diff --git a/notebooks/MPC_racecar.ipynb b/notebooks/MPC_racecar.ipynb index e446be3..5669c03 100644 --- a/notebooks/MPC_racecar.ipynb +++ b/notebooks/MPC_racecar.ipynb @@ -69,17 +69,11 @@ "A =\n", "\\quad\n", "\\begin{bmatrix}\n", - "\\frac{\\partial f(x,u)}{\\partial x} & \\frac{\\partial f(x,u)}{\\partial y} & \\frac{\\partial f(x,u)}{\\partial \\theta} \\\\\n", + "\\frac{\\partial f(x,u)}{\\partial x} & \\frac{\\partial f(x,u)}{\\partial y} & \\frac{\\partial f(x,u)}{\\partial v} & \\frac{\\partial f(x,u)}{\\partial \\theta} \\\\\n", "\\end{bmatrix}\n", "\\quad\n", "=\n", - "\\quad\n", - "\\begin{bmatrix}\n", - "0 & 0 & -v\\sin{\\theta} \\\\\n", - "0 & 0 & v\\cos{\\theta} \\\\\n", - "0 & 0 & 0\\\\\n", - "\\end{bmatrix}\n", - "\\quad\n", + "\\displaystyle \\left[\\begin{matrix}0 & 0 & \\cos{\\left(\\theta \\right)} & - v \\sin{\\left(\\theta \\right)}\\\\0 & 0 & \\sin{\\left(\\theta \\right)} & v \\cos{\\left(\\theta \\right)}\\\\0 & 0 & 0 & 0\\\\0 & 0 & \\frac{\\tan{\\left(\\delta \\right)}}{L} & 0\\end{matrix}\\right]\n", "$\n", "\n", "and\n", @@ -88,17 +82,11 @@ "B = \n", "\\quad\n", "\\begin{bmatrix}\n", - "\\frac{\\partial f(x,u)}{\\partial v} & \\frac{\\partial f(x,u)}{\\partial \\delta} \\\\\n", + "\\frac{\\partial f(x,u)}{\\partial a} & \\frac{\\partial f(x,u)}{\\partial \\delta} \\\\\n", "\\end{bmatrix}\n", "\\quad\n", "= \n", - "\\quad\n", - "\\begin{bmatrix}\n", - "\\cos{\\theta} & 0 \\\\\n", - "\\sin{\\theta} & 0 \\\\\n", - "\\frac{\\tan{\\delta}}{L} & \\frac{v}{L{\\cos{\\delta}}^2}\n", - "\\end{bmatrix}\n", - "\\quad\n", + "\\displaystyle \\left[\\begin{matrix}0 & 0\\\\0 & 0\\\\1 & 0\\\\0 & \\frac{v \\left(\\tan^{2}{\\left(\\delta \\right)} + 1\\right)}{L}\\end{matrix}\\right]\n", "$\n", "\n", "are the *Jacobians*.\n", @@ -613,19 +601,23 @@ "source": [ "# Problem Formulation: Study case\n", "\n", - "In this case, the objective function to minimize is given the sum of the **cross-track error** plus **heading error**\n", + "In this case, the objective function to minimize is given by: \n", "\n", "$\n", "\\begin{equation}\n", "\\begin{aligned}\n", - "\\min_{} \\quad & \\sum^{t+T-1}_{j=t} cte_{j|t,ref}^TQcte_{j,t,ref} + \\psi_{j|t,ref}^TP\\psi_{j|t,ref} + u^T_{j|t}Ru_{j|t} \\\\\n", + "\\min_{} \\quad & \\sum^{t+T-1}_{j=t} cte_{j|t,ref}^TQcte_{j,t,ref} + \\psi_{j|t,ref}^TP\\psi_{j|t,ref} + (v_{j|t} - v_{j|t,ref})^TK(v_{j|t} - v_{j|t,ref}) + u^T_{j|t}Ru_{j|t} \\\\\n", "\\textrm{s.t.} \\quad & x(0) = x0\\\\\n", - " & x_{j+1|t} = Ax_{j|t}+Bu_{j|t}) \\quad \\textrm{for} \\quad t:29: RuntimeWarning: invalid value encountered in true_divide\n", " v /= np.linalg.norm(v)\n", - ":34: RankWarning: Polyfit may be poorly conditioned\n", + ":34: RankWarning: Polyfit may be poorly conditioned\n", " K=road_curve(x_sim[:,sim_time],track)\n", - ":34: RankWarning: Polyfit may be poorly conditioned\n", + ":34: RankWarning: Polyfit may be poorly conditioned\n", " K=road_curve(x_sim[:,sim_time],track)\n", - ":34: RankWarning: Polyfit may be poorly conditioned\n", + ":34: RankWarning: Polyfit may be poorly conditioned\n", " K=road_curve(x_sim[:,sim_time],track)\n", - ":34: RankWarning: Polyfit may be poorly conditioned\n", + ":34: RankWarning: Polyfit may be poorly conditioned\n", + " K=road_curve(x_sim[:,sim_time],track)\n", + ":34: RankWarning: Polyfit may be poorly