10 rules to code like NASA (applied to interpreted languages)

Foreword — Dear beginner, dear not-so-beginner, dear reader. This article is a lot to take in. You’ll need perspective for it to make sense. Once in a while, take a step back and re-think about all the concepts explained here. They helped me a lot over the years, and I hope that they will help you too. This article is my interpretation of them for the work I do, which is mostly web-related development.

NASA’s JPL, which is responsible for some of the most awesomest science out there, is quite famous for its Power of 10 rules (see original paper). Indeed, if you are going to send a robot on Mars with a 40 minutes ping and no physical access to it then you pretty damn well should make sure that your code doesn’t have bugs.
These rules were made with embedded software in mind but why wouldn’t everybody be able to benefit from this? Could we apply them to other languages like JavaScript and Python — and thus make web applications more stable?
That’s a question I have been considering for years and here is my interpretation of the 10 rules applied to interpreted languages and web development.

1. Avoid complex flow constructs, such as goto and recursion.

Original rule — Restrict all code to very simple control flow constructs – do not use goto statements, setjmp or longjmp constructs, and direct or indirect recursion.

When you use weird constructs then your code becomes difficult to analyze and to predict. The generations that came out after goto was considered harmful did indeed avoid using it. We’re at the stage where we’re debating if continue is goto and thus should be banned.
My take on this is that continue in a loop is exactly the same as return in a forEach() (especially now that JS has block scoping) so if you’re saying that continue is goto then you’re basically closing your eyes on the issue. But that’s a JS-specific implementation detail.
As a general rule you should avoid everything that is mind-bending or hard to spot because if your brain power is spent understanding the quirks of jumping around then you’re not spending it on the actual logic and then you might be hiding some bugs without your knowledge.
I’ll let you be the judge of what you put in that category but I would definitely put:

goto itself of course
PHP’s continue and break used in conjunction with numbers, which is just pure insanity

switch constructs, because they usually require a break to close the block and I guarantee you that there will be bugs. A series of if/else if will do the same job in a non-confusing manner

Besides this, avoid of course recursions, for several reasons:

As they build on the call stack, whose size is very limited, you can’t really control how deep your recursion can go. Even if your code is legit, it might fail because it recurses too much.
Do you get this feeling when doing recursions where you don’t really know if your code is ever going to stop? It’s very hard to imagine a recursion and to prove that it will stop correctly at the end.
It’s also more compatible with the following rules to use an iterative algorithm instead of a recursive one, because you have more control (again) on the size of the problem you’re dealing with

As a bonus, recursions can often come as an intuitive implementation of an algorithm but is usually also far from optimal. By example we often ask in job interviews to implement the factorial function using a recursive function but that’s far less efficient than an iterative implementation. Regular expressions too can be disastrous.

2. All loops must have fixed bounds. This prevents runaway code.

Original rule — All loops must have a fixed upper-bound. It must be trivially possible for a checking tool to prove statically that a preset upper-bound on the number of iterations of a loop cannot be exceeded. If the loop-bound cannot be proven statically, the rule is considered violated.

The idea with this rule is the same as with the interdiction of recursions: you want to prevent runaway code. The way you implement this is by making sure it’s trivial to prove statically that the loop won’t exceed a given number of iterations.
Let’s give an example in Python. You could do this:
def iter_max(it, max_iter):
cnt = 0

