swarm2d-experiments/Pack_hunting_experimental_PID_averaged_target_online-optimization.html at master · EliasHasle/swarm2d-experiments · GitHub

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<html>
<head>
	<title>Swarm hunting with common control parameter optimization using simulated annealing</title>
	<script src="js/lib/three.js"></script>
	<!--My scripts:-->
	<script src="js/Swarm.js"></script>
	<script src="js/Vector2D_operations.js"></script>
	<script src="js/Steering.js"></script>
	<script src="js/ParameterOptimizer.js"></script>
	<script src="js/PID.js"></script>
</head>

<body>

<nav style="position: absolute; top:0; left:0, height:2vh; z-index:999">
	<a href="." style="color: #66ffff">Back to index.</a>
</nav>

<script>
"use strict";

/*TODO: Maybe drop three.js in favor of pure WebGL.*/

//Shorthands
var PI = Math.PI, cos = Math.cos, sin = Math.sin, max=Math.max, min=Math.min, abs=Math.abs, random=Math.random, exp=Math.exp, PHI = (1+5**0.5)/2;

//Globals for graphics
var renderer, scene, camera;

//Globals for simulation
var targets, agents, numActiveT, hits;

//SIMULATION PARAMETERS:
let A = 20; //Number of agents
let T = 500; //Number of targets
let W = 800; //Space width
let H = 600; //Space height
let maxR = 100; //Maximal target orbit radius
let S = 5; //point radius
let Cdrag = 0.1;
let aMaxPreys = 100; //maximal goal acceleration
let aMaxHunters = 2*aMaxPreys;

//Game loop function
var animate = (function() {
	let t_start = 0.001*performance.now();
	let ot = t_start;
	return function(t) {
		t *= 0.001;
		let dt = (t-ot);// || 0.0000001; //0 not allowed
		ot = t;

		if (numActiveT === 0) {
			console.info("Done after %.2f seconds", 0.001*performance.now()-t_start);
			return;
		}

		//MOVE TARGETS
		targets.update(t, dt);

		//AGENTS
		agents.update(t, dt);

		renderer.render(scene, camera);
		requestAnimationFrame(animate);
	};
})();

function selectGoal([ax,ay],i,velPID,posPID,agentPos,tPos,tAct,gParams) {
	let {eInteract, wAvoid, separation} = gParams;
	let Area = W*H;

	let [agx,agy] = [0,0];
	if (numActiveT === 0) return [agx,agy];

	let sumWeights = 0;

	//Approach targets:
	for (let j = 0; j < T; j++) {
		if (!tAct[j]) continue;

		let [gx,gy] = tPos[j];

		let r2 = lengthSq(sub([gx,gy],[ax,ay]));
		if (r2 < S*S) {
			tAct[j] = 0;
			velPID.reset();
			posPID.reset();
			hits += 1/numActiveT**1.2;//**2; //right? //Performance
			numActiveT--;
			if (numActiveT === 0) return [agx,agy];
			continue;
		}
		//let r = r2**0.5;
		let weight = (1-wAvoid)*exp(-((2*r2/Area)**0.5)*eInteract);
		if (isNaN(weight)) console.error("Approach weight is NaN!");
		[agx,agy] = add([agx,agy], scale([gx,gy],weight));
		sumWeights += weight;
	}
	//And avoid other agents, to limit clustering:
	for (let j = 0; j < A; j++) {
		if (j===i) continue;
		let [oax,oay] = agentPos[j];
		let diff = delta([ax,ay], [oax,oay]);
		let r2 = lengthSq(diff);
		let r = r2**0.5;
		let weight = wAvoid*exp(-((2*r2/Area)**0.5)*eInteract);
		if (isNaN(weight)) console.error("Avoid weight is NaN!");
		agx += max(0,min(W,(ax-separation/(diff[0]+Math.sign(diff[0])*0.01))))*weight;
		agy += max(0,min(H,(ay-separation/(diff[1]+Math.sign(diff[0])*0.01))))*weight;
		sumWeights += weight;
	}

	if (isNaN(sumWeights) || sumWeights===0) console.error("sumWeights is NaN or 0: ", sumWeights);
	if (isNaN(agx)) console.error("agx is NaN!");
	if (isNaN(agy)) console.error("agy is NaN!");

	agx /= sumWeights;
	agy /= sumWeights;

	return [agx,agy];
}

//INITIALISATION
(function main() {
	numActiveT = T; //Global that holds number of active targets

