【脑洞大赛】第6组老少爷们队 DF创客社区

本帖最后由 longxchen 于 2016-7-24 16:41 编辑

神经元探索车
用Genuino 101 (神经元)模块通过wifi带视屏和抓手的小车远程控制取物排障，基本原理见原理图。
拓展：可以用四驱飞机投放到更加复杂地形进行排障，救援工作。

神经元部分代码：

#include "BLESerial.h"
#include "CurieIMU.h"
int ax, ay, az;       // accelerometer values
int gx, gy, gz;       // gyrometer values
int calibrateOffsets = 1; // int to determine whether calibration takes place or not
#include <CurieNeurons.h>
CurieNeurons hNN;
int catL=0; // category to learn
int prevcat=0; // previously recognized category
int dist, cat, nid, nsr, ncount; // response from the neurons
//
// Variables used for the calculation of the feature vector
//
#define sampleNbr 10  // number of samples to assemble a vector
#define signalNbr  6  // ax,ay,az,gx,gy,gz
int raw_vector[sampleNbr*signalNbr]; // vector accumulating the raw sensor data
byte vector[sampleNbr*signalNbr]; // vector holding the pattern to learn or recognize
int mina=0xFFFF, maxa=0, ming=0xFFFF, maxg=0, da, dg;
void setup()
{
  pinMode(13,OUTPUT);
  Serial.begin(9600); // initialize Serial communication
  BLESerial.setName("Bluno101");
  BLESerial.begin();
  //Serial.println("Bluetooth device active, waiting for connections...");
  while(!BLESerial);

  //while(!Serial); //Not need Serial while Demo zkp // wait for the serial port to open
  // initialize device
  Serial.println("Initializing IMU device...");
  CurieIMU.begin();
  // use the code below to calibrate accel/gyro offset values
  if (calibrateOffsets == 1)
  {
Serial.println("About to calibrate. Make sure your board is stable and upright");
delay(5000);
Serial.print("Starting Gyroscope calibration and enabling offset compensation...");
CurieIMU.autoCalibrateGyroOffset();
Serial.println(" Done");
Serial.print("Starting Acceleration calibration and enabling offset compensation...");
CurieIMU.autoCalibrateAccelerometerOffset(X_AXIS, 0);
CurieIMU.autoCalibrateAccelerometerOffset(Y_AXIS, 0);
CurieIMU.autoCalibrateAccelerometerOffset(Z_AXIS, 1);
Serial.println(" Done");
  }

  // Initialize the neurons and set a conservative Max Influence Field
  hNN.Init();
  hNN.Forget(1000); //set a conservative  Max Influence Field prior to learning
  //int value=hNN.MAXIF(); // read the MAXIF back to verify proper SPI communication
  //Serial.print("\nMaxif register=");Serial.print(value);

  Serial.print("\n\nEntering loop...");
  Serial.print("\nMove the module vertically or horizontally...");
  Serial.print("\ntype 1 + Enter if vertical motion");
  Serial.print("\ntype 2 + Enter if horizontal motion");
  Serial.print("\ntype 0 + Enter for any other motion");
}
void loop()
{
// Learn if push button depressed and report if a new neuron is committed
if (Serial.available() == 2)
{
   catL = Serial.read();
   char inChar = (char)Serial.read();
   if (inChar == '\n')
   {
      catL = catL - 48;
      Serial.print("\nLearning motion category "); Serial.print(catL);
      // learn 5 consecutive sample vectors
      for (int i=0; i<5; i++)
      {
      getVector(); // the vector array is a global
      //Serial.print("\nVector = ");
      //for (int i=0; i<signalNbr*sampleNbr; i++) {Serial.print(vector);Serial.print("\t");}
      ncount=hNN.Learn(vector, sampleNbr*signalNbr, catL);
      }
      Serial.print("\tNeurons="); Serial.print(ncount);
   }
}
else
{
   // Recognize
   getVector(); // the vector array is a global
   hNN.Classify(vector, sampleNbr*signalNbr,&dist, &cat, &nid);
   if (cat!=prevcat && BLESerial.operator bool())
   {
      digitalWrite(13,HIGH);
      if (cat!=0x7FFF)
      {
      Serial.print("\nMotion category #"); Serial.print(cat);
      //BLESerial.write("\nMotion category # ");BLESerial.println(cat);
      switch(cat) {
         case 1: BLESerial.println("F"); break;
         case 2: BLESerial.println("B"); break;
         case 3: BLESerial.println("L"); break;
         case 4: BLESerial.println("R"); break;
         case 5: BLESerial.println("S"); break;
         default: BLESerial.println(cat); break;
      }
      delay(100);
      }
      else
      {
      Serial.print("\nMotion unknown");
      //BLESerial.write("\nMotion unknown ");
      }
      prevcat=cat;
   }
}
}
void getVector()
{
  // the reset of the min and max values is optional depending if you want to
  // use a running min and max from the launch of the script or not
  mina=0xFFFF, maxa=0, ming=0xFFFF, maxg=0, da, dg;

  for (int sampleId=0; sampleId<sampleNbr; sampleId++)
  {
//Build the vector over sampleNbr and broadcast to the neurons
CurieIMU.readMotionSensor(ax, ay, az, gx, gy, gz);

// update the running min/max for the a signals
if (ax>maxa) maxa=ax; else if (ax<mina) mina=ax;
if (ay>maxa) maxa=ay; else if (ay<mina) mina=ay;
if (az>maxa) maxa=az; else if (az<mina) mina=az;
da= maxa-mina;

// update the running min/max for the g signals
if (gx>maxg) maxg=gx; else if (gx<ming) ming=gx;
if (gy>maxg) maxg=gy; else if (gy<ming) ming=gy;
if (gz>maxg) maxg=gz; else if (gz<ming) ming=gz;
dg= maxg-ming;
// accumulate the sensor data
raw_vector[sampleId*signalNbr]= ax;
raw_vector[(sampleId*signalNbr)+1]= ay;
raw_vector[(sampleId*signalNbr)+2]= az;
raw_vector[(sampleId*signalNbr)+3]= gx;
raw_vector[(sampleId*signalNbr)+4]= gy;
raw_vector[(sampleId*signalNbr)+5]= gz;
  }

  // normalize vector
  for(int sampleId=0; sampleId < sampleNbr; sampleId++)
  {
for(int i=0; i<3; i++)
{
   vector[sampleId*signalNbr+i]  = (((raw_vector[sampleId*signalNbr+i] - mina) * 255)/da) & 0x00FF;
   vector[sampleId*signalNbr+3+i]  = (((raw_vector[sampleId*signalNbr+3+i] - ming) * 255)/dg) & 0x00FF;
}
  }
}