Wall-E3 Charging Station Integration

Posted 14 April 2022

Wall-E3 has a significantly different form-factor than Wall-E2, requiring modification of the lead-in rails on the charging station, as shown below.

Once the required mods were made, I uncommented ‘#define IR_HOMING_ONLY in my code to have Wall-E3 concentrate solely on homing to the charging station, and then worked my way into a set of PID values (100,0,30) that gave reasonable homing performance, as shown in the following short video:

And here’s an Excel plot showing typical homing performance.

24 April 2022 Update:

The above data and video was collected using the ‘NoPing’ version of the IR Homing algorithm, so I went back and did this again using the version that monitors the front distance, both for detecting a ‘stuck’ condition and for the ability to navigate around the charging station if a charge isn’t needed. Of course, this didn’t work right away for various reasons, but eventually I got it working and made another test run. Here’s a short slo-mo video, and the accompanying Excel chart showing homing performance.

IR Homing run in slo-mo with tracking status shown on rear panel LEDs
Steering value and front distance vs time

01 May 2022 Update:

Wall-E3 has made some significant progress in the last week, and is now capable of switching from right-wall tracking to IR Homing to the charging station, as shown in the following short video and telemetry output:

automatic switching from right-wall tracking to charging station homing

Stay tuned,

Frank

New Wall-Following Capability For Wall-E3

Posted 28 March, 2022

I’ve been working with Wall-E3, my new Teensy 3.5-powered autonomous wall-following robot. I’ve gotten left-wall and right-wall tracking working pretty well, but the transition from one wall to the next (typically right-angle) wall was pretty awkward. The robot basically ran right up to the next wall, stopped, backed up, and then made a right-angle turn to follow the next wall. So, I am trying to make that transition a bit smoother.

After trying a few different ideas, the one I settled on was to use my current very successful ‘SpinTurn()’ function to do the transition. I modified my ‘CheckForAnomalies()’ function to add a check for forward distance less than twice the desired offset distance. When this distance is detected, the robot stops and then makes a right-angle ‘spin’ turn (one sides wheels go forward, the other sides wheels go backward) in the direction away from the currently tracked wall, and then re-enters ‘Track’ mode causing it to track the next wall normally. Here’s a short video showing the process:

robot tracking left wall, then making a ‘spin’ turn to follow the next wall

Now that I have it working for left-wall tracking, it should be easy to port it to the right-wall tracking condition.

07 April 2022 Update:

Well, as usual, what I thought would be easy has turned out to be anything but. I was able to port the left wall tracking algorithm to the right side, but as I was testing the result, I noticed that Wall-E3 doesn’t really track the right (or left, for that matter) wall. After it ‘captures’ the desired wall offset and turns back to the parallel orientation, it basically goes straight ahead (same speed applied to both side’s motors). If the initial orientation is close to parallel, it looks like it is tracking, but it isn’t.

So, I tried a number of ideas to actually get it to track the desired offset, but they all resulted in poor-to-catastrophic tracking. After working the problem, I began to see that, as always, the issue is the errors associated with the VL53L0X sensor distance measurements. There are two distinct types of errors – an initial ‘calibration’ error associated with sensor-to-sensor variation, and the measurement error that occurs when the robot isn’t oriented parallel to the measured surface.

Calibration Errors:

Each individual VL53L0X sensor gets a slightly different value for the distance to the target, and sometimes ‘slightly’ can be pretty big – 2-3cm at 20cm, for instance. Up until now I had been ignoring these errors, but the time had come to do something about. So, as I always do when troubleshooting an issue, I started taking data. I ran a bunch of trials for all seven VL53L0X sensors at various distances. After gathering the data, I used Excel’s curve-fitting capability to fit a linear equation to the points, as shown below:

The linear-fit equations gave me a starting point, but they still had to be tweaked a bit to provide the best possible match between what the VL53L0X sensor reported and the actual measurement. Again I used Excel to tweak the equations to give the best match as shown below:

Left-side ‘tweaked’ correction expression
Right-side ‘tweaked’ correction expression
Rear ‘tweaked’ correction expression

The expressions shown in red are the ones used to correct the VL53L0X-measured distances to be as close as possible to the actual distances (10cm, 20cm, 30cm).

The above corrections were coded into a set of seven ‘correction’ functions for the Teensy 3.5 program that manages the two VL53L0X arrays and the single VL53L0X rear distance sensor.

Correction functions in Teensy_7VL53L0X_I2C_Slave_V4.ino

While this did, indeed, solve a lot of problems – especially with the calculations for wall offset capture initial approach angle, it still didn’t entirely address Wall-E3’s inconsistent offset tracking performance.

Orientation Angle Induced Errors:

Wall-E3 tracks walls by offset by comparing the center VL53L0X measurement to the desired offset, and adjusting the left/right motor speeds to turn the robot in the desired direction. Unfortunately, the turn also throws off the measurements as now the sensors are pointing off-perpendicular, and return a different distance than the actual robot-to-wall perpendicular distance. I tried adjusting the PID controller algorithm to control the robot’s steering angle rather than the offset distance, and then calculating a new steering angle each time – this worked, but not very well.

So, the solution (I think) is to come up with a distance correction factor for off-perpendicular orientations. Going through the trigonometry, I came up with this expression:

corrdist=measdist*cos⁡(steeringAngle) 

I programmed this into the following function:

and then ran some tests to verify that the correction algorithm was having the desired effect. Here’s the setup:

and here are some Excel plots showing the results

Distance correction for off-perpendicular angles

As can be seen from the above plot, the corrected distance (gray curve) is pretty constant for angles of -30, 0 and +30 degrees.

09 April 2022 Update:

I have been thinking about the above orientation angle induced errors issue for a couple of days. I wasn’t really happy with that correction as shown in the above Excel plot, and it occurred to me that I didn’t really have to strictly abide by the above correction expression derived from the actual geometry. What I really wanted was a correction that would be accurate at low (or zero) offset angles, but would slightly over-correct for orientation angles in the +/- 30 deg range. In this way, when the PID engine adjusts the motor speeds to correct for an offset error, the system doesn’t try to run away. In fact, for a slight overcorrection algorithm, the center distance reported by the robot might actually go down rather than up for off-perpendicular angles. This would tend to make the PID think that it was over-correcting instead of under-correcting as it does with uncorrected distance reporting.

So, I went back to my test setup, and made some more measurements of corrected vs uncorrected center distances for -30, 0, and 30 degree orientations, for varying values of ‘tweak’ values in he correction expression, as shown in the Excel plots below:

Out of the above correction values, I like the “cos(1.1*corr_ang_rad)” configuration the best. The correction doesn’t modify the center distance at all for the parallel case, and produces a very slight over-correction at the +/- 30 degree orientation cases.

I added the ‘1.1*’ correction to the ‘OrientCorr()’ function and performed another right wall tracking test in my office ‘sandbox’ as shown in the short video below:

Right wall tracking with sensor calibration and orientation correction applied

Here is the telemetry output for this run:

Looking at the video and the telemetry, the first leg starts with the normal offset capture maneuver, which ends with the robot about 44cm from the wall. Then it makes a pretty distinct correction toward the wall, overshoots the desired offset, and winds up the leg at about 22cm from the wall.