conditioned\n", + " K=road_curve(x_sim[:,sim_time],track)\n", + ":34: RankWarning: Polyfit may be poorly conditioned\n", " K=road_curve(x_sim[:,sim_time],track)\n" ] }, @@ -859,15 +855,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "CVXPY Optimization Time: Avrg: 0.2116s Max: 0.2973s Min: 0.1930s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":34: RankWarning: Polyfit may be poorly conditioned\n", - " K=road_curve(x_sim[:,sim_time],track)\n" + "CVXPY Optimization Time: Avrg: 0.2258s Max: 0.3213s Min: 0.2024s\n" ] } ], @@ -928,8 +916,7 @@ " #Prediction Horizon\n", " for t in range(T):\n", "\n", - " #cost += 30*cp.sum_squares(x[2,t]-np.arctan(df(x_bar[0,t],K))) # psi\n", - " cost += 50*cp.sum_squares(x[3,t]-np.arctan(df(x_bar[0,t],K))) # psi\n", + " cost += 30*cp.sum_squares(x[3,t]-np.arctan(df(x_bar[0,t],K))) # psi\n", " cost += 20*cp.sum_squares(f(x_bar[0,t],K)-x[1,t]) # cte\n", " cost += 10*cp.sum_squares(1.-x[2,t]) # desired v\n", "\n", @@ -978,12 +965,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] diff --git a/notebooks/equations.ipynb b/notebooks/equations.ipynb index b210987..4047e11 100644 --- a/notebooks/equations.ipynb +++ b/notebooks/equations.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -28,7 +28,7 @@ "[0, 0, 0, v*cos(psi), 0]])" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -69,7 +69,7 @@ "[ sin(psi), 0]])" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -98,7 +98,7 @@ "[0, 0, 1]])" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -135,7 +135,7 @@ "[ 0, dt]])" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -156,27 +156,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\left[\\begin{matrix}0 & 0 & \\cos{\\left(\\theta \\right)} & - v \\sin{\\left(\\theta \\right)}\\\\0 & 0 & \\sin{\\left(\\theta \\right)} & v \\cos{\\left(\\theta \\right)}\\\\0 & 0 & 0 & 0\\\\0 & 0 & \\frac{\\tan{\\left(\\delta \\right)}}{L} & 0\\end{matrix}\\right]$" - ], - "text/plain": [ - "Matrix([\n", - "[0, 0, cos(theta), -v*sin(theta)],\n", - "[0, 0, sin(theta), v*cos(theta)],\n", - "[0, 0, 0, 0],\n", - "[0, 0, tan(delta)/L, 0]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "x,y,theta,v,delta,L,a = sp.symbols(\"x y theta v delta L a\")\n", "\n", @@ -185,15 +167,20 @@ " [a],\n", " [ v*sp.tan(delta)/L]])\n", "\n", - "state = sp.Matrix([x,y,v,theta])\n", + "X = sp.Matrix([x,y,v,theta])\n", "\n", "#A\n", - "gs.jacobian(state)" + "A=gs.jacobian(X)\n", + "\n", + "U = sp.Matrix([a,delta])\n", + "\n", + "#B\n", + "B=gs.jacobian(U)#.subs({x:0,y:0,theta:0})B=" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -209,16 +196,96 @@ "[0, v*(tan(delta)**2 + 1)/L]])" ] }, - "execution_count": 7, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "state = sp.Matrix([a,delta])\n", - "\n", - "#B\n", - "gs.jacobian(state)#.subs({x:0,y:0,theta:0})" + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}1 & 0 & dt \\cos{\\left(\\theta \\right)} & - dt v \\sin{\\left(\\theta \\right)}\\\\0 & 1 & dt \\sin{\\left(\\theta \\right)} & dt v \\cos{\\left(\\theta \\right)}\\\\0 & 0 & 1 & 0\\\\0 & 0 & \\frac{dt \\tan{\\left(\\delta \\right)}}{L} & 1\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[1, 0, dt*cos(theta), -dt*v*sin(theta)],\n", + "[0, 1, dt*sin(theta), dt*v*cos(theta)],\n", + "[0, 0, 1, 0],\n", + "[0, 0, dt*tan(delta)/L, 1]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#A