for x in it:
assert cnt < max_iter yield x cnt += 1 def main(): for i in iter_max(range(0, 100), 10): print(i) A language like Python will however limit the number of iterations by itself in many cases. So if you prove that the input lists won't be too long there is a bunch of cases where you don't need to do this. A good application of that is pagination: make sure that you always work with pages that are of a reasonable size and this way you won't need loops that could run forever. Always think your code so it only works on a finite amount of data and let tools that were made for that handle infinity (like your DB engine). 3. Avoid heap memory allocation. Original rule — Do not use dynamic memory allocation after initialization. This makes of course no sense in interpreted languages where literally everything is allocated dynamically. But this doesn't mean that the rule does not apply to them. The core idea of the rule is that, beyond the tedious memory management techniques that you have to use in C, it's also very important to be able to fixate an upper bound in the memory consumption of your program. So for interpreted languages it means that when you write your code, you should be able to know that given any accepted input the memory consumption won't go beyond a certain point. While this can be hard to prove in an absolute manner, there is good clues and principles that you can follow. To be more specific and to repeat the previous sections, pagination is an essential technique. If you only work with pages and that you know that the content of each page is limited (DB fields have limited length and so on) then it's quite easy to prove that at least the data coming from those pages can be contained within an upper bound. So once again if you use pages you'll know that the memory allocated to each page is reasonable and that your system can handle it. 4. Restrict functions to a single printed page. Original rule — No function should be longer than what can be printed on a single sheet of paper in a standard reference format with one line per statement and one line per declaration. Typically, this means no more than about 60 lines of code per function. This is about two different things. First, the human brain can only fully understand so much logic and the symbolic page looks about right. While this estimation is totally arbitrary you'll find that you can easily organize your code into functions of about that size or smaller and that you can easily understand those functions. Nobody likes to land on a 1000-lines function that seems to do a gazillion things at the same time. We've all been there and we know it should not happen. Second, when the function is small — or rather as small as possible — then you can worry about giving this function the least possible power. Make it work on the smallest unit of data and let it be a super simple algorithm. It will de-couple your code and make it more maintainable. And let me emphasis on the arbitrary aspect of this rule. It works for the very reason that it is arbitrary. Someone decided that they don't want to see a function longer than a page because it's not nice to work with if it is any longer. And they've also noticed that it is doable. At first I rejected this rule but more than a decade later I must say that if you just follow either of the goals mentioned above then your code will always fit in a page of paper. So yes, it's a good rule. 5. Use a minimum of two runtime assertions per function. Original rule — The assertion density of the code should average to a minimum of two assertions per function. Assertions are used to check for anomalous conditions that should never happen in real-life executions. Assertions must always be side-effect free and should be defined as Boolean tests. When an assertion fails, an explicit recovery action must be taken, e.g., by returning an error condition to the caller of the function that executes the failing assertion. Any assertion for which a static checking tool can prove that it can never fail or never hold violates this rule. (I.e., it is not possible to satisfy the rule by adding unhelpful "assert(true)" statements.) That one is tricky because you need to understand what would count as an assertion. In the original rules, assertions are consider to be a boolean test done to verify "pre- and post- conditions of functions, parameter values, return values of functions, and loop-invariants". If the test fails then the function must do something about it, typically returning an error code. In the context of C or Go it is mostly as simple as this. In the context of almost every other language it means raising an exception. And depending on the language, a lot of those assertions are made automatically. To give Python as an example, you could do this: assert "foo" in bar do_something(bar["foo"]) But why bother when the fact of doing this will also raise an exception? do_something(bar["foo"]) For me it's always very tempting to make as if the input value was always right by falling back to defaults when the input is crap. But that's usually not helpful. Instead, you should let your code fail as much as possible and use an exception reporting tool (I personally love Sentry but there is plenty out there). This way you'll know what goes wrong and you'll be able to fix your code. Of course, this means that your code will fail at runtime. But it's all right! Runtime is not production time. If you test your application extensively before sending it to production, this will allow you to see most of the bugs. Then your real users will also encounter some bugs, but you will also be informed of them, instead of things failing silently. As a side-note, if you don't have control over the input, like if you're doing an API by example, it's not always a good idea to fail. Raise an exception on incorrect input and you'll get an error 500 which is not really a good way to communicate bad input (since it would rather be something in the range of the 4xx status codes). In that case you need to properly validate the input before hand. However depending on who's using the code you might or might not want to report the exceptions. A few examples: An external tool calls your API. In that case you want to report exceptions because you want to know if the external tool is going sideways. Another of your services calls your API. In that case you also want to report exceptions as it's yourself doing things wrong. The general public calls your API. In that case you probably don't want to receive an email every time that someone does something wrong. In short it's all about knowing about the failures that you will find interesting to improve your code stability. 