The second leg again starts with a capture maneuver to about 43cm. Then it stabilizes at about 32cm from the wall – nice.

The third leg maneuvers to about 43cm, and then again stabilizes at about 30cm.

The fourth leg was a bit anomalous, as it appeared to way overcorrect after capturing the desired 40cm offset, but I couldn’t find anything in the telemetry to explain it. It’s a mystery!

It’s clear from the above that I no longer need to correct for orientation angle induced errors during the offset maneuver, as these are now handled by my recent ‘global’ correction code. This will probably help with subsequent offset tracking, as the initial offset should be closer to the offset target at the start of the tracking phase. We’ll see…

After a number of trial runs, I finally settled (as much as anything is ‘settled’ in the Wall-E world) on PID = (400,5,40). Here’s a short video showing performance in this configuration:

And, once again, I still have to port this configuration and code back to the left-side wall tracking configuration. Here’s a short video of left-side wall tracking. Interestingly, my ‘random walk’ PID tuning technique resulted in significantly different PID values (300,0,200) vs (400,5,40) than the right side. No clue why.

At this point, I believe I have gone about as far as I can at the moment for wall tracking. WallE3 now can consistently track the walls in my office ‘sandbox’ using either the left-side or the right-side wall for reference. My plan going forward is to ‘archive’ this version (WallE3_WallTrack_V5) by copying it to a new project. The new project will have the goal of integrating charging station homing/connection into the system.

In preparation, I recently modified the charging station lead-in structure to accommodate the wider wheelbase on WallE3, as shown in the following photo:

17 April 2022 Update:

Well, I spoke too soon when I said above that wall-tracking was “settled”. I ran into a couple of significant problems; first, when the robot is already close to the proper offset, it is supposed to just turn to parallel the wall and then go into tracking mode, but on a number of occasions Wall-E3 ran out of control into the next wall. Secondly, wall tracking was anything but smooth, and I couldn’t get it to reliably track the desired offset. So, back to the drawing board (again).

The ‘close enough’ failures were being caused by a flaw in the ‘RotateToParallelOrientation() routine; as the robot approached the parallel orientation, the PID controller also started slowing the rotation speed, to the point where the robot wasn’t rotating anymore – just going straight ahead. If the actual parallel orientation wasn’t reached, the robot just kept going straight ahead forever – oops! The fix for this was to abandon the RotateToParallelOrientation() subroutine entirely, and just use WallOrientDeg() to get the current angular offset from parallel, and SpinTurn() to turn that angular amount back to parallel. RotateToParallelOrientation() is only used in two places (TrackRight/LeftWallOffset()), so the entire function can be removed as well.

The issue with offset tracking continues to bedevil me. When the robot is turned to approach the offset, the measured distances go the wrong way, so the PID tends to ‘wind up’ and drive the robot toward or away from the wall, rather than smoothly approaching the offset. I thought I had the answer to this by ‘tweaking’ the distance corrections due to off-parallel angles, but sadly, this did not help.

So, I removed the off-angle distance correction and went back to just tracking the steering angle – a value proportional to the difference between the front and rear side distance measurements. Now tracking was much more stable, but the robot traveled in a straight line slightly toward the wall. After a few trials, I realized that the robot was doing exactly what I told it to do – drive the front/back measurement error to zero, but unfortunately ‘zero’ did not equate to ‘parallel’. After scratching my head for a while, I realized that rather than using zero as the setpoint, I should use the value that causes the robot to travel parallel to the wall – which turned out to be about 0.25. Using this value I could increase the Kp value back up to 400 or so, and this resulted in very good tracking of whatever offset resulted from the ‘offset approach’ phase of the tracking algorithm. Just this step was a huge improvement in tracking performance, but it wasn’t quite ‘offset tracking’ yet as it didn’t pay any attention to the actual offset – just the difference between the front and back wall offset measurements.

Once I had this working, I was able to re-incorporate my earlier idea of biasing the actual steering value with a term that is proportional to the actual offset, i.e.

WallTrackSteerVal = glRightSteeringVal + (float)(glRightCenterCm – offsetCm) / 50.f;

This, coupled with the empirically determined steering value setpoint of 0.25 resulted in a very stable, very precise tracking performance, as shown in the short video below and the associated telemetry and Excel plots.

PID (400,0,0), SetPoint = +0.25
All four wall sections – note straight lines are due to gaps between wall sections

So now I think I finally (I’ve only been working on this for the last three years!) have a wall tracking algorithm that actually makes sense and does what it is supposed to do – track the wall at a constant offset – yay!!

After getting the right side working, I ported everything back to the left side, with some differences; for the left side approach phase, I wound up using a fudge factor of 10cm vice 5cm to get the approach to stop near the desired offset. Also, the base steering value setpoint was -0.35 instead of +0.25, and the input (WallTrackSteerVal) wound up being

  WallTrackSteerVal = glLeftSteeringVal + ((float)glLeftCenterCm - (float)offsetCm) / 25.f; 

With these settings, Wall-E3 seemed pretty comfortable navigating around my office ‘sandbox’, as shown in the following short video:

Stay tuned,

Frank

Wall-E3 Right Wall Following Trial

Posted 23 March 2022,

Earlier this month I was able to demonstrate a multi-lap left-side wall tracking run by Wall-E3 in my office ‘sandbox’. This post describes my efforts to extend this capability to right-side wall tracking.

Since I already had the left-side wall tracking algorithm “in the can”, I thought it would be a piece of cake to extend this capability to right-side tracking. Little did I know that this would turn into yet another adventure in Wonderland – but at least when I finally made it back out of the rabbit-hole, the result was a distinct improvement over the left-side algorithm I started with. Here’s the left-side code:

The above code works, in the sense that it allows Wall-E3 to successfully track the left-side wall of my ‘sandbox’. However, as I worked on porting the left-side tracking code to the right side, I kept thinking – this is awful code – surely there is a better way?

After letting this problem percolate for few days, I decided to see if I could approach the problem a little more logically. I realized there were two major conditions associated with the problem – namely is the robot’s initial position inside or outside the desired offset distance K? In addition, the robot can start out parallel to the wall, or pointed toward or away from the wall. Ignoring the ‘started out parallel’ degenerate case, this reminded me of a 3-parameter Karnaugh map configuration, so I started sketching it out in my notebook, and then later in a Word document, as shown below:

As shown above, I broke the 3-parameter into two 2-parameter Karnaugh maps, and the output is denoted by αT. After a few minutes it became obvious that the formula for αT is pretty simple – its either αR – αA1 or αR – αA2 depending on whether the robot starts out outside or inside the desired offset distance. In code, this boils down to one line, as shown at the bottom of the Karnaugh map above, using the C++ ‘?’ trinary operator, and choosing CW vs CCW is easy too, as a negative result implies CCW, and a positive one implies CW. The actual code block is shown below:

Here’s a short video of Wall-E3 navigating the office ‘sandbox’ while tracking the right-side wall.

