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author | Jie Tan <jietan@jietan0.mtv.corp.google.com> | 2017-02-09 14:43:40 -0800 |
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committer | Jie Tan <jietan@jietan0.mtv.corp.google.com> | 2017-02-09 14:43:40 -0800 |
commit | 509b77054af28124035080dbd963bb0ae2176101 (patch) | |
tree | 823e74347a0edef12791b5a9f73d44084f1cdcad /examples | |
parent | 4df8b2762669f7ad116c763fea417c1830e37521 (diff) | |
download | bullet3-509b77054af28124035080dbd963bb0ae2176101.tar.gz |
now minitaur class can output joint angles, velocities and torques. I also extract evaluate functions to a file
Diffstat (limited to 'examples')
-rw-r--r-- | examples/pybullet/minitaur.py | 72 | ||||
-rw-r--r-- | examples/pybullet/minitaur_evaluate.py | 59 | ||||
-rw-r--r-- | examples/pybullet/minitaur_test.py | 37 |
3 files changed, 137 insertions, 31 deletions
diff --git a/examples/pybullet/minitaur.py b/examples/pybullet/minitaur.py index 5364d8124..58aab4a38 100644 --- a/examples/pybullet/minitaur.py +++ b/examples/pybullet/minitaur.py @@ -2,15 +2,11 @@ import pybullet as p import numpy as np class Minitaur: - def __init__(self): + def __init__(self, urdfRootPath=''): + self.urdfRootPath = urdfRootPath self.reset() - def reset(self): - self.quadruped = p.loadURDF("quadruped/quadruped.urdf",0,0,.3) - self.kp = 1 - self.kd = 0.1 - self.maxForce = 3.5 - self.motorDir = [1, -1, 1, -1, -1, 1, -1, 1] + def buildJointNameToIdDict(self): nJoints = p.getNumJoints(self.quadruped) self.jointNameToId = {} for i in range(nJoints): @@ -20,13 +16,39 @@ class Minitaur: for i in range(100): p.stepSimulation() + def buildMotorIdList(self): + self.motorIdList.append(self.jointNameToId['motor_front_leftR_joint']) + self.motorIdList.append(self.jointNameToId['motor_front_leftL_joint']) + self.motorIdList.append(self.jointNameToId['motor_back_leftR_joint']) + self.motorIdList.append(self.jointNameToId['motor_back_leftL_joint']) + self.motorIdList.append(self.jointNameToId['motor_front_rightL_joint']) + self.motorIdList.append(self.jointNameToId['motor_front_rightR_joint']) + self.motorIdList.append(self.jointNameToId['motor_back_rightL_joint']) + self.motorIdList.append(self.jointNameToId['motor_back_rightR_joint']) + + + def reset(self): + self.quadruped = p.loadURDF("%s/quadruped/quadruped.urdf" % self.urdfRootPath,0,0,.3) + self.kp = 1 + self.kd = 0.1 + self.maxForce = 3.5 + self.nMotors = 8 + self.motorIdList = [] + self.motorDir = [1, -1, 1, -1, -1, 1, -1, 1] + self.buildJointNameToIdDict() + self.buildMotorIdList() + + def disableAllMotors(self): nJoints = p.getNumJoints(self.quadruped) for i in range(nJoints): p.setJointMotorControl2(bodyIndex=self.quadruped, jointIndex=i, controlMode=p.VELOCITY_CONTROL, force=0) + def setMotorAngleById(self, motorId, desiredAngle): + p.setJointMotorControl2(bodyIndex=self.quadruped, jointIndex=motorId, controlMode=p.POSITION_CONTROL, targetPosition=desiredAngle, positionGain=self.kp, velocityGain=self.kd, force=self.maxForce) + def setMotorAngleByName(self, motorName, desiredAngle): - p.setJointMotorControl2(bodyIndex=self.quadruped, jointIndex=self.jointNameToId[motorName], controlMode=p.POSITION_CONTROL, targetPosition=desiredAngle, positionGain=self.kp, velocityGain=self.kd, force=self.maxForce) + self.setMotorAngleById(self.jointNameToId[motorName], desiredAngle) def resetPose(self): #right front leg @@ -76,11 +98,29 @@ class Minitaur: def applyAction(self, motorCommands): motorCommandsWithDir = np.multiply(motorCommands, self.motorDir) - self.setMotorAngleByName('motor_front_leftR_joint', motorCommandsWithDir[0]) - self.setMotorAngleByName('motor_front_leftL_joint', motorCommandsWithDir[1]) - self.setMotorAngleByName('motor_back_leftR_joint', motorCommandsWithDir[2]) - self.setMotorAngleByName('motor_back_leftL_joint', motorCommandsWithDir[3]) - self.setMotorAngleByName('motor_front_rightL_joint', motorCommandsWithDir[4]) - self.setMotorAngleByName('motor_front_rightR_joint', motorCommandsWithDir[5]) - self.setMotorAngleByName('motor_back_rightL_joint', motorCommandsWithDir[6]) - self.setMotorAngleByName('motor_back_rightR_joint', motorCommandsWithDir[7]) + for i in range(self.nMotors): + self.setMotorAngleById(self.motorIdList[i], motorCommandsWithDir[i]) + + def