Random Targeting

A method of targeting that simply chooses a random angle among the angles that could possibly hit the opponent. Some successful NanoBots use this firing method. Its implementation is very small and for unpredictable movements, it will give a consistent hit percentage.

Example

// Add import robocode.util.* for Utils
// This code goes in your onScannedRobot() event handler.

public void onScannedRobot(ScannedRobotEvent e) {
    double randomGuessFactor = (Math.random() - .5) * 2;
    double bulletPower = 3;
    double maxEscapeAngle = Math.asin(8.0/(20 - (3 * bulletPower)));
    double firingAngle = randomGuessFactor * maxEscapeAngle;
    double absBearingToEnemy = e.getBearingRadians() + getHeadingRadians();
	
    setTurnGunRightRadians(Utils.normalRelativeAngle(
            absBearingToEnemy + firingAngle - getGunHeadingRadians()));
    fire(bulletPower);
}

A simpler solution

A simpler method is to assume that the enemy is traveling in a circle around you (see Musashi Trick), which is often true among NanoBots and 1-vs-1 bots. If the enemy is traveling in a circle around you, the maximum distance it can cover before a bullet reaches it is enemy velocity / bullet velocity (in radians). For example, a power 3.0 bullet fired at an enemy going at full speed should be fired at a bearing offset between -8/11 and +8/11.

Selecting firepower

The advantage of a random gun is that it should have a roughly equal hit rate on any type of movement. This makes ideal firepower selection pretty easy to pre-calculate. In the following equations x is the choice of firepower. It is assumed that damage output per tick is the quantity you're interested in maximizing, it may not be.

Bullet damage, assuming firepower > 1:

<math>D=4x+2(x-1)</math>

The smallest size of a robot, in radians:

<math>s=\frac{36}{d}</math>, where <math>d</math> is the distance to the other bot.

The total escape area, in radians:

<math>\alpha=2 \cdot asin(\frac{8}{20-3x})</math>

Probability of a hit, assuming uniform spread of bullets over A:

<math>P=min(1,\frac{s}{\alpha})</math>

The expected damage from a shot:

<math>E=DP</math>

The heat created by a shot:

<math>H=1+\frac{x}{5}</math>

The firing frequency:

<math>F=\frac{1}{ceil(10H)}</math>

Expected damage <math>E_{x,d}</math> per tick of combat:

<math>E_{x,d}=E \cdot F = D \cdot P \cdot \frac{1}{ceil(10H)} =\frac{(6x-2) \cdot min(1,\frac{16}{d \cdot asin(\frac{8}{20-3x})})}{10+ceil(2x))}</math>

Back substitution to get the expected damage per tick in terms of distance and x and optimization of <math>E_{x,d}</math> are left as an exercise for the coder.

See also

• Maximum Escape Angle

Targeting Quick Head-On · Linear · Circular · Random · Mean · Area · Head-Fake Statistical Laser · Avg Bearing Offset · Factored Angular · Visit Count Stats · BestPSpace Log-Based Pattern Matching · Dynamic Clustering Heuristics Neural · FuzzyLogic Hybrid Symbolic Dynamic Segmentation · Zoom Targeting · SmartFactor · Anti-Surfer Firing Angle Bearing Offset · GuessFactor · Play It Forward · Displacement Vector Energy Selecting Fire Power · When To Fire Terms Bullet Travel Time · Waves · Multiple Choice · Max Escape Angle Related Targeting Challenges · Segmentation · Chase Bullets · Virtual Guns · Saving See Also: Category:Targeting