So, it looks like Wall-E3 now has tracking ability for both left-side and right-side walls, although I still have to clean things up and port the simpler right-side code into TrackLeftWallOffset().

25 March 2022 Update:

Well, that was easy! I just got through porting the new right-side wall tracking algorithm over to TrackLeftWallOffset(), and right out of the box was able to demonstrate successful left-wall tracking in my office ‘sandbox’.

At this point I believe I’m going to consider the ‘WallE3_WallTrack_V3’ project ‘finished’ (in the sense that most, if not all, my wall tracking goals have been met with this version), and move on to V4, thereby limiting the possible damage from my next inevitable descent through the rabbit hole into wonderland.

Stay tuned,

Frank

Teensy 4.1 Replacement for Teensy 3.5?

A while back, I had some problems with damaged Teensy 3.5 main controllers on Wall-E3, my autonomous wall-following robot. I eventually traced the problem back to large voltage transients that occurred when I connected/disconnected the charging probe. These transients were conducted to the Teensy 3.5 pin used to detect the probe connection status, causing the Teensy to immediately reboot, and then eventually become unusable. I solved this problem by using a non-conductive photonic charger connect/disconnect system using the supplied charge LED on the TP5100 charger coupled to a photoresistor.

Unfortunately, I ran through most of my Teensy 3.5 stock while I was figuring this out, and now this part is unavailable from PJRC or anywhere else – maybe a victim of the world-wide chip shortage? The good news though is that the Teensys 4.1, successor to the Teensy 3.5 is available, so I purchased a few to evaluate as a replacement for the T3.5 on my robot.

The Teensy 4.1 has the same form factor and pin layout as the T3.5, which means it is a drop-in replacement in most cases. However, there are some gotchas. According to the comparison sheet on Paul Stoffregen’s PJRC site, the T4.1 is 5X faster (600MHz vs 120MHz) and has more memory. However, it doesn’t have any analog output DACs (T3.5 has 2), and more importantly, the T4.1 pins are not 5V tolerant!

To start my evaluation, I loaded a simple ‘blink’ program, and as expected it worked great. Here’s the test setup, utilizing my newly-discovered OONO breakout board.

Teensy 4.1 on the OONO breakout board

After this first test, I am convinced that the T4.1 will work nicely as a T3.5 replacement, except possibly for the 5V tolerance issue. To investigate this, I looked at my current system schematic

Wall-E3 System Schematic

Wall-E3’s main controller is directly connected to two VNH5019 motor controllers, several LEDs (the Chg Stat Display module), two INA169 current sense modules, a Pulsed Light (now owned by Garmin) LIDAR system via it’s ‘Mode’ pin, the ‘HC-05 BT Module’ (now replaced by my new 5V Reg/Wixel board) via its TX/RX pins, and the battery pack via the ‘Chg Conn’ pin (this the photonic connection discussed above). It is also connected to a MPU6050 IMU, a Teensy 3.2 running the IR charging beam detector, and another Teensy 3.5 running the 7-element VL53L0X distance sensing array, all via two different I2C ports.

The I2C ports are no problem, as they are either Teensy-to-Teensy or Teensy-to-MPU6050, which has an onboard regulator to regulate 5V down to 3.3, so the data lanes are 3.3V. The LED panels is passive (doesn’t generate any signals of its own), so that’s not a problem. The Wixel RF Transceiver inside my 5V Regulator/Wixel module runs on 3.3V, so the RX/TX lines are compatible with Teensy 4.1. The ‘Irun’, ‘Itot’ and ‘BattV’ A/D inputs are all below 3.3V at their maximum values (The Irun and Itot lines max out at about 2V (2 amps through the current sensor), while BattV maxes out at about 2.4V (8.4V max battery voltage minus 6V drop through a 6V zener diode).

The ‘Mode’ line on the LIDAR could be an issue. The original Pulsed Light LIDAR was acquired by Garmin, so the original datasheets are no longer available. The Garmin datasheet says that the MODE pin output is limited to 3.3V, but I don’t know if that is the same for the original Pulsed Light model I’m using on Wall-E3. So, I hooked up my digital O’scope to the LIDAR’s MODE pin on Wall-E3 and measured it directly. As shown in the following scope grab, the output is indeed limited to 3.3V – yay!

Pulsed Light LIDAR-Lite MODE pin pulse output. Note max amplitude (Ma) = 3.308V

So, it looks like I can drop a Teensy 4.1 into Wall-E3’s system and it should do fine. I don’t really need the speed and/or memory improvements, but I do need a replacement for the now-defunct Teensy 3.5, in case I manage to kill yet another one.

21 March 2022 Update:

I took the time today to see how (or if) the Teensy 4.1 breakout module would fit on my current Wall-E3 robot. As shown below, it’s pretty big!

Based on the above photos, I don’t think this breakout board has a future on Wall-E3. Even though the module will fit, that doesn’t take into account the fact that the wiring from the module will extend horizontally from the module, rather than vertically as it does with the plain ‘Teensy + Female header’ arrangement. It was a great idea a the time, but I guess the reality is that the breakout module will be relegated to testing,

07 September 2024 Update:

I was playing with my 4WD robot and noticed the MPU6050 IMU wasn’t responding correctly, so to start the troubleshooting process I pulled the MPU6050 off the robot and connected it to a Teensy 4.1 instead of the Teensy 3.5 I have in the robot, because I had a 4.1 available and didn’t have a 3.5. This led to a couple of interesting discoveries:

  • I discovered it is very difficult to get a Teensy 4.1 with pins to fit into my ‘small’ plugboard. No amount of finger pressure would get the pins to fit into the correct sockets. This continued until I discovered that, way back in the day when I first got a couple of 4.1’s to try, I had soldered header pins to all the pins on the 4.1, including the five pins across the breakout board – oops! A few seconds with a side-cutter to remove these pins and I was back in business.
  • The default Wire1 pins on a Teensy 4.1 are different than the ones on a 3.5/3.2, and this threw me for a bit of a loop. Eventually I figured out that Wire1.begin() gets aimed at the proper default pins based on the compile target – Teensy 4.1 vs Teensy 3.5.

After making these changes, I was able to compile and run my ‘Teensy_MPU6050_DMP6_V4.ino’ Arduino sketch and verified that my MPU6050 module was working fine.

Stay tuned,

Frank

Wall-E3 Time Required for All-Sensor Update

Corralling all sensor data updates into one place:

I have been struggling with how to manage sensor data updates for Wall-E3. When I originally started working with Wall-E, it had only three sensors – a left and right-side HC-04 ‘ping’ sensor and the front LIDAR distance sensor, so data updates weren’t a significant part of the algorithm. Since then the sensor population as ballooned past the double-digit mark, with seven VL53L0X side/rear distance sensors, the front LIDAR sensor, two high-side current sensors, and the MPU6050 IMU.

