providing depth in the outlier sensors. This is very useful where the robot will be encountering many corners in quick succession.

This final layout is more extreme and might be more at home in a commercial robot but can still be useful in smaller robots. The central sensor bar provides the focus and the core steering input. The lead bar provides advance information to allow the controller to take predictive actions, and the trailing sensor bar provides some alignment information to help reduce crabbing and hunting of the robot as well as aiding aligning the robot in a straight line at the start.

High Resolution Cameras

      While they can require significantly great processing power in a robot controller, the cost of adding a camera can be very modest and equivalent to a point sensor array. The processing may be more intense but effectively provides the same level of  guidance as a multilevel array giving a degree of lookahead absent from single line sensors. Cameras can be mounted away from the surface of the course so can avoid being snagged on a 'speed hump'. Using cameras can give a very high quality of control but does require significant investment in learning to implement in code. A variation on this is to add a pan feature to the camera to provide additional lookahead capability.

Wide angle cameras such as this can provide a good view for line following but some compensation for the lens distortion might be needed for accuracy.

Combinations

    Combining all these may be difficult, but a few would be very useful.  Wall following can be a complete solution, but including it with the others for collision avoidance makes the extra points more likely and gives greater confidence in increasing the speed. Line following can achieve the whole navigation of the course, but adding the dead-reckoning information to it can aid in speed control, accelerating the robot from the start, slowing as a corner approaches and accelerating afterwards. Without knowing where the 'speed bump' is, a robot either must moderate its speed throughout or potentially risk crashing, however dead-reckoning can add a suitable speed reduction to safely navigate it. 

Memorising and recall

    Having completed one successful run of the course (we will all be successful!!!), we should have enabled our robot to do it again just as easily, but with the extra knowledge of having done it once. Recording the robots good run means that without sensors it should be able to do it again and perhaps faster. The distance to the corners is known, the 'speed bump' has been located and where the robot can and can't run at full speed determined. 

The methods of recording a 'good' run are varied but the course isn't complicated so a small array of control points may suffice.

What will make for a good robot for this challenge?

     The Lava Palaver is one of the challenges and going all out to win just this one thing may be the sole objective, but in PiWars, a robot chassis will need to be adaptable to the other challenges.

    Adding a line follower array attachment will be perhaps the easiest option, but some mechanism may be needed to allow it to navigate the 'speed bump', fitting it with a hinge and either a roller or idler wheel for the time it is in contact with the 'speed bump'. Fitting this hinged part with a detector would also inform the robot that it had found the bump. Line following competitions sometimes feature quite extreme 'bumps' which delicate high performance robots just get on with.  

    Placing the array well in front of the robot with this mechanism would also perhaps give the robot a small amount of time to decelerate to navigate the bump safely. However, placing the array far in front of the robot may be a problem for steering depending on the technique involved. 

A few mock-up pictures of a suspended sensor running on ball castors. The spring provides suspension to hold the sensor bar down as well as accommodate the rise and fall of the bar.

     

    A robot can be up to 300mm long in its base configuration, longer than half the width of  the course so a mecanum wheel equipped robot could find itself colliding with the sides of the course at corners. Using skid or differential steering would offer a robot the chance to steer precisely but only if  it was to slowdown to do so at corners. Ackermann steering would give the best control over the course at speed, but might prove problematical to use in the other challenges. One thing which would be consistent is that full length robots with attachments will be at a disadvantage on this challenge.

Remember: 70% of the points are available just for finishing the course without errors three times in 5 minutes, which isn't fast, so just that would be a good result for any robot entrant.

It's not certain, but I suspect East Devon Pirates will have a camera's eye view of the course with skid steering :)  There's code out there we've used before if you want a look.  uggoth/EastDevonPirates2024: Work towards Pi Wars 2024 by East Devon Pirates (github.com)

 

Didn't get in

 

Failed to get into PiWars 2024, guess there's lots of strong advanced entries, but we've been put on the reserve list which will be a bit odd. If we were a school team it would be easier as it would be a project, but creating a blog and video for a competition you probably won't be in will be a bit different.

Anyway, we may continue for a while. 

Three of us met to discuss Sidmouth Science week and inevitably we started discussing PiWars. Much fun was had experimenting with arms for sorting toxic waste barrels and a Nerf gun for shooting Zombies. Despite not getting in, we're already doing well at having solutions to the challenges and even measured out an area of kitchen for a test arena, though we have to move the table and chairs out first!

An analysis of the challenges has already been done, so they'll be written up over the next few days and published in the hope they're useful to everyone else.