LIN\n", + "DT = sp.symbols(\"dt\")\n", + "sp.eye(4)+A*DT" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}0 & 0\\\\0 & 0\\\\dt & 0\\\\0 & \\frac{dt v \\left(\\tan^{2}{\\left(\\delta \\right)} + 1\\right)}{L}\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ 0, 0],\n", + "[ 0, 0],\n", + "[dt, 0],\n", + "[ 0, dt*v*(tan(delta)**2 + 1)/L]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B*DT" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}dt \\theta v \\sin{\\left(\\theta \\right)}\\\\- dt \\theta v \\cos{\\left(\\theta \\right)}\\\\0\\\\- \\frac{\\delta dt v \\left(\\tan^{2}{\\left(\\delta \\right)} + 1\\right)}{L}\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ dt*theta*v*sin(theta)],\n", + "[ -dt*theta*v*cos(theta)],\n", + "[ 0],\n", + "[-delta*dt*v*(tan(delta)**2 + 1)/L]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DT*(gs - A*X - B*U)" ] }, { @@ -422,6 +489,8 @@ "source": [ "It is necessary to take *actuation latency* into account: so instead of using the actual state as estimated, the delay factored in using the kinematic model\n", "\n", + "Starting State is :\n", + "\n", "* $x_{delay} = 0.0 + v * dt$\n", "* $y_{delay} = 0.0$\n", "* $psi_{delay} = 0.0 + w * dt$\n", diff --git a/racecar.py b/racecar.py deleted file mode 100644 index be71a2f..0000000 --- a/racecar.py +++ /dev/null @@ -1,52 +0,0 @@ -import pybullet as p -import pybullet_data - -import time -import os - -p.connect(p.GUI) - -p.resetSimulation() -p.setGravity(0, 0, -10) - -useRealTimeSim = 1 - -#for video recording (works best on Mac and Linux, not well on Windows) -#p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4, "racecar.mp4") -p.setRealTimeSimulation(useRealTimeSim) # either this -p.loadURDF(os.path.join(pybullet_data.getDataPath(), "plane.urdf")) - -car = p.loadURDF(os.path.join(pybullet_data.getDataPath(), "racecar/racecar.urdf")) -for i in range(p.getNumJoints(car)): - print(p.getJointInfo(car, i)) - -inactive_wheels = [3, 5, 7] -wheels = [2] - -for wheel in inactive_wheels: - p.setJointMotorControl2(car, wheel, p.VELOCITY_CONTROL, targetVelocity=0, force=0) - -steering = [4, 6] - -targetVelocitySlider = p.addUserDebugParameter("wheelVelocity", -10, 10, 0) -maxForceSlider = p.addUserDebugParameter("maxForce", 0, 10, 10) -steeringSlider = p.addUserDebugParameter("steering", -0.5, 0.5, 0) -while (True): - maxForce = p.readUserDebugParameter(maxForceSlider) - targetVelocity = p.readUserDebugParameter(targetVelocitySlider) - steeringAngle = p.readUserDebugParameter(steeringSlider) - - for wheel in wheels: - p.setJointMotorControl2(car, - wheel, - p.VELOCITY_CONTROL, - targetVelocity=targetVelocity, - force=maxForce) - - for steer in steering: - p.setJointMotorControl2(car, steer, p.POSITION_CONTROL, targetPosition=steeringAngle) - - if (useRealTimeSim == 0): - p.stepSimulation() - - time.sleep(0.01) diff --git a/racecar/checker_blue.png b/racecar/checker_blue.png new file mode 100644 index 0000000..0894b91 Binary files /dev/null and b/racecar/checker_blue.png differ diff --git a/racecar/f10_racecar/differential/diff_arm.stl b/racecar/f10_racecar/differential/diff_arm.stl new file mode 100644 index 0000000..8d33d77 Binary files /dev/null and b/racecar/f10_racecar/differential/diff_arm.stl differ diff --git a/racecar/f10_racecar/differential/diff_carrier.stl