Back in August 2020 I decided to change from a ‘request only when needed’ to a TIMER interrupt-based sensor update paradigm. The idea was to update all sensor data X times/sec in an Interrupt Service Routine (ISR). This worked great, but caused other problems that eventually led me to abandon this approach. In addition to not knowing exactly when/where in the program the sensor data changed, it appeared this approach was incompatible with my use of the PID library for motion control. The PID library’s ‘Compute()’ function expects to be called in a loop that runs many times faster than the PID’s internal update period (100mSec by default). PID::Compute() returns without doing anything until its internal 100mSec timer expires, at which point it does one PID computation and then resets the timer. So, there was a conflict, because I wanted to call PID::Compute() each time the TIMER ISR executed (using a ‘global’ boolean flag), but PID::Compute() wants to execute only when it’s internal timer expires. I never could figure out how to make those two requirements work together. Eventually I abandoned both of them. First, I dumped the PID library and rolled my own PIDCalcs() function that computed a new output every time it was called, and I dumped the timer ISR in favor of ‘just in time’ sensor data updates.

Fast-forward to the present, and now I’m still struggling to figure out how to manage sensor data updates. As my latest ‘sand-box’ testing showed, I need to update all the distance sensors even when I’m only tracking one side, so just updating one side or the other doesn’t work. So, I created a ‘UpdateAllDistances()’ function as shown below:

This function also causes the front and rear distance arrays to be updated and new front/rear variances to be calculated. The function is intended to be called from both the left and right wall tracking loops, and anywhere else updated distance and related data updates are required.

Having created this function, the next question becomes – how long does this function take to execute? If it is too long, then tracking performance will suffer. To answer this question, I placed code at the beginning and end of UpdateAllDistances() to toggle a hardware pin so I can measure the elapsed time on a scope, and I placed code at the beginning/end of a small WALL_TRACK_UPDATE_INTERVAL_MSEC test loop in setup(), as follows:

With this test, I got the following output on my HANMATECK DOS1102 digital O’Scope:

200mSec tracking loop (blue) and UpdateAllEnvironmentParameters() duration (yellow)

As can bee seen in the above plot, UpdateAllEnvironmentParameters() takes around 6-10mSec to update all environmental parameters (basically everything except MPU6050 heading), leaving 190-194mSec to complete the rest of the tracking update loop. This is very good news, as it means I can basically think of UpdateAllEnvironmentParameters() as a one-line ‘do everything’ command with negligible duration.

Next I made the same measurement, but this time with the actual ‘TrackLeftWallOffset() code being executed. As can be seen from the following image, the result is essentially identical to the first test; UpdateAllEnvironmentParameters() takes around 6-10mSec.

200mSec tracking loop (blue) and UpdateAllEnvironmentParameters() duration (yellow)

Then I did this test one more time, except this time I toggled the blue trace at the beginning and end of wall track processing, to show the time remaining in the 200mSec loop. Here’s what actually happens in the tracking loop:

And here’s the screen grab from my O’scope showing the actual duration of everything in the above loop.

tracking loop processing duration (blue) and UpdateAllEnvironmentParameters() duration (yellow)

As can be seen, almost all of the tracking processing time is spent in UpdateAllEnvironmentParameters(), and there is plenty of time to do additional processing (like anomaly handling). The 200 mSec loop is denoted above by adjacent rising edges of the blue trace, and all processing is finished at the trailing edge of the blue trace, so only about 10mSec, or about 5%, of the 200mSec is taken.

So it is clear that consolidating all environmental sensor updates into one function is a big winner. The time taken for sensor data updates is a small percentage of the time available for the entire tracking loop, but it is almost all of the time required in each tracking loop. This is very interesting result. The time required for sensor update probably cannot be reduced, as it depends on the actual hardware sensor response times and the ability to get the sensor data back to the main Teensy 3.5 processor via I2C. However, it now appears that I could easily reduce the overall tracking loop duration from the nominal 200mSec to 100, 50, or even 20mSec with no adverse effects, and presumably a corresponding increase in tracking performance. This is probably the biggest win associated with the change from the Arduino MEGA2560 to the Teensy 3.5 – so much less time required for processing.

Stay tuned,

Frank

Wall-E3 Multi-Lap Wall-Following Trial

Posted 06 March 2022

I’ve pretty much finished with transitioning my autonomous wall-following robot from the old Arduino MEGA 2560 main controller to the new Teensy 3.5 main controller, and now I am moving on to actually getting Wall-E3 to do the job it was created for – namely, to follow walls autonomously. This post describes the result of a multi-lap run through my little testing ‘sandbox’, consisting of a set of barriers forming a 2m X 2m rectangular ‘room’. Here’s a short video showing the run:

I captured the telemetry output from the run and went back through it pretty much line-by-line, trying to make sure I understood all the actions displayed in the video – particularly the little off-piste excursion at about 1:00 on the second lap, just before the end of the run (the run was cancelled when a portion of the wall fell over on Wall-E3).

First Leg: 36.0- 38.9 Sec (5-8 sec in video)

In the first leg, the robot starts off parallel to the wall on the left, but too close (16cm instead of 40cm). It makes a 27.1º CW turn away from the wall to achieve a cut angle of 30º, moves forward, and then turns back 30º CCW to end up parallel to the wall, and about 36cm away – almost perfectly spaced 40cm off the wall.

Next it tracks down the wall from 14.992 sec to 17.390sec , trying (and generally succeeding) to maintain the 40cm standoff distance.

At 17.390 sec it detects an upcoming obstacle (the obstacle detection distance was set to 20cm for this run), and stops (not quite getting all the way stopped before running into the wall – oops!).

First-to-Second Leg Transition:

The handling procedure for the ‘OBSTACLE_AHEAD’ case is to stop, back straight up to achieve the nominal wall offset distance (40cm here), then make a 90º turn (CW in this case) away from the wall to orient itself parallel to the wall again.

As can be seen from the above telemetry, that is exactly what happens, backing up to the point where the front LIDAR sensor shows 38cm and then making the required 90º turn with SpinTurn(CW, 90.00, 45.00). The ‘backup and turn’ evolution is completed in approximately 1.5 sec.

Second Leg: 47.4 – 49.0 Sec (16-19 sec in video)

Second-to-Third Leg Transition:

Third Leg: 57.0 – 59.8 Sec (25-29 sec in video)

Transition and Fourth Leg: 61.0 – 70.4 Sec (30-40 sec in video)

Transition and Fifth Leg: 71.6 – 80.9 Sec (40-50 sec in video)

Fifth-to-Sixth Leg Transition: 82.1 – 88.0 Sec (50-58 sec in video)

Sixth Leg up to Anomaly 88.9 – 89.8 Sec (57-59 sec in video)

On this leg an anomaly occurred. The robot detected a ‘Stuck Ahead’ condition, defined by the condition where the mathematical variance of the last N front LIDAR distance readings falls below a set threshold. This should never happen while the robot is actually moving, but it clearly did happen in this case (unfortunately I wasn’t recording the front distance measurement or the distance measurements to the other wall, so I can’t go back and see exactly what happened). The recovery procedure for a ‘stuck ahead’ condition is to make a 90 deg turn away from the nearest wall, move forward for 1 second, and then make another 90 deg turn to return to a parallel course, but offset 10-20 cm from the previous track. In this case, the robot turned toward the nearest wall, clearly a mistake (it was a mistake in TrackLeftWallOffset() – since fixed). However, it was not a disaster, as the robot made the second turn before hitting the wall, and from there on it returned to normal left wall tracking mode.