6. Restrict the scope of data to the smallest possible. Original rule — Data objects must be declared at the smallest possible level of scope. In short, don't use global variables. Keep your data hidden within the app and make it so that different parts of the code can't interfere with each other. You can hide your data in classes, modules, second-order functions, etc. One thing though is that when you're doing unit testing then you'll notice that this sometimes backfires to you because you want to set that data manually just for the test. This might mean that you need to hide your data away but keep a way to change it which you conventionally won't use. That's the famous _name in Python or private in other languages (which can still be accessed using reflection). 7. Check the return value of all non-void functions, or cast to void to indicate the return value is useless. Original rule — The return value of non-void functions must be checked by each calling function, and the validity of parameters must be checked inside each function. In C, the mostly-used way of indicating an error is by the return value of the corresponding function (or by reference into an error variable). However, with most interpreted languages it's simply not the case since errors are indicated by an exception. Even PHP 7 improved that (even if you still get warnings printed as HTML in the middle of your JSON if you do something non-fatal). So in truth this rule is: let errors bubble up until you can handle them (by recovering and/or logging the error). In languages that have exceptions it's pretty simple to do, simply don't catch the exceptions until you can handle them properly. See it another way: don't catch exceptions too early and don't silently discard them. Exceptions are meant to crash your code if needs to be and the proper way to deal with exceptions is to report them and fix the bug. Especially in web development where an exception will just result in a 500 response code without dramatically crashing the whole front-end. 8. Use the preprocessor sparingly. Original rule — The use of the preprocessor must be limited to the inclusion of header files and simple macro definitions. Token pasting, variable argument lists (ellipses), and recursive macro calls are not allowed. All macros must expand into complete syntactic units. The use of conditional compilation directives is often also dubious, but cannot always be avoided. This means that there should rarely be justification for more than one or two conditional compilation directives even in large software development efforts, beyond the standard boilerplate that avoids multiple inclusion of the same header file. Each such use should be flagged by a tool-based checker and justified in the code. In C code, the macros are a particularly efficient way to hide the mess. They allow you to generate C code, mostly like you would write a HTML template. It's easy to understand that it's going to be used sideways and actually you can have a look at the IOCCC contestants which usually make a very heavy use of C macros to generate totally unreadable code. However C (and C++) is mostly the only mainstream language making use of this, so how would you translate this into other languages? Did we get rid of the problem? Does compiling code into other code that will then be executed sound familiar to someone? Yes, I'm talking about the huge pile of things we put in our Webpack configurations. The initial rule recognizes the need for macros but asks that they are limited to "simple macro definitions". What is the "simple macro" of Webpack? What is the good transpiler and the bad transpiler? My rationale is simple: Keep the stack as small as possible. The less transpilers you have the less complexity you need to handle. Stay as mainstream as possible. By example I always use Webpack to transpile my JS/CSS, even in Python or PHP projects. Then I use a simple wrapper around a manifest file to get the right file paths on the server side. This allows me to stay compatible with the rest of the JS world without having to write more than a simple wrapper. Another way to put it is: stay away from things like Django Pipeline. Stay as close as possible from the real thing. Using ES6+ is nice because it's a superset of previous JS versions, so you can see transpiling as a simple layer of compatibility. I wouldn't recommend however to transpile Dart or Python or anything like that into JS. Only do it if it brings an actual value for your daily work. By example, CoffeeScript is just an obfuscated version of JavaScript so it's probably not worth the pain, while something like Stylus/LESS/Sass bring variables and mixins to CSS will help you a lot to maintain CSS code. You're the judge of good transpilers for your projects. Just don't clutter yourself with useless tools that are not worth your time. 9. Limit pointer use to a single dereference, and do not use function pointers. Original rule — The use of pointers should be restricted. Specifically, no more than one level of dereferencing is allowed. Pointer dereference operations may not be hidden in macro definitions or inside typedef declarations. Function pointers are not permitted. Anybody who's done C beyond the basic examples will know the headache of pointers. It's like inception but with computer memory, you don't really know how deep you should follow the pointers. The need for that is, by example, the qsort() function. You want to be able to sort any type of data but without knowing anything on them before compiling. Have a look at the signature: void qsort( void *ptr, size_t count, size_t size, int (*comp)(const void *, const void *) ); It's one if the most frighteningly unsafe things you'll ever see in a standard library documentation. Yet, it allows the standard library to sort any kind of data, which other more modern language still have a little bit awkward solutions. But of course when you open the gate for this kind of things, you open the gate to any kind of pointer madness. And as you know, when a gate is open then people will go through it. Hence this rule for C. However what about our case of interpreted languages? We will first cover why references are bad and then we will explain how to accomplish the initial intent of writing generic code. Don't use references Pointers don't exist but some ancient and obscure languages like PHP still thought that it would be a good idea to have it. However, most of the other languages will only use a strategy named call-by-sharing. The idea is — very quickly — that instead of passing a reference you will pass objects that can modify themselves. The core point against references is that, beyond being memory unsafe and crazy in C, they also produce side-effects. By example, in PHP: function read($source, &$n) { $content = // some way to get the content $n = // some way to get the read length return $content; } $n = 0; $content = read("foo", $n); print($n); That's a common, C-inspired, use-case for references. However, what