b/racecar/f10_racecar/differential/diff_carrier.stl new file mode 100644 index 0000000..2b9cd9c Binary files /dev/null and b/racecar/f10_racecar/differential/diff_carrier.stl differ diff --git a/racecar/f10_racecar/differential/diff_carrier_cover.stl b/racecar/f10_racecar/differential/diff_carrier_cover.stl new file mode 100644 index 0000000..8afd680 Binary files /dev/null and b/racecar/f10_racecar/differential/diff_carrier_cover.stl differ diff --git a/racecar/f10_racecar/differential/diff_leftshaft.stl b/racecar/f10_racecar/differential/diff_leftshaft.stl new file mode 100644 index 0000000..f44b120 Binary files /dev/null and b/racecar/f10_racecar/differential/diff_leftshaft.stl differ diff --git a/racecar/f10_racecar/differential/diff_motor_cover.stl b/racecar/f10_racecar/differential/diff_motor_cover.stl new file mode 100644 index 0000000..a664860 Binary files /dev/null and b/racecar/f10_racecar/differential/diff_motor_cover.stl differ diff --git a/racecar/f10_racecar/differential/diff_pinion.stl b/racecar/f10_racecar/differential/diff_pinion.stl new file mode 100644 index 0000000..7e96387 Binary files /dev/null and b/racecar/f10_racecar/differential/diff_pinion.stl differ diff --git a/racecar/f10_racecar/differential/diff_rightshaft.stl b/racecar/f10_racecar/differential/diff_rightshaft.stl new file mode 100644 index 0000000..db40249 Binary files /dev/null and b/racecar/f10_racecar/differential/diff_rightshaft.stl differ diff --git a/racecar/f10_racecar/differential/diff_ring.stl b/racecar/f10_racecar/differential/diff_ring.stl new file mode 100644 index 0000000..7ca2a6c Binary files /dev/null and b/racecar/f10_racecar/differential/diff_ring.stl differ diff --git a/racecar/f10_racecar/differential/diff_ring.urdf b/racecar/f10_racecar/differential/diff_ring.urdf new file mode 100644 index 0000000..3e20aeb --- /dev/null +++ b/racecar/f10_racecar/differential/diff_ring.urdf @@ -0,0 +1,198 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/racecar/f10_racecar/differential/diff_side.stl b/racecar/f10_racecar/differential/diff_side.stl new file mode 100644 index 0000000..f8ed10e Binary files /dev/null and b/racecar/f10_racecar/differential/diff_side.stl differ diff --git a/racecar/f10_racecar/differential/diff_spider.stl b/racecar/f10_racecar/differential/diff_spider.stl new file mode 100644 index 0000000..1c15240 Binary files /dev/null and b/racecar/f10_racecar/differential/diff_spider.stl differ diff --git a/racecar/f10_racecar/differential/diff_spider_shaft.stl b/racecar/f10_racecar/differential/diff_spider_shaft.stl new file mode 100644 index 0000000..12b2213 Binary files /dev/null and b/racecar/f10_racecar/differential/diff_spider_shaft.stl differ diff --git a/racecar/f10_racecar/differential/diff_stand.stl b/racecar/f10_racecar/differential/diff_stand.stl new file mode 100644 index 0000000..168bdb1 Binary files /dev/null and b/racecar/f10_racecar/differential/diff_stand.stl differ diff --git a/racecar/f10_racecar/differential/modelorigin.txt b/racecar/f10_racecar/differential/modelorigin.txt new file mode 100644 index 0000000..af250e7 --- /dev/null +++ b/racecar/f10_racecar/differential/modelorigin.txt @@ -0,0 +1,2 @@ +stl files were copied from http://www.otvinta.com/download09.html +URDF file was manually created, along with mimicJointConstraint.py \ No newline at end of file diff --git