Summary:

This first test of left wall tracking performance was very encouraging. The robot was able to continue tracking operations over several laps, including an instance where it recovered from an inadvertent ‘Stuck’ detection. The test could easily have continued until the batteries died, but had to be aborted when one section of the foam-core wall fell in on top of the robot – oops!

Also, this run pointed out the need for more focused telemetry. For this test I was only reporting the left-side and rear distances, but now I know I need to add the right-side measurements as well as the front and rear variance numbers.

09 March 2023 Update:

After cleaning up some messy initialization code, and improving telemetry readouts, I ran another complete left-side wall-tracking lap in my sandbox, as shown in the following short video:

09 March 22 Left-side wall-tracking lap

The telemetry for this run is shown below:

From the telemetry, it takes about 8 sec for all sensor hardware initialization. After that the left/right/front/rear distance arrays are initialized, and the initial front/rear variances are calculated. All this is summarized on line 26-27.

First leg: 12.866 – 15sec (3-5 sec in video)

Left-side tracking starts on line 34. First (line 37) the robot turns to its initial offset capture heading, moves to the desired offset distance (not shown) and then turns back to parallel the wall (line 39). Actual tracking starts on line 43 at 12.866 sec elapsed time (this corresponds to about 3 sec into the video)

At line 65 (about 15 sec elapsed time) the robot ‘sees’ the upcoming wall, stops and then backs up to 38cm (16.4 – 17.3 sec, 5-6 sec in video). At line 78 it makes a 90deg CW turn to line up with the current wall section, and then navigates down the wall (18.6-21.3sec, 8-11sec in video)

Second leg: 18.6 – 21.3sec (8-11 sec in video)

The robot ‘sees’ the upcoming wall on line 119 (21.010 sec, 11sec in video), backs up (lines 123-129, 11-12 sec in video) and makes another 90deg CW turn to follow the 3rd wall

The third and 4th legs are very similar to the first two, with the robot ending back where it started at 32.574 sec (23 sec on video), ‘seeing’ the upcoming wall at line 221. At this point I transmitted the ‘C’ character over the wireless link to enter manual control to terminate the run.

Summary:

This 4-leg run was pretty much perfect. I adjusted the MIN_FRONT_OBSTACLE_DIST_CM from 20 to 30cm, and this stopped the robot from banging its head against the walls – yay! Also, the telemetry readout changes made for a much more understandable output. I was happy to see that the front variance stayed well above 10,000 the whole time, but unhappy to see that the rear variance was essentially zero the entire time. The low rear variance is due to the fact that the rear VL53L0X sensor range is only about 100cm, and after that it always reports ‘819’. This is not a real problem – it just means that I can’t use the rear variance number to detect a ‘rear stuck’ condition unless it happens within a meter or so from a wall. Hmm, maybe I could use the information from both the front & rear variance numbers to create a more robust detection system.

Stay tuned,

Frank

Wall-E3 Replacing Mega 2560 With Teensy 3.5 Part VIII

Posted 19 February 2022,

At this point in the evolution of Wall-E3, all the hardware seems to be working, so it’s time to get serious about wall tracking. Last fall I made another run at wall tracking with Wall-E2, and wound up with an algorithm that would first capture the desired wall offset, and then track it ‘forever’. This worked great, but the approach is at odds with the general processing architecture. The current tracking architecture is set up as a loop, where all pertinent parameters are updated every loop period, and the appropriate action is taken. In the case of wall tracking, the ‘action’ was one left/right motor speed update. This allows rapid recognition of, and adaptation to, various ‘error’ conditions, like being stuck or about to run into something. The algorithm developed last fall does none of this, so it can’t react properly (or at all, for that matter) to things like an upcoming wall.

I’m starting to think I can use a hybrid approach – use the current capture/tracking algorithm pretty much as it stands from last fall, but have it check for ‘error’ conditions each time through its own internal loop. If any unusual conditions are detected, then force an exit from the tracking routine and another pass through the main ‘traffic director’ function ‘GetOpMode()’. The updated mode assignment will then percolate back down through loop() and cause the appropriate handling function to be called.

22 February 2022 Update:

As a start, I ported the ‘TrackLeft/RightWallOffset() functions to Wall-E3 and, after the normal number of screwups and mistakes, I got the left side tracking algorithm working, as shown in the Excel plot and short movie clip below:

Here’s the complete code for ‘TrackLeftWallOffset()’:

The above algorithm works great, but it runs in an infinite loop once it has captured the wall offset. Based on my above comments about a hybrid approach, I could add tests for stuck and/or front or back obstacles to this loop (instead of the current ‘while(true)’). This would cause TrackLeftWallOffset() to exit, and the GetOpMode() function could assign the appropriate mode, which would then cause the proper function to execute.

Or, I could eliminate GetOpMode() entirely and put it’s logic in ‘loop()’? Actually, looking at the loop() function in FourWD_WallE2_V12.ino, my last iteration with the Arduino Mega2560, I see that GetOpMode() is called at the start of loop(), and then the OpMode switch statement comes pretty much immediately afterwards. Here’s the code:

The MODE_CHARGING and MODE_HOMING cases are self-contained, so no changes would be needed for them. The MODE_WALLFOLLOW case is sub-divided into TRACKING_LEFT and TRACKING_RIGHT cases. If all the inline code in TRACKING_LEFT was replaced with TrackLeftWallOffset() and that of TRACKING_RIGHT with TrackRightWallOffset(), with these two functions augmented by the current if (bIsStuck) , if(bObstacleAhead) and if(bObstacleBehind) guard code (pretty much as it now stands), then that should work. I think I’ll give that whirl and see what happens.

To start the process, I created yet another project – WallE3_WallTrack_V3 (to preserve the currently ‘working OK on left side’ status of WallE3_WallTrack_V2) and try porting the GetOpMode() and loop() code from FourWD_WallE2_V12.

02 March 2022 Update:

I now have a ‘loop() only’ version of WallE3 running that properly tracks the left side. Everything is basically the same as before, except the loop() function, shown below:

The ‘IR HOMING’ and ‘CHARGING’ blocks are essentially unchanged, and the ‘WALL TRACKING’ block is much simpler. All ‘anomaly’ (robot stuck either forward or backward, robot approaching an obstacle ahead or behind, dead battery, etc are all handled internally to the two ‘TrackLeft/RightWallOffset()’ functions. Here’s the (potentially infinite) ‘while()’ loop:

As the code above shows, the while loop will continue to execute as long as the ‘errcode’ value is ‘NO_ANOMALIES’. Internally the ‘CheckForErrorCondx()’ function surveys the inputs from all sensors and attempts to detect any anomalous behavior. Any return value except NO_ANOMALIES causes the while() loop to exit. Each potential anomaly condition has its own handling function, which exits back to ‘loop()’ and the process starts all over again. I believe this is a much cleaner approach than I had before with the ‘GetOpMode()’ function.