you really want to do in this case is function read($source) { $content = // some way to get the content $n = // some way to get the read length return [$content, $n]; } list($content, $n) = read("foo"); print($n); All you need is two return values instead of one. You can also return data objects which can fit any information you want them to fit and also evolve in the future without breaking existing code. And all of this without affecting the scope of the calling function, which is rather nice. Another safety point though is when you're modifying an object then you're potentially affecting the other users of that object. That's by example a common pitfall of Moment.js. Let's see. function add(obj, attr, value) { obj[attr] = (obj[attr] || 0) + value; return obj; } const a = {foo: 1}; const b = add(a, "foo", 1); console.log(a.foo); // 2 console.log(b.foo); // 2 On the other hand you can do: function add(obj, attr, value) { const patch = {}; patch[attr] = (obj[attr] || 0) + value; return Object.assign({}, obj, patch); } const a = {foo: 1}; const b = add(a, "foo", 1); console.log(a.foo); // 1 console.log(b.foo); // 2 Both a and b stay distinct objects with distinct values because the add() function did a copy of a before returning it. Let's conclude this already-too-long section with the final form of the rule: Don't mutate your arguments unless the explicit goal of your function is to mutate your arguments. If you do so, do it by sharing and not by reference. That would by example be the no-param-reassign rule in ESLint as well as the Object.freeze() method. Or in Python you can use a NamedTuple in many cases. Note on performance: if you change the size of an object then the underlying process will basically be to allocate a new contiguous region of memory for it and then copy it. For this reason, a mutation is often a copy anyways, so don't worry about copying your objects. Leverage the weak-ish dynamic typing Now that we closed the crazy door of references, we still need to write generic code if we want to stay DRY. The good news is that while compiled languages are bound by the rules of physics and the way computers work, interpreted languages can have the luxury of putting a lot of additional support logic on top of that. Specifically, they mostly rely on duck typing. Of course you can add some level of static type checking like TypeScript, Python's type hints or PHP's type declarations. Using the wisdom of other rules: Rule 5 — Make many assertions. Expecting something from an object which doesn't actually have it will raise an exception, which you can catch and report. Rule 10 — No warnings allowed (explained hereafter). Using the various type checking mechanisms you can rely on a static analyzer to help you spot errors that would arise at runtime. Those two rules will protect you from writing dangerous generic code. Which would result in the following rule You can write generic code as long as you use as many tools as possible to catch mistakes, and especially you need to follow rules 5 and 10. 10. Compile with all possible warnings active; all warnings should then be addressed before release of the software. The initial full rule is: All code must be compiled, from the first day of development, with allcompiler warnings enabled at the compiler’s most pedantic setting. All code must compile with these setting without any warnings. All code must be checked daily with at least one, but preferably more than one, state-of-the-art static source code analyzer and should pass the analyses with zero warnings. Of course, interpreted code is not necessarily compiled so it's not about the compiler warnings per se but rather about getting the warnings. There is fortunately a great amount of warning sources out there: All the JetBrains IDEs are pretty awesome at finding out issues in your code. Recently, those IDE taught me a lot of patterns in different languages. That's really the main reason why I prefer something like this to a simplistic code editor: the warnings are very smart and helpful. Linters for all the languages JavaScript — eslint with a set of rules AirBnB maybe? Python — There is a bunch of tools that will help you either with type checking or with code smells detection Automated code review tools like SonarQube Spell checkers are also surprisingly important because they will allow you to sniff out typos regardless of type analysis or any complicated static code analysis. It's a really efficient way to not lose hours because you typed reuslts instead of results. The main thing about warnings is that you must train your brain to see them. A single warning in the IDE will drive me mad while on the other hand I know people that just won't see them. A final point on warnings is that on the contrary of compiled languages, warnings here are not always 100% certain. They are more like 95% certain and sometimes it's just an IDE bug. In that case, you should explicitly disable the warning and if possible give a small explanation of why you're sure that you don't need to apply this warning. However, think well before doing so because usually the IDE is right. Key takeaways The long discussion above tells us that those 10 rules were made for C and while you can use there philosophy in interpreted languages you can't really translate them into 10 other rules directly. Let's make our new power of 10 + 2 rules for interpreted languages. Rule 1 — Don't use goto, rationalize the use of continue and break, don't use switch. Rule 2 — Prove that your problem can never create runaway code. Rule 3 — To do so, limit the size of it. Usually using pagination, map/reduce, chunking, etc. Rule 4 — Make code that fits in your head. If it fits in a page, it fits in your head. Rule 5 — Check that things are right. Fail when wrong. Monitor failures. See rule 7. Rule 6 — Don't use global-ish variables. Store data in the smallest possible scope. Rule 7 — Let exceptions bubble up until you properly recover and/or report them. Rule 8 — If you use transpilers, make sure that they solve more problems than they bring Rule 9.1 — Don't use references even if your language supports it Rule 9.2 — Copy arguments instead of mutating them, unless it's the explicit purpose of the function Rule 9.3 — Use as many type-safety features as you can Rule 10 — Use several linters and tools to analyze your code. No warning shall be ignored. And if you take a step back, all of those rules could be summed up in one rule to rule them all. Your computer, your RAM, your hard drive even your brain are bound by limits. You need to cut your problems, code and data into small boxes that will fit your computer, RAM, hard drive and brain. And that will fit together. — Morpheus Me I consider that to be the core rule of programming and I apply it as an universal rationale to everything I do which is computer-related.