a/racecar/f10_racecar/meshes/barca_track.mtl b/racecar/f10_racecar/meshes/barca_track.mtl new file mode 100644 index 0000000..e16a0fe --- /dev/null +++ b/racecar/f10_racecar/meshes/barca_track.mtl @@ -0,0 +1,65 @@ +# Blender MTL File: 'None' +# Material Count: 7 + +newmtl Grass +Ns 1.960784 +Ka 0.000000 0.000000 0.000000 +Kd 0.034510 0.131765 0.028235 +Ks 0.000000 0.000000 0.000000 +Ni -1.000000 +d 1.000000 +illum 2 + +newmtl Road +Ns 1.960784 +Ka 0.000000 0.000000 0.000000 +Kd 0.172549 0.172549 0.172549 +Ks 0.000000 0.000000 0.000000 +Ni -1.000000 +d 1.000000 +illum 2 + +newmtl Text +Ns 1.960784 +Ka 0.000000 0.000000 0.000000 +Kd 0.608627 0.445490 0.163138 +Ks 0.000000 0.000000 0.000000 +Ni -1.000000 +d 1.000000 +illum 2 + +newmtl Wall-top +Ns 1.960784 +Ka 0.000000 0.000000 0.000000 +Kd 0.040784 0.087843 0.235294 +Ks 0.000000 0.000000 0.000000 +Ni -1.000000 +d 1.000000 +illum 2 + +newmtl Walls +Ns 1.960784 +Ka 0.000000 0.000000 0.000000 +Kd 0.288627 0.649412 0.652549 +Ks 0.000000 0.000000 0.000000 +Ni -1.000000 +d 1.000000 +illum 2 + +newmtl Water +Ns 1.960784 +Ka 0.000000 0.000000 0.000000 +Kd 0.131765 0.250981 0.495686 +Ks 0.000000 0.000000 0.000000 +Ni -1.000000 +d 1.000000 +illum 2 + +newmtl water-wall +Ns 1.960784 +Ka 0.000000 0.000000 0.000000 +Kd 0.564706 0.125490 0.125490 +Ks 0.000000 0.000000 0.000000 +Ni -1.000000 +d 1.000000 +illum 2 diff --git a/racecar/f10_racecar/meshes/barca_track.obj b/racecar/f10_racecar/meshes/barca_track.obj new file mode 100644 index 0000000..550d217 --- /dev/null +++ b/racecar/f10_racecar/meshes/barca_track.obj @@ -0,0 +1,54915 @@ +# Blender v2.71 (sub 0) OBJ File: '' +# www.blender.org +mtllib barca_track.mtl +o Line004_Line004Mesh +v 6.247754 3.775198 0.000000 +v 7.205369 3.815950 0.000000 +v 8.245951 3.717267 0.000000 +v 9.356565 3.494656 0.000000 +v 10.524273 3.163628 0.000000 +v 11.736139 2.739689 0.000000 +v 12.979229 2.238349 0.000000 +v 14.349886 1.595434 0.000000 +v 15.891727 0.775766 0.000000 +v 17.512718 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/racecar/plane.mtl b/racecar/plane.mtl new file mode 100644 index 0000000..cd10152 --- /dev/null +++ b/racecar/plane.mtl @@ -0,0 +1,15 @@ +newmtl Material + Ns 10.0000 + Ni 1.5000 + d 1.0000 + Tr 0.0000 + Tf 1.0000 1.0000 1.0000 + illum 2 + Ka 0.0000 0.0000 0.0000 + Kd 0.5880 0.5880 0.5880 + Ks 0.0000 0.0000 0.0000 + Ke 0.0000 0.0000 0.0000 + map_Ka cube.tga + map_Kd checker_blue.png + + diff --git a/racecar/plane.obj b/racecar/plane.obj new file mode 100644 index 0000000..6062095 --- /dev/null +++ b/racecar/plane.obj @@ -0,0 +1,18 @@ +# Blender v2.66 (sub 1) OBJ File: '' +# www.blender.org +mtllib plane.mtl +o Plane +v 15.000000 -15.000000 0.000000 +v 15.000000 15.000000 0.000000 +v -15.000000 15.000000 0.000000 +v -15.000000 -15.000000 0.000000 + +vt 15.000000 0.000000 +vt 15.000000 15.000000 +vt 0.000000 15.000000 +vt 0.000000 0.000000 + +usemtl Material +s off +f 1/1 2/2 3/3 +f 1/1 3/3 4/4 diff --git a/racecar/plane.urdf b/racecar/plane.urdf new file mode 100644 index 0000000..bb7ae10 --- /dev/null +++ b/racecar/plane.urdf @@ -0,0 +1,29 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/racecar/racecar.py b/racecar/racecar.py new file mode 100644 index 0000000..2b414b5 --- /dev/null +++ b/racecar/racecar.py @@ -0,0 +1,198 @@ +import pybullet as p +import time +import math +import