I also took the opportunity at this point to fix a long-standing problem with the code. The front-facing LIDAR unit returns distances in Cm, while all seven VL53L0X time-of-flight distance sensors report in mm. Not only did this torture me mentally (let’s see – is it Cm or mm here?), but it caused the new ‘CalcRearVariance()’ function to crater, because the 10x larger numbers, when squared, caused the ‘uint16_t’ type to overrun and produce crazy variance numbers. So, I changed the GetRequestedVL53L0XValues() function to convert mm to Cm, changed all the variable names from xxxxMM to xxxxCm and carefully combed through the entire codebase, correcting the inevitable wash of ’10x’ errors. In the end though, I made the codebase much more consistent and understandable (I hope).

Stay Tuned,

Frank

Wall-E3 Replacing Mega 2560 With Teensy 3.5 Part VII

Posted 02 February 2022,

After struggling through all the I2C craziness with the Wire library vs i2c_t3, and internal pullups vs no internal pullups, I think I’m back in business with a running Wall-E3 system

Wall-E3 with second deck installed and (mostly) tested

So far, I have been able to verify that the main processor can talk to the second deck VL53L0X array, the IR Homing subsystem, and that the red laser still works. Now I need to go through the rest of the robot’s subsystems:

  • Irun & Itot current sensors << CHECK
  • HC-05 Bluetooth link
  • Front LIDAR distance sensing << CHECK
  • Speaker << CHECK
  • Charging subsystem << CHECK
  • MPU6050 IMU response << CHECK
  • Charge/Turn Status LEDs << CHECK

I also still need to do something about porting the ‘tail-light’ assembly from Wall-E2 to Wall-E3. On Wall-E2, this assembly was part LED holder and part terminal strip mount – but the terminal strip portion is no longer needed. What to do? What I did was simply mount just the top half of the assembly at the rear center of the robot, and connect up the pins to the T3.5. Neat, clean, sweet!

Tail Light assembly and power strip cover on old WALL-E2
Top part of LED assembly mounted rear center on WALL-E3

13 February 2022 Update:

When I looked at the HC-05 Bluetooth module again I decided to see if I can replace it with a Pololu Wixel module, due to the problems I have been having with the 1-3 second delay associated with the HC-05 Bluetooth link. On several occasions I was trying to stop my robot remotely, only to have the delay that seems to be inherent in the BT link allow my robot to crash into (or dive over) something that it shouldn’t have. I have been using Wixels for years with my robot project, and find them quite reliable, and getting a single command through the link and into the robot seems to be instantaneous.

I started by simply switching the Teensy 3.5’s Rx0 & Tx0 jumpers from the HC-05 to a Wixel module, without doing anything else. I already had a companion Wixel connected to my PC via USB, so getting telemetry out of (and commands into) the robot was pretty painless.

Pololu Wixel wired into Teensy 3.5 Rx0 & Tx0 pins

The next step in the process was to figure out how to actually replace the HC-05 module with a Wixel. I figured I could just hack the current perfboard module that houses the 5V regulator and the HC-05, but I decided maybe I could make thinks a bit nicer by combining the Wixel module (mounted vertically like the HC-05), the Adafruit 1NA169 high-side current sensor, and the voltage regulator all into one PCB. IBased on my previous experience with the VL53L0X array PCB, I thought I could do this fairly easily (OK, so I was smoking dope – what can I say).

I did manage to work out a method for plugging the Wixel perfboard into the regulator board, without having to change its original wiring at all. I did have to add an ‘outboard’ 2-pin female header for Wixel Tx & Rx lines, as the two header pins I used for the HC-05 configuration were obstructed by the Wixel/perfboard module.

Wixel/perfboard module plugged into HC-05 socket

Anyway I did manage to get a PCB designed and sent off to JCLPCB without too much agony, and I gained some more skill working with DipTrace as well. I hadn’t done any real pattern and/or component editing before, and this project forced me to learn how to do that – neat!

Here’s the schematic for the combined circuits:

Schematic combining Adafruit 1NA169 current sensor, Wixel and 5V regulator

I decided to use the 1NA169 ‘as is’, and simply use a 5-pin header to connect the module to the PCB. This required that I construct a custom component for the module, along with a custom ‘attached pattern’, as shown:

Custom module and ‘attached pattern’ for Adafruit 1NA169

Then I used DipTrace’s very nice and intuitive PCB layout software modules to produce a reasonable layout, as shown:

PCB layout for the combined module

Then I literally copied the playbook from my VL53L0X Array PCB project and ordered 5 PCBs from JLCPCB. Within an hour of finalizing the above layout in DipTrace, I had uploaded the Gerber files to JLCPCB and ordered 5 boards (the default order number). Total cost for 5 boards, including shipping? – – – –

Put that in your pipe and smoke it! (oops – the PC SWAT squad will be after me now!)

09 March 2022 Update:

When I received the finished PCBs, I discovered that my little perfpoard Wixel module didn’t fit onto the PCB – I hadn’t taken into account the ‘overhang’ of the Wixel on the component side of the module, and the fact that the actual Wixel module sports a 5-pin header instead of a 4-pin one. So, back to the drawing board (literally) where I produced another PCB design and sent it off to JLCPCB (at $10/shot for 5 PCB’s, I can afford a lot of mistakes!). Here’s the new design schematic and PCB

Wixel and 5V Regulator Module. Note added ‘Power ON’ LED
Revised PCB design

11 March 2022:

So I got my new PCBs in, and had a chance today to check it against the schematic (yep – works), populate one PCB, and check it electrically (yep – works). Here’s the finished module:

populated and checked PCB. Note ‘Wixel’ installed on far side of perfboard.