Link: https://dev.to/xowap/10-rules-to-code-like-nasa-applied-to-interpreted-languages-40dd

Do you actually want your own open source project?

Open source is great! It’s an amazing way to grow as a developer, influence our craft, make friends, build your online resume, and help solve your own and others’ problems.
Open source is terrible! Angry issues, ambiguous PRs, pressure to maintain projects you’re not using anymore — all start to feel like the worst sort of homework.
Deciding to start and run open source projects is a tough choice — there’s no right choice for any person, life context, and project and many people rightfully avoid it.
Here’s a few thoughts:

It is a lot of work

Go in expecting this. Some projects start busy and work gradually declines as the project approaches “done". Many others continue to demand more and more work as time passes. It might be fun at first to work all day Saturday on your project but that won’t last.

What is your motive?

These are some motives that are strong and sustainable and will
keep things fun and rewarding even when the project feels especially demanding:

You need the project for your job
You like making friends with other OSS developers
You want to innovate and influence the direction of our craft
You want to solve an annoying problem you keep encountering
You love learning. OSS projects bring a constant stream of new challenges your way.

Link: https://www.bricolage.io/do-you-want-your-own-open-source-project/

I Am Mesmerized By Our New Robotic Vacuum

Disclaimer: this is more a rant than anything else. Maybe more #crazydudewontstoptalkingavoidthewatercooler but that’s unwieldy as a hashtag. The #help part is in the TL;DR.
My girlfriend (somewhat impulsively) recently bought us a new robotic vacuum. We’d had the idea in the back of our heads for a while but I always assumed we were talking about at least a $500+ purchase having only heard of the big name brands like Roomba. Thanks to Lord Bezos, we got a Eufy RoboVac 30 for under $200 – selling at $270 from the manufacturer, still considerably cheaper than I had anticipated. It’s a pretty well-reviewed “budget" model. I can’t even bring myself to be that upset, this thing is really cool. Dammit, Amazon, it’s so hard to quit you. Her only other purchase? God of War for PS4. I’m gonna keep her.
I swear I’m not a Eufy shill, I’d never even heard of this brand, I’m just geeking out over our new robot. I’m not necessarily specifically recommending this model over other similar-tier models.
We put it through its inaugural run this weekend and I am utterly fascinated by it. That little thing is pretty clever, more so than I had anticipated, and I’ve just pretty much wasted my morning following it around trying to get inside its software head. I know these things have been around a long time now, but I’d never really been around one long enough to see it do its thing, and I’m impressed at how thorough a job it’s doing.
It seems to be a great example of emergent behavior from very simple parts, and I can’t stop thinking about it.
I admit, I was initially a total elitist. Some cursory searching had led me to believe that the super fancy high end ones have this whole mapping feature that figures out our floor map and stores it to efficiently cover the area, using its knowledge of the layout to optimize the path whereas the cheaper ones bumble around with sensors to get the same job done but much less efficiently, and likely only to 100% coverage over multiple runs. Not so much a replacement for normal manual vacuuming but an extra help that’s pretty good for day-to-day maintenance and touch-ups.
I hadn’t really ever thought about the problem before, so when we opened it up I had initially thought the basic MO would be to just run in straight lines until its bumper hits something, then try a new angle. Rinse, repeat for X amount of time, floor will be eventually cleaned. Do it every day or multiple times a week, and over the course of a week your whole floor is clean. Our apartment is a little funky layout-wise, and with a decent amount of floor space but odd partitions and angles in places, so I figured without some sort of stored picture of the shape of the space there was no way it could reasonably cover the area in a single 100-minute shot-in-the-dark style run.
I owe the entire industry an apology for what’s clearly been a gross underestimation. I know I should have known better, there’s all these different companies making all these different models. It’s the future now, and people have put some thought into this even (especially?) at this price point.
This thing schooled my ass on how to get the job done. I’m not even ashamed to say this is likely the cleanest these floors have been since we moved in a year ago. It’s nuts that it got the job done just through an effective application of very simple movement algorithms. If it’s not 100%, it’s pretty damn close. I ended up following it around or watching it from my desk for the duration (which felt a little embarrassing, yes, but I couldn’t stop looking) and don’t believe a spot went untouched even if it got there in a pretty roundabout way. It even crawled up on an area mat without complaining and handled all raised thresholds between rooms like a champ.
It likely simply has better suction than our manual vacuum to boot, but that particular unit of measure (1500Pα) doesn’t mean a lot to me. I couldn’t tell you what a Pascal of suction feels like, and don’t know what the other vacuum is rated. I do know it did a seriously good job considering it’s so small.
