219 lines
6.6 KiB
Python
Executable File
219 lines
6.6 KiB
Python
Executable File
#! /usr/bin/env python
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib import animation
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from utils import compute_path_from_wp
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from cvxpy_mpc import optimize
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import sys
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import time
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# Robot Starting position
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SIM_START_X=0.
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SIM