	//Renderer setup
	document.body.style = "overflow: hidden;";
	var container = document.createElement("div");
	Object.assign(container.style, {
		position: "absolute",
		top: 0,
		left: 0,
		width: W,
		height: H
	});
	document.body.appendChild(container);
	renderer = new THREE.WebGLRenderer({antialias: true});
	renderer.setSize(W, H);
	//renderer.setClearColor(0x000000);
	container.appendChild(renderer.domElement);

	//Scene setup:
	scene = new THREE.Scene();
	scene.add(new THREE.AmbientLight(0xffffff, 0.5));

	//Camera setup
	camera = new THREE.OrthographicCamera(0,W, H, 0, 1, 200);
	camera.position.set(0, 0, 100);
	scene.add(camera);

	//INITIATE TARGETS:

	//Orbit parameters:
	//aMax=0.5*r*(2*PI*maxF)**2;
	//==> maxF = sqrt(2*aMax/r)/(2*PI)
	let ft = new Float32Array(T);
	let rt = new Float32Array(T);
	let ot = new Float32Array(T);
	for (let i = 0; i < T; i++) {
		rt[i] = random()*maxR;
		ft[i] = (2*random()-1)*(2*aMaxPreys/rt[i])**0.5/(2*PI);
		ot[i] = random()*2*PI;
	}

	//Target swarm object
	targets = new Swarm({
		number: T,
		activeColor: 0x00ff00,
		passiveColor: 0x0000ff,
		pointRadius: S,
		initProc: function(pa,va,aa) {
			for (let i = 0; i < T; i++) {
				pa[i][0] = random()*(W-2*maxR)+maxR;
				pa[i][1] = random()*(H-2*maxR)+maxR;
			}
		},
		//Update procedure:
		updateProc: function(pa, va, aa, t, dt) {
			for (let i = 0; i < T; i++) {
				if (!aa[i]) continue;
				let r = rt[i];
				let f = ft[i];
				let o = ot[i];
				let omega = 2*PI*f;
				let v = r*omega;
				let C = v*dt;
				let a = r*omega**2;
				let D = 0.5*a*dt**2;
				pa[i][0] = pa[i][0] - C*sin(o+omega*t) - D*cos(o+omega*t);
				pa[i][1] = pa[i][1] + C*cos(o+omega*t) -D*sin(o+omega*t);
			}
		}
	});
	scene.add(targets);

	//AGENTS:

	//Optimizers for common PID parameters:
	/*Note that velOptimizer is updated more often than posOptimizer, and that the ratio between the rates is the golden ratio, to minimize update resonance that could interfere with optimization or affect PID operation. Likewise, the goal optimizer (at least initially) updates less frequently than both the velOptimizer and the posOptimizer, and the initial frequency ratio against the posOptimizer is again the golden ratio. The goal setting optimizer will later adapt its update frequency in response to the frequency of hits, but always in powers of sqrt(2), so it will not easily null out the exceptional irrationality of the golden ratio that ensures minimal resonance.*/

	let baseUpdatePeriod = /*60**/10*T;
	let velOptimizer, posOptimizer;