os +import pybullet_data + +p.connect(p.GUI) + +p.resetSimulation() + +p.setGravity(0,0,-10) +useRealTimeSim = 0 + +p.setTimeStep(1./120.) +p.setRealTimeSimulation(useRealTimeSim) # either this + +track = p.loadURDF("plane.urdf") +#track = p.loadSDF("f10_racecar/meshes/barca_track.sdf", globalScaling=1) +#otherCar = p.loadURDF("f10_racecar/racecar_differential.urdf", [0,1,.3]) +car = p.loadURDF("f10_racecar/racecar_differential.urdf", [0,0,.3]) + + + + +for wheel in range(p.getNumJoints(car)): + print("joint[",wheel,"]=", p.getJointInfo(car,wheel)) + p.setJointMotorControl2(car,wheel,p.VELOCITY_CONTROL,targetVelocity=0,force=0) + p.getJointInfo(car,wheel) + +wheels = [8,15] +print("----------------") + +#p.setJointMotorControl2(car,10,p.VELOCITY_CONTROL,targetVelocity=1,force=10) +c = p.createConstraint(car,9,car,11,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=1, maxForce=10000) + +c = p.createConstraint(car,10,car,13,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=-1, maxForce=10000) + +c = p.createConstraint(car,9,car,13,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=-1, maxForce=10000) + +c = p.createConstraint(car,16,car,18,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=1, maxForce=10000) + + +c = p.createConstraint(car,16,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=-1, maxForce=10000) + +c = p.createConstraint(car,17,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=-1, maxForce=10000) + +c = p.createConstraint(car,1,car,18,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15, maxForce=10000) +c = p.createConstraint(car,3,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0]) +p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15,maxForce=10000) + + +steering = [0,2] + +hokuyo_joint=4 +zed_camera_joint = 5 + +targetVelocitySlider = p.addUserDebugParameter("wheelVelocity",-2,2,0) +maxForceSlider = p.addUserDebugParameter("maxForce",0,50,20) +steeringSlider = p.addUserDebugParameter("steering",-1,1,0) + +# replaceLines=True +# numRays=100 +# rayFrom=[] +# rayTo=[] +# rayIds=[] +# rayHitColor = [1,0,0] +# rayMissColor = [0,1,0] +# rayLen = 8 +# rayStartLen=0.25 +# for i in range (numRays): +# #rayFrom.append([0,0,0]) +# rayFrom.append([rayStartLen*math.sin(-0.5*0.25*2.*math.pi+0.75*2.*math.pi*float(i)/numRays), rayStartLen*math.cos(-0.5*0.25*2.*math.pi+0.75*2.*math.pi*float(i)/numRays),0]) +# rayTo.append([rayLen*math.sin(-0.5*0.25*2.*math.pi+0.75*2.*math.pi*float(i)/numRays), rayLen*math.cos(-0.5*0.25*2.*math.pi+0.75*2.*math.pi*float(i)/numRays),0]) +# if (replaceLines): +# rayIds.append(p.addUserDebugLine(rayFrom[i], rayTo[i], rayMissColor,parentObjectUniqueId=car, parentLinkIndex=hokuyo_joint )) +# else: +# rayIds.append(-1) + + + +#def getCarYaw(car): +# carPos,carOrn = p.getBasePositionAndOrientation(car) +# carEuler = p.getEulerFromQuaternion(carOrn) +# carYaw = carEuler[2]*360/(2.