The next step is to install the new module to replace the stand-along INA169 module and the 5V Regulator/Wixel module. Here’s a comparison shot:

New PCB on the left, stand-alone 1NA169 module and 5V Reg/Wixel on the right

I printed up a nice PLA mount for the new PCB (I forgot to design mounting holes into the PCB, so I had to add those manually), and installed it onto the robot, as shown below:

Much cleaner layout after combining current sensor, Wixel module, and 5V reg onto one PCB

02 April 2022 Update:

After using the Rev2 regulator/Wixel board for a while, I decided I needed to turn the vertical Wixel board around, so I could see the status LEDs when the board is mounted on the robot. So, in just a few minutes with DipTrace, I was able to spin the mounting configuration for the Wixel 180 degrees, as shown the comparison photos below:

Revised PCB design
Flipped the vertical Wixel, and added mounting holes
Latest PCB design mounted on Wall-E3

Stay Tuned,

Frank

Wall-E3 Replacing Mega 2560 With Teensy 3.5 Part VI

Posted 30 January 2022

This ‘Replacing Mega 2560 with Teensy 3.5’ series of posts deals with upgrading my autonomous wall-following robot with a new form factor for easier turning, and a Teensy 3.5 for the main processor instead of the older/slower Arduino Mega 2560. In my previous post on this subject I described my failed effort to interface the new Teensy 3.5 main processor with the existing second deck hardware, specifically the Teensy 3.5 processor that manages the seven-element array of VL53L0X ‘time-of-flight’ distance sensors. The failure was due to the lack of pullup resistors on the I2C SCL/SDA lines between the main processor and the Teensy 3.5 VL53L0X array manager. I2C pullups were not required when I was using the i2c_t3 library, but apparently are when using the Teensy variant of the Wire library

I have spent the week between that last post and this one trying to determine why the Wire library requires external pullups and the i2c_t3 library doesn’t, and how I can work around that requirement so that I don’t have to find a way of installing the external pullups. Although it wouldn’t be a disaster to use external pullups it would be a major PITA, because there is no convenient place to put them; the connection from the main T3.5 processor to the VL53L0X T3.5 processor is made with direct male-male pin jumpers. To install external jumpers I’d have to figure out an intermediate landing spot on either the main deck or the second deck, and add an auxiliary board with +3.3V (although +5V would probably be OK as T3.5 GPIO pins are all 5V tolerant), ground, and the 4 connections for the two sets of I2C lines, all to add two measly resistors to the circuit. And to add insult to injury, I would have to do all this while knowing for a fact that the resistors aren’t actually necessary – grrr!

Being a stubborn type, I spent the week trying to figure out how to enable the internal (~33KΩ) pullups on the I2C lines without screwing up the normal I2C activity on the pins, and I think I finally succeeded just this morning (see this post for all the gory details).

So now that I have eliminated the need for external pullup resistors (HAH!!) I can continue my effort to port the second deck functionality to Wall-E3. The following photo shows where I left off with the I2C pullup resistor investigation:

simulated main processor T3.5 on plugboard, with I2C connection to VL53L0X processor – and NO PULLUPS!

As can be seen in the above photo, I could successfully communicate between the two T3.5’s using I2C without external pullups with simple I2C demo example programs running on both processors.

Now the challenge is to install the main processing code on the plugboard T3.5 and the VL53L0X management code on the second deck T3.5 and verify that the main program can ‘see’ distance measurements from the seven VL53L0X modules tied to the second deck T3.5.

The first thing I did was to load up the unmodified sketches for both the VL53L0X array manager T3.5 and the main processor T3.5. When I started them up, the main processor sketch hung up at the VL53L0X array processor connection check:

Then I added in the physical 2.2KΩ pullups on the plugboard, and the system immediately came to life, displaying VL53L0X distances on the main processor telemetry screen – yay! This told me two things immediately; the first was, all I had to do way back when I first transitioned from the i2c_t3 library to the Wire library (but NO, I was too damned stubborn! . The second was, it is pretty much a dead certainty that adding the internal pullup activation code to both the main processor and VL53L0X array processor sketches will allow comms without pullups.

So, I modified both the sketches to do just that. The VL53L0X processor only requires internal pullup activation on Wire0 – the I2C buss connected to the main processor, as the VL53L0X breakout boards contain 10KΩ pullups (I did it for all three I2C busses anyway). Here’s the modified section:

Note in the above that the Wire2 code uses PORT_PCR_MUX(5) rather than PORT_PCR_MUX(2), as the I2C function is found on ALT5 for pins 3 & 4. Kurt E’s wonderful spreadsheet is a great reference for this sort of thing – any serious Teensy user should have this document in their reference library.

After making these changes, the I2C comms between the main processor and the VL53L0X array processor worked perfectly – job done!

The Wire1 I2C bus goes to the T3.5 VL53L0X array processor as discussed above. I enabled internal pullups on this bus as follows:

And then I added code to the Wire I2C bus going to the T3.2 IR Homing processor as follows:

To verify proper operation with the actual Wall-E3 robot Teensy 3.5 main processor, I loaded the main operating system on the main processor, and connected the second deck hardware via the multi-pin second deck connector. I already had the second deck firmware installed on the Teensy 3.5 VL53L0X array manager, and as soon as the power came up after loading the main processor firmware, I was seeing valid distances from all seven VL53L0X modules via the main processor’s ‘distances only’ debugging configuration. This validates I2C communications between two Teensy modules without the need for external pullup resistors by using the CORE_PIN37_CONFIG = PORT_PCR_PE | PORT_PCR_PS | PORT_PCR_MUX(2) – style commands to set each relevant I2C pin to enable the internal pullup feature

Hopefully this all spells the end of the I2C library compatibility goat-rope, and now I can get back to actual robot work! 😉

Stay tuned,

Frank

Wall-E3 Replacing Mega 2560 With Teensy 3.5 Part V

Posted 15 January 2022,

In my last post on this subject I described my effort to get IR Homing functional on my new Wall-E3 robot. This post is intended to document the process of getting the ‘second deck’ from Wall-E2 ported over to Wall-E3. The second deck from Wall-E2 houses the forward-looking PulsedLight (acquired by Garmin in 2015) LIDAR system, the two side-looking VL53L0X arrays, and a rear-looking VL53L0X, as shown in the photo below

Wall-E2’s ‘second deck’ module

The second deck connects to the main system via the 16-pin Amp connector visible in the bottom left-hand corner of the photo above.

Looking at the code, the only pin assignments associated with ‘second deck’ functionality are:

  • const int RED_LASER_DIODE_PIN = 5;//Laser pointer
  • const int LIDAR_MODE_PIN = 2; //LIDAR MODE pin (continuous mode)
  • const int VL53L0X_TEENSY_RESET_PIN = 4; //pulled low for 1 mSec in Setup()

To start the process, I ported the following sections from From FourWD_WAllE2_V12.ino::setup() to T35_WallE3::setup():

  • The code to reset the VL53L0X Teensy on the second deck
  • The code to initialize the above output pins.
  • The entire #pragma region VL53L0X_TEENSY section
  • The entire #ifdef DISTANCES_ONLY section
  • pragma region L/R/FRONT DISTANCE ARRAYS

That should take care of all new declarations and initializations associated with the second deck. And wonder of wonders, the T35_WallE3_V5 project compiled without errors! – time to quit for the night! 🙂

16 January 2022:

Well, of course when I connected up the second deck – nothing worked, so back to basic troubleshooting. First, I connected a USB cable directly to the T3.5 running the VL53L0X array, and determined that I can, in fact, see valid distance values from all seven sensors – yay! Now to determine why I can’t see them from the main processor.

Next, I loaded a basic I2C scanner program onto the main processor to see if the main processor could see the VL53L0X process on Wire1. The I2C scanner reported it could find the MPU6050 IMU module and the Teensy 3.2 IR homing beacon detection processor, but nothing else. After a few more seconds (and yet another face palm!) I realized that the I2C scanner program wasn’t finding the VL53L0X processor because it was only checking the Wire bus, not Wire1 or Wire2 – oops!