While I don’t have an uber-fancy model to compare to in the same space, this thing is leading me to believe the next level up probably wouldn’t have even been worth it, at least for us. I suppose if we had a mansion and the battery life simply wasn’t long enough for all of the floors the time saved by optimizing for our specific home might be useful. Watching it putter, though, about I actually almost believed it was mapping the space out.
Instead, the manual and some searching around suggests it simply cycles through a few modes, based on data from its sensors. It’s all very vague, but I think that might mean it really is quite simple. It starts by shooting off in a random direction, as expected, and turning when it detects an obstacle. It also has an edge-tracking mode with methodically tracks an edge by continuously attempting to turn right at a low speed, and a spot cleaning mode that spirals out from a point. You can enable each mode specifically or just let it auto-cycle, and I see no reason not to do that.
I had also underestimated the number of sensors – it’s not just a big front bumper like I thought. It’s got an infrared sensor on the side and a sensor on the bottom for drops, as well as a magnetic sensor to detect strips you can lay out to prevent it from going in certain areas. The infrared sensor may be just for the remote, but I think it might be used for locating home base as well if it’s not storing any locations.
What got me to keep watching is how this cycling of simple algorithms managed to still cover all previously uncovered territory. As I was watching it I kept thinking "oh no, it doesn’t know that it just missed a whole corner over here", watching it scoot off at some random diagonal. I actually had assumed it would be a little more methodical, tracing a zigzag across a space, but no, this thing was all random unrelated directions, which kept it interesting enough (apparently). Inevitably ten or fifteen minutes later some other seemingly random tack across the house would bring it exactly where it needed to go. It triggered edge mode at least once on every wall of every room and every weird nook and corner, got under all chairs and the kitchen table, found the bathroom a few times and got wherever it could fit, just really did a smashing job of it all. We never had to intervene.
These new vectors its choosing can’t be random. It works too well. They sure seem like they must be informed by some sort of previous understanding of found edges, but one guess I had is that it actually just tries a range of vectors over time to ensure it hits a wide range. My gut feeling is that it’s actually tuned even better than that but I couldn’t tell from watching the first run, and I’d be curious to learn more about that process. I also don’t know if that behavior is specific to this model, and others do take a more zig-zag approach. Ideally I’d try it out, but I’m not ready to bring a second vacuuming robot into our lives. I’m not that excessive, I swear.
As far as I can tell it’s not storing where specifically it finds edges, and I did notice it repeat some work – for example, it methodically scooted along the same wall of the living room three times over the course of it’s 100-minute run. If it were storing specific edges, it wouldn’t have done so, so it must have just sensed an edge and kicked in to that mode.
It must just be calibrated properly to avoid the problem. For instance, it must know how often to kick in to edge mode to maximize for exploration and minimize repeated work without sacrificing thoroughness. 100 minutes is a long time to vacuum, but not that long if you waste 20 of it on one wall over and over again. Most of the time it just turns away, though, and sometimes it even anticipates a wall and turns before it hits it well before (I think) any of its sensors would have noticed it. I can’t figure that out either if it’s not keeping track of anything. It still manages to activate for all the different walls, though!
Due to a connecting door we keep closed for airflow purposes, in the robot’s universe our unit is kind of U shaped. We have furniture in odd places and some large open areas and some small, more fractured non-rectangular areas. All six of the rooms we want it to cover are different shapes and sizes, and all have more than four walls due to nooks and crannies and closets of varying sizes. Most of the doorways are your standard door size, but the living room and dining room are connected by a big opening almost as wide as the wall but not quite. This creates even more weird edges and corners to confuse it. Optimizing a filing path in a given time constraint by hand even with prior knowledge would probably not be simple.
Somehow, amazingly, it just kept passing through rooms at different enough angles to cover the whole floor. Sometimes it’d be stuck in a room for a while, sometimes it would get on a tack that has it passing between two rooms regularly, back and forth in a big line, but by the time it hit low power mode and returned itself to the charger it had actually hit all the spots I had made mental notes to look out for.
It even surprised me once again when I got worried it ran out of battery too far from home. I heard it kick into low power mode, and it was in a completely different room facing the wrong way. It did know how to orient itself towards home and try to get there, but on the way there it hit the ottoman (so, it didn’t anticipate that was coming) and turned around the wrong way again. It even still with no line of sight managed to get itself oriented towards the station, bump its way around the ottoman to a straight shot, and amble its way back onto the contact pins. Even better, this straight shot included a bit of edge mode – which I was pleased to see still enabled even in low power mode – along the front edge of the couch. I’d been taking note of unexplored edges and that was the only one remaining it hadn’t done!
Okay. Enough gushing. Here’s the point.