getMotorAngles(self): + motorAngles = [] + for i in range(self.nMotors): + jointState = p.getJointState(self.quadruped, self.motorIdList[i]) + motorAngles.append(jointState[0]) + motorAngles = np.multiply(motorAngles, self.motorDir) + return motorAngles + + def getMotorVelocities(self): + motorVelocities = [] + for i in range(self.nMotors): + jointState = p.getJointState(self.quadruped, self.motorIdList[i]) + motorVelocities.append(jointState[1]) + motorVelocities = np.multiply(motorVelocities, self.motorDir) + return motorVelocities + + def getMotorTorques(self): + motorTorques = [] + for i in range(self.nMotors): + jointState = p.getJointState(self.quadruped, self.motorIdList[i]) + motorTorques.append(jointState[3]) + motorTorques = np.multiply(motorTorques, self.motorDir) + return motorTorques diff --git a/examples/pybullet/minitaur_evaluate.py b/examples/pybullet/minitaur_evaluate.py new file mode 100644 index 000000000..73212f125 --- /dev/null +++ b/examples/pybullet/minitaur_evaluate.py @@ -0,0 +1,59 @@ +from minitaur import Minitaur +import time +import numpy as np +import pybullet as p +import math +import sys + +minitaur = None + +def current_position(): + global minitaur + position = minitaur.getBasePosition() + return np.asarray(position) + +def is_fallen(): + global minitaur + orientation = minitaur.getBaseOrientation() + rotMat = p.getMatrixFromQuaterion(orientation) + localUp = rotMat[6:] + return np.dot(np.asarray([0, 0, 1]), np.asarray(localUp)) < 0 + + +def evaluate_params_hop(params, urdfRoot='', timeStep=0.01, maxNumSteps=1000, sleepTime=0): + print('start evaluation') + beforeTime = time.time() + p.resetSimulation() + + p.setTimeStep(timeStep) + p.loadURDF("%s/plane.urdf" % urdfRoot) + p.setGravity(0,0,-10) + + amplitude = params[0] + speed = params[1] + + global minitaur + minitaur = Minitaur(urdfRoot) + start_position = current_position() + last_position = None # for tracing line + + for i in range(maxNumSteps): + a1 = math.sin(i*speed)*amplitude+1.57 + a2 = math.sin(i*speed+3.14)*amplitude+1.57 + joint_values = [a1, 1.57, a2, 1.57, 1.57, a1, 1.57, a2] + minitaur.applyAction(joint_values) + p.stepSimulation() + if (is_fallen()): + break + + if i % 100 == 0: + sys.stdout.write('.') + sys.stdout.flush() + time.sleep(sleepTime) + + print(' ') + + final_distance = np.linalg.norm(start_position - current_position()) + elapsedTime = time.time() - beforeTime + print ("trial for amplitude", amplitude, "speed", speed, "final_distance", final_distance, "elapsed_time", elapsedTime) + return final_distance diff --git a/examples/pybullet/minitaur_test.py b/examples/pybullet/minitaur_test.py index ddc2ac136..2b0726f6f 100644 --- a/examples/pybullet/minitaur_test.py +++ b/examples/pybullet/minitaur_test.py @@ -1,6 +1,7 @@ import pybullet as p from minitaur import Minitaur +import minitaur_evaluate import time import math import numpy as np @@ -10,24 +11,30 @@ def main(unused_args): c = p.connect(p.SHARED_MEMORY) if (c<0): c = p.connect(p.GUI) - p.resetSimulation() - p.setTimeStep(timeStep) - p.loadURDF("plane.urdf") - p.setGravity(0,0,-10) - minitaur = Minitaur() amplitude = 0.24795664427 speed = 0.2860877729434 - for i in range(1000): - a1 = math.sin(i*speed)*amplitude+1.57 - a2 = math.sin(i*speed+3.14)*amplitude+1.57 - joint_values = [a1, 1.57, a2, 1.57, 1.57, a1, 1.57, a2] - minitaur.applyAction(joint_values) - - p.stepSimulation() -# print(minitaur.getBasePosition()) - time.sleep(timeStep) - final_distance = np.linalg.norm(np.asarray(minitaur.getBasePosition())) + + final_distance = minitaur_evaluate.evaluate_params_hop(params=[amplitude, speed], timeStep=timeStep, sleepTime=timeStep) print(final_distance) + # p.resetSimulation() + # p.setTimeStep(timeStep) + # p.loadURDF("plane.urdf") + # p.setGravity(0,0,-10) + + # minitaur = Minitaur() + + # for i in range(1000): + # a1 = math.sin(i*speed)*amplitude+1.57 + # a2 = math.sin(i*speed+3.14)*amplitude+1.57 + # joint_values = [a1, 1.57, a2, 1.57, 1.57, a1, 1.57, a2] + # minitaur.applyAction(joint_values) + # torques = minitaur.getMotorTorques() + # print(torques) + # p.stepSimulation() + # time.sleep(timeStep) + # final_distance = np.linalg.norm(np.asarray(minitaur.getBasePosition())) + # print(final_distance) + main(0) |