	velOptimizer = new ParameterOptimizer(
		"Swarm velocity PID",
		{
			Kp: {val: 1.5, minval: 0, scale: 0.2, minLR: 0.01},
			Ki: {val: 0.7, minval: 0, scale: 0.2, minLR: 0.01},
			Kd: {val: 0.8, minval: 0, scale: 0.2, minLR: 0.01},
			iDecay: {val: 1, minval: 0.99, maxval: 1, scale: 0.001},
			dSmoothing: {val: 0, minval: 0, maxval: 0.01, scale: 0.001}
		},
		{
			probWorse: 0.5,
			minimize: true,
			threshold: baseUpdatePeriod,
			pwDecay: 0.99
		});
posOptimizer = new ParameterOptimizer(
		"Swarm position PID",
		{
			Kp: {val: 1.8, minval: 0, scale: 0.2, minLR: 0.01},
			Ki: {val: 1, minval: 0, scale: 0.2, minLR: 0.01},
			Kd: {val: 0.16, minval: 0, scale: 0.2, minLR: 0.01},
			iDecay: {val: 1, minval: 0.99, maxval: 1, scale: 0.001},
			dSmoothing: {val: 0, minval: 0, maxval: 0.01, scale: 0.001}
		},
		{
			probWorse: 0.5,
			minimize: true,
			threshold: PHI*baseUpdatePeriod, //Golden ratio
			pwDecay: 0.984
	});

	//Individual PID controllers:
	let agentVelPID = new Array(A);
	let agentPosPID = new Array(A);
	for (let i = 0; i < A; i++) {
		agentVelPID[i] = new PID(2, velOptimizer);
		agentPosPID[i] = new PID(2, posOptimizer);
	}

	//Optimizer for common goal setting parameters:
	let goalSettingOptimizer = new ParameterOptimizer(
		"Goal setting parameters",
		{
			eInteract: {val: 11, minval: 4, maxval: 13, scale: 0.9, minLR: 0.05},
			wAvoid: {val: 0.07, minval: 0.01, maxval: 0.2, scale: 0.05},
			separation: {val: 1.67, minval: 0.01, maxval: 3, scale: 0.4}
		},
		{
			probWorse: 0.5,
			minimize: false,
			threshold: PHI**2*baseUpdatePeriod/A, //Golden ratio
			pwDecay: 0.97
	});

	agents = new Swarm({
		number: A,
		activeColor: 0xff0000,
		passiveColor: 0x00ffff,
		pointRadius: S,
		initProc: function(pa, va, aa) {
			for (let i = 0; i < A; i++) {
				pa[i][0] = random()*W;
				pa[i][1] = random()*H;
			}
		},
		//Update procedure:
		updateProc: function(agentPos, agentVel, ignoreActive, t, dt) {
			let tPos = targets.positions;
			let tAct = targets.active;
			let gParams = goalSettingOptimizer.getParameters();

			hits = 0;

			let accs = new Array(A);

			for (let i = 0; i < A; i++) {
				let [ax,ay] = agentPos[i];

				let [agx,agy] = selectGoal([ax,ay],i,agentVelPID[i],agentPosPID[i],agentPos,tPos,tAct, gParams);

				//Calculate control signal:
				let [targetVelX, targetVelY] = agentPosPID[i].update([ax,ay], [agx,agy], dt);

				if (isNaN(targetVelX) || isNaN(targetVelY)) {
					console.error("%d: Target velocity has NaN!", i);
					//console.error("\tagentVel[3*i]="+agentVel[3*i]);
					//console.error("\tagentVel[3*i+1]="+agentVel[3*i+1]);
					return;
				}

				let [avx,avy] = agentVel[i];

				let [accx, accy] = agentVelPID[i].update([avx,avy], [targetVelX, targetVelY], dt);
				if (abs(accx)===Infinity || abs(accy)===Infinity) {
					console.error("%d: Infinite control signal!", i);
					return;
				}
				if (isNaN(accx) || isNaN(accy)) {
					console.error("%d: Control is NaN!", i);
					console.error("\taccx="+accx);
					console.error("\taccy="+accy);
					return;
				}

				//Clamp controls:
				([accx,accy] = clampLength([accx,accy],aMaxHunters));

				//Update physical agent state:
				//Speed damping. I think this is a bit wrong, actually, but it suits the purpose.
				([accx, accy] = add([accx,accy], calculateDrag([avx,avy], Cdrag)));
				accs[i] = [accx,accy];
			}
			agents.applyAccelerations(accs, dt);

			goalSettingOptimizer.accumulate(hits);
		}
	});
	scene.add(agents);

	requestAnimationFrame(animate);
})();
</script>
</body>
</html>