An implementation of the pathfinding algorithm described by

Dimdal / Jönsson, 1997. An optimal pathfinder for vehicles in real-world digital terrain maps. Masters Thesis, The Royal Institute of Science, School of Engineering Physics, Stockholm, Sweden

Demo: https://csbrandt.github.io/dimdal-pathfinder/test/

## Installation

```
$ npm install dimdal-pathfinder
```

## Methods

```
constructor(options)
```

options:object

- memInit:
string, path to memory initialization file- heightScaleFactor:
number, applied to raw (8 bit) heightmap values- maxHeightDiff:
number, the maximum difference in height between two cells before it is considered as unpassable- terrainLUT:
object,

- cost:
array, terrain class movement costs ordered by class index, infinite costs are represented by the string`"Infinity"`

- roadLUT:
object,

- cost:
array, road class movement costs ordered by class index

```
findPath(startCoord, endCoord)
```

startCoord:array, coordinate of the starting cell in X,Y order

endCoord:array, coordinate of the ending cell in X,Y order

Returns

Promise, resolved with an array of coordinates that make up the path

## Background

#### A*

The cost function of the A* (denoted as `f(x)`

) is defined as

```
f(x) = g(x) + h(x)
```

where:

`g(x)`

past path-cost function, which is the known distance from the starting node to the current node x`h(x)`

future path-cost function, which is an admissible "heuristic estimate" of the distance from x to the goal^{[wikipedia]}

#### Dimdal Pathfinder

An addition to `g(x)`

(denoted as `w(u,v)`

) is defined as^{[2]}

```
w(u,v) = e(u,v) + r(u,v) + s(u,v) + t(u,v) + v(u,v)
```

where:

`e(u,v)`

edge check function`r(u,v)`

road check function`s(u,v)`

slope function`t(u,v)`

terrain function`v(u,v)`

visibility function

such that:

```
g(x) = g(u) + w(u,v)
```

where:

`g(u)`

movement cost from the starting point to u

The A* heuristic `h(x)`

is defined as

```
h(x) = ((Diagonal Edge Length * min(dx , dy)) +
(Axial Edge Length * |dx – dy|)) *
Minimum Terrain Cost
```

where:

`dx = |SourceX – DestinationX|`

`dy = |SourceY – DestinationY|`

## Implementation Details

#### Priority queue

A Fibonacci heap is used as a priority queue within the A* algorithm. Dense search graphs (containing millions of nodes) are generated from processing real-world raster data.

#### Memory space

Dimdal^{[1]} describes an efficient graph representation that uses 3 bytes per node.

This particular implementation is designed to be used with grayscale heightmaps. Only 1 byte is required to represent the terrain height and total memory footprint per node is reduced to 2 bytes.

#### Memory initialization

A static memory initialization file is used to store all nodes in the search graph. A memory initialization file must be generated for each region in which searches will be conducted.

To generate a memory initialization file first create a configuration file and run,

```
$ node tools/generate-mem-init.js test/config.json
```

## Running Tests

Install the development dependencies:

```
$ npm install
```

Then run the tests:

```
$ firefox test/index.html
```

## Browser Bundle

```
$ npm run build
```