A playable dungeon map generator

Last days I recycled my dungeon map generator and I plugged it to the engine of my proto 61 Cygni.

You can experiment the result below. Generate a map (with custom parameters or randomly) and then click  the "play it" button.

Just for fun, you can directly catch 4 weapons: the shotgun, the machine gun, the plasmagun and the rocketgun ! You will use these to kill the bots ditributed on the level. Additionally, you can run by pushing quickly the UP key 2 times and dodge with LEFT or RIGHT keys quickly 2 times.

61 Cygni - Game prototype

I hope you will enjoy this new prototype.

Some screen captures:

I built it in 2 months. I used Box2D for physics. I used musics from Super Metroid and sounds from Doom PS1.

I used Daedalus Lib to triangulate automatically the entire level. It made things fast and easy for pathfinding, fast bullet collision detection and fog of war. A great save of time allowing me to focus deeply on gameplay.

I would love to continue the production of that game. If you are producer or publisher, feel free to contact me. We could target Steam as well as mobile platform.

Click below to play the game (fullscreen mode)

Introducing Daedalus Lib

Project on GitHub

Daedalus is a library I develop. It manages 2D environment modeling and pathfinding. I really hope this library will help designers and developers to invent new gameplays.

When I began to code Daedalus, I had many ideas in mind:

1- focus on 2D
2- fastness and accuracy
3- simplicity of use


1- Why focus only on 2D and not on 3D ? Because constraining to 2D allows to gain simplicity and efficiency. Many great games today are still based on 2D engines involving 2D mechanics, 2D physics and 2D display ; so I hope Daedalus will find his place as a new component for new 2D projects.


2- Fastness and accuracy are reached by using among the best techniques available in the fields of computational geometry : quad-edge structure and fully dynamic Delaunay triangulation. Daedalus algorithms are based on many research publications, among them:

Fully Dynamic Constrained Delaunay Triangulation by Kallmann, Bieri and Thalmann
An improved incremental algorithm for constructing... by Anglada
Efficient Triangulation-Based Pathfinding by Jon Demyen


3- For simplicity, I assumed that the library should work without any pre-generated data. Everything should work in real time : obstacles insertion/motion/deletion and path generation. Also I assumed that the library should be fault tolerant to designer/developer mistakes : obstacles can overlap and can be of any shape : open, convex, concave... At last, the path generation manages non-null size objects in order to avoid any obstacle collision.


Now the basics are exposed, I can show you some demos :

Today Daedalus is coded in Actionscript. But because the library has no dependancies from any other library (I wrote the whole mathematics), it can easily and quickly be translated in any other language. The project is not open-source at this moment, so not available for free download. I plan to use it in some new projects before release it in the open world.

So I am now looking for some collaboration. If you feel interested by Daedalus for your game engine, for a commercial game project or any other application, you can contact me directly at flash dot cedric at google mail service.

A dungeon map generator

I try to find new methods and algorithms to proceduraly build dungeons and mazes. Something that definitevely breaks up with the boring square and rectangular aligned patterns.

Finaly I had interesting results using Daedalus lib, as illustrated in the examples below:

The method I used to generate this map has many interesting properties. These are easier to understand by looking the steps of generation.

Step 1:

We generate a simple Delaunay triangulation. We use an algorithm that iterates through a nxm grid of points and add some of them in the triangulation according to a fixed probability P. With P=1, the triangulation would be a full regulat grid. With P=0, the triangulation would be empty. See a result below with a 20x20 grid and P=0.5 :

Step 2:

Considering the triangluation as a graph, starting from the vertex in the center, we use a custom depth-first algorithm to extract a sub-graph. At depth n, the next node at n+1 is choosen at random among unvisited nodes. Paramaters used are the total nodes count NC, the maximum branch depth BD and the maximum branches count BC. See a result below with NC=24 :

See that sub-graph as the underlying navigation graph of our futur dungeon. Nodes represent rooms and edges represent accessibility bewteen them.

Step 3:

We build the dual of the triangulation. It is similar to the Voronoi diagram but for better results we use the average positions instead of circumcircles centers:

The dual represents the real shape that will be used for the dungeon.

Step 4:

We keep only the dual cells surrounding the previously generated sub-graph at step 2. Then we build a fresh new triangulation from them. Additionally, we dig a door in the edges shared by connected rooms :

Step 5:

Finally we use a chamfer algorithm to add thickness to the walls :

Notice 2 importants properties of the resulting map:

1. It is directly built on a Delaunay triangulation

2: The navigation graph is given

In conclusion, navigation and pathfinding algorithms are ready to use, without any extra cost. It means that AI can navigate efficiently and accurately through the generated map using the pathfinding solution included in Daedalus.

Box2D to Daedalus bridge

Things become interesting. Now Daedalus is equiped with 2 new components to cooperate closely with Box2D.

The first is a simple Box2D-body to Daedalus- object converter. It makes things very easy when you want to generate and update a constrained Delaunay triangulation as a mirror of a Box2D world.

The second is more complex. We can see it as a Box2D body object "drived" by the Daedalus pathfinder. It applies Box2D forces to the object to ensure it closely follows a path and reach a destination. This is the first attempt and many other "driver" must be created now according to the nature of the entities we want to simulate (a pedestrian, a car, a spaceship, a robot...).

I hope you will enjoy the demo:

Pathfinding on bitmap triangulation

Because large triangulated maps can be a pain to design by hand, I implemented into Daedalus an algorithm generating optimized triangulations from bitmap images. It is directly inspired by the bitmap segmentation in the Potrace algorithm.

How does-it work ?

Just give a picture made from black and white pixels to the algorithm. For example:

Then the algorithm returns to you a clean and beautiful triangulation:

Neither parameter nor configuration filling are required. Just give the bitmap and get the mesh.

However, black and white pictures are required. But we can consider the use of a threshold to easily convert gray scale and color images.

From that, I experimented with success the use of pathfinding on several triangulations generated from:

- a SNES Mario Kart circuit map

- a Doom map

- a picture of the labyrinth at Grace Cathedral

- a map of Paris created by artist Jazzberry Blue

I used Photoshop to extract properly black and white pictures from the previous maps. Then I just played with the algorithm and enjoyed the fast and accurate pathfinding implemented in Daedalus Lib.

As you can see by yourself:

Daedalus : group pathfinding and FOV

Today 2 new demos made with Daedalus, showing in action 2 new components: group pathfinding and field of view.

The group pathfinding has many interesting features:
- it can manage any number of entities
- it can manage any size of entities
- generated paths are automatically sampled in order to synchronise the entities and avoid collision between them

We could just reproach the single common path the entities use. Indeed, if the entities have different sizes, it could be legitimate to expect them to reach the goals by different optimized paths and keep synchronisation to avoid collision. But in fact, this behavior is the subject of a new component coming soon, more focused on synchronisation for entities having completely different start and goal positions.


Then the field of view, while looking not so impressive, is a very important component to give awareness to AI entities, allowing them to detect cleverly the other entities. The features are:
- radius, opening angle, position and direction as dynamic properties
- the FOV is broken by constrained edges (other entities can hide behind walls)
- it manages non-null size entites

In the details, the FOV implementation is simply an accurate clipping system. So it can really manages the visibility of an entity through very complex situation with partially overlapping walls.

Demo : pathfinding on Delaunay constrained triangulation