*math.pi)-90 +# return carYaw + +#prevCarYaw = getCarYaw(car) +frame = 0 + +lineId = p.addUserDebugLine([0,0,0],[0,0,1],[1,0,0]) +lineId2 = p.addUserDebugLine([0,0,0],[0,0,1],[1,0,0]) +lineId3= p.addUserDebugLine([0,0,0],[0,0,1],[1,0,0]) +print("lineId=",lineId) + +#camInfo = p.getDebugVisualizerCamera() +lastTime = time.time() +lastControlTime = time.time() +#lastLidarTime = time.time() + +def getCarVel(car): + linVel,angVel = p.getBaseVelocity(car) + return linVel[0] + +#frame=0 +while (True): + #print (getCarVel(car)) + nowTime = time.time() + #render Camera at 10Hertz + # if (nowTime-lastTime>.1): + # ls = p.getLinkState(car,zed_camera_joint, computeForwardKinematics=True) + # camPos = ls[0] + # camOrn = ls[1] + # camMat = p.getMatrixFromQuaternion(camOrn) + # upVector = [0,0,1] + # forwardVec = [camMat[0],camMat[3],camMat[6]] + # #sideVec = [camMat[1],camMat[4],camMat[7]] + # camUpVec = [camMat[2],camMat[5],camMat[8]] + # camTarget = [camPos[0]+forwardVec[0]*10,camPos[1]+forwardVec[1]*10,camPos[2]+forwardVec[2]*10] + # camUpTarget = [camPos[0]+camUpVec[0],camPos[1]+camUpVec[1],camPos[2]+camUpVec[2]] + # viewMat = p.computeViewMatrix(camPos, camTarget, camUpVec) + # projMat = camInfo[3] + # #p.getCameraImage(320,200,viewMatrix=viewMat,projectionMatrix=projMat, flags=p.ER_NO_SEGMENTATION_MASK, renderer=p.ER_BULLET_HARDWARE_OPENGL) + # p.getCameraImage(320,200,viewMatrix=viewMat,projectionMatrix=projMat, renderer=p.ER_BULLET_HARDWARE_OPENGL) + # lastTime=nowTime + + nowControlTime = time.time() + + nowLidarTime = time.time() + #lidar at 20Hz + # if (nowLidarTime-lastLidarTime>.3): + # #print("Lidar!") + # numThreads=0 + # results = p.rayTestBatch(rayFrom,rayTo,numThreads, parentObjectUniqueId=car, parentLinkIndex=hokuyo_joint) + # for i in range (numRays): + # hitObjectUid=results[i][0] + # hitFraction = results[i][2] + # hitPosition = results[i][3] + # if (hitFraction==1.): + # p.addUserDebugLine(rayFrom[i],rayTo[i], rayMissColor,replaceItemUniqueId=rayIds[i],parentObjectUniqueId=car, parentLinkIndex=hokuyo_joint) + # else: + # localHitTo = [rayFrom[i][0]+hitFraction*(rayTo[i][0]-rayFrom[i][0]), + # rayFrom[i][1]+hitFraction*(rayTo[i][1]-rayFrom[i][1]), + # rayFrom[i][2]+hitFraction*(rayTo[i][2]-rayFrom[i][2])] + # p.addUserDebugLine(rayFrom[i],localHitTo, rayHitColor,replaceItemUniqueId=rayIds[i],parentObjectUniqueId=car, parentLinkIndex=hokuyo_joint) + # lastLidarTime = nowLidarTime + + #control at 100Hz + if (nowControlTime-lastControlTime>.01): + carPos,carOrn = p.getBasePositionAndOrientation(car) + + # Keep the previous orientation of the camera set by the user. + + # yaw = camInfo[8] + # pitch = camInfo[9] + # distance = camInfo[10] + # targetPos = camInfo[11] + # camFwd = camInfo[5] + # carYaw = getCarYaw(car) + # + # #the car yaw is clamped between -90 and 270, make sure to deal with angles that wrap around + # if (carYaw-prevCarYaw>45): + # yaw+=360 + # if (carYaw-prevCarYaw<-45): + # yaw-=360 + # prevCarYaw = carYaw + + #print("carYaw=", carYaw) + #print("camYaw=", yaw) + + #slowly rotate the camera behind the car + #diffYaw = (carYaw-yaw)*0.03 + + #track the position of the car as target + #p.resetDebugVisualizerCamera(distance, yaw+diffYaw, pitch, carPos) + + maxForce = p.readUserDebugParameter(maxForceSlider) + targetVelocity = p.readUserDebugParameter(targetVelocitySlider) + steeringAngle = p.readUserDebugParameter(steeringSlider) + #print(targetVelocity) + gearRatio=1./21 + + for wheel in wheels: + p.setJointMotorControl2(car,wheel,p.VELOCITY_CONTROL,targetVelocity=targetVelocity/gearRatio,force=maxForce) + + for steer in steering: + p.setJointMotorControl2(car,steer,p.POSITION_CONTROL,targetPosition=steeringAngle) + + if (useRealTimeSim==0): + frame+=1 + p.stepSimulation() + lastControlTime=nowControlTime