So, I modified the basic scanner so it would optionally check Wire1 & Wire2 in addition to Wire1, as shown below:

And here’s the output with all three I2C busses enabled.

So now that I know that the main processor can ‘see’ the VL53L0X Teensy on Wire1, I have to figure out why it’s not working properly. As usual, this turned out to be pretty simple once I knew what I was looking for. The entire solution was to change this line:

To this line:

Wall-E2 used a Mega 2560 processor with only one I2C bus, so everything had to be on that one bus. However, when I changed to the Teensy 3.5, I had more busses available, so I chose to move the VL53L0X array manager to Wire1, but forgot to change the initialization code to use Wire1 vs Wire – oops!

18 January 2022 Update:

Or,….. Maybe not. When I tried to compile the above changes I started running into ‘#include file hell’. I couldn’t figure out whether to use #include <i2c_t3.h> or #include <Wire.h> and every time I changed the include in one file, it seemed to conflict with an earlier change in another – argggghhhhh!

So, I took my troubles to the Teensy forum and asked what the difference was between the ‘i2c_t3.h’ and ‘Wire’ libraries, particularly with respect to multiple I2C bus support. The answer I got from ‘defragster’ (a very experienced Teensy forum contributor) was:

Wire.h is the base i2c supplied and supported by PJRC. It covers all WIRE#’s on various Teensy models: See {local install}\hardware\teensy\avr\libraries\Wire\WireKinetis.h

i2c_t3.h is an alternative library that can be used to instead of WIRE.h when it works or offers some added feature or alternate method.

I took his post to say “use Wire.h” unless you have some specific reason why the i2c_t3.h library offers a needed feature that Wire.h doesn’t offer.

So, now I have to go back through all the code that I have dicked with over the years to make work (like ‘I2C_Anything’, for instance) with multiple I2C buses, and see what needs to be done to, as much as possible, use ‘Wire.h’ in lieu of ‘i2c_t3.h’

The main file for this project has the following #include’s that may require work:

MPU6050_6Axis_MotionApps_V6_12.h: Right away I run into problems; the first thing this file does is #include “I2Cdev.h”, which has the following code:

Which I modified sometime in the distant past to use #include <i2c_t3.h> instead of #include <Wire.h>. At the time I think I was convinced that I had to use i2c_t3.h to get access to multiple I2C buses, but now I know that isn’t necessary. Fortunately this is a ‘local’ file, so changing this back shouldn’t (fingers crossed!) break other programs.

Aside: I did a search on MPU6050_6Axis_MotionApps_V6_12.h and I2Cdev.h in the Arduino folder, and got 48 different hits for MPU6050_6Axis_MotionApps_V6_12.h and 108 hits for ‘I2Cdev.h – ouch!! Not going to worry about this now, but I’m sure I’ll be dealing with this problem forever – if not longer 🙁

Aside2: The ‘I2Cdev.h’ file gets specialized to different hardware in I2CDevLib. For ‘Arduino’ hardware it is this file:

Where it can be seen that sometime in the distant past, I modified the original library file to select ‘I2CDEV_TEENSY_3X_WIRE’, which has the effect of #include <i2c_t3.h> instead of #include <Wire.h> – oops! So, any project that uses the library version of this file will always #include <i2c_t3.h> instead of #include <Wire.h> – double, triple, and quadruple oops! In addition, I discovered that the version of i2cdev.h I am using for this project is at least 6 years out of date – wonderful (at least it looks like the MPU6050_6Axis_MotionApps_V6_12.h is reasonably current (in fact, it is essentially identical to the library version).

To start with, I made sure T35_WallE3_V5 compiles for T3.5 ‘as is’, and then modified the includes to use the library version of MPU6050_6Axis_MotionApps_V6_12.h. This actually worked (yay!), although I discovered I had to use the “file.h” format rather than <file.h> – don’t know why.

23 January 2022 Update:

As usual, there were a few detours on the way to full ‘second-deck’ functionality. To start with, I discovered/realized that I was using the wrong I2C library (i2c_t3.h) for working with multiple I2C busses. The i2c_t3.h library does a lot of nice things, including multiple I2C bus support, but unfortunately isn’t compatible with a lot of the hardware driver libraries I use, most of which assume you are using the ‘Wire.h’ library. I had ‘sort-of’ solved this problem with many of my Teensy projects by hacking the needed hardware driver libraries to use i2c_t3.h instead of Wire.h, but this got old pretty quickly, and even older when I started trying to use multiple I2C bus enabled hardware driver libraries (like Kurt E’s wonderful multiple bus MPU6050 driver).

So, I wound up spending way too many hours tracking down the differences and similarities between i2c_t3.h and the Teensy version of Wire.h See this post for all the gory details. Along the way I learned how to simplify access to deeply buried library files using the Windows 10 flavor of symlinks, which was kinda cool, but I could probably have spent the time more wisely elsewhere. Also, I ran headlong into yet another multiple bus problem with one of my favorite libraries – Nick Gammon’s wonderful ‘I2C_Anything’ library that does just two things – it reads/rights ‘anything’ – ints, floats, unsigned long ints, doubles, whatever – across an I2C connection – no more worrying about how to do that, or being forced to transmit ASCII across the bus and construct/deconstruct as necessary on both ends. Unfortunately, this library is unabashedly single-bus – it expects to be used in a Wire.h environment and that is that. The good news is, by the time I was done the question of ‘why use i2c_t3.h as opposed to Wire.h?’, I was able to intelligently (I hope) modify I2C_Anything.h to accommodate multiple I2C busses (though it still assuming a ‘Wire.h’ environment). At the end, I made a pull request to the I2C_Anything github repo, so maybe others can use this as well.

After all this was done, I still hadn’t even started on getting the second-deck VL53L0X sensors talking to the main Teensy 3.5 processor, but at least now I (kinda) knew what I was doing.

Anyhoo, once I got back to trying to get connected to the VL53L0X array, I was able to reasonably quickly revise both the main Teensy 3.5 processor program and the VL53L0X array management program to use Wire.h vs i2c_t3.h, and to instantiate/initialize the multiple busses required for both processors (the main processor talks to the VL53L0X array manager via Wire1 on its end to Wire0 on the array end, and the array manager talks to the seven VL53L0X modules via its Wire1 & Wire2 busses). And with my newly modified I2C_Anything library, I was ready to get them talking to each other.

Here’s the working Array manager code (pardon the extra comment lines):

And here’s the working main processor code. Note that the code as it is presented here has the ‘DISTANCES_ONLY’ define enabled, and all non-existent hardware modules ‘#defined’ out so I could concentrate on just the VL53L0X array connection.

And here is the ‘multiple I2C bus’ version of I2C_Anything.h, just in case you come across this post before Nick updates the Github repo (assuming he likes what I have done):

Here’s the the test setup:

Main Teensy 3.5 processor on plugboard, connected via I2C to the Teensy 3.5 VL53L0X array manager