TL;DR

I’m fascinated by how effective this budget-end robot vacuum’s seemingly simple set of algorithms is at cleaning our whole weird space. I want to try to model the problem and implement the movement algorithms myself to see if I can replicate that emergent behavior with totally random inputs or if it requires more tuning, and what sort of tuning if so. It’s not a type of programming I have much experience with. What would you use to explore this sort of thing?
I have some general space-filling/path-finding algorithms in my toolkit to start from, but I also know that some environments are easier than others for this sort of modeling. Some options I’ve heard of but don’t know anything about:

Processing – Java (I don’t really know Java, but am studying C++? Similar-ish?)
The python turtle – Python. I don’t know anything about turtle and very little about Python but it sounds like it can be used to explore this problem space. Python seems like a good choice for this but I don’t know the ecosystem at all.
I guess the HTML canvas element but that sounds unwieldy and complicated. Is there a framework you recommend?
I know Rust has some geospatial crates and image/graph crates for visualization, but nothing that I know of integrated for ease of use, experimentation and one-offs.
Game engines? I’ve never used any, is that the right genre of tool for this? Love2d for LUA looks like it might be a good choice but it also might be overkill.

…that’s it, really. Is there anything else I should be aware of?
Also, if you know more about these robot vacuums than I do and can enlighten me/us, please do!
Photo by Rock’n Roll Monkey on Unsplash

Link: https://dev.to/deciduously/i-am-mesmerized-by-our-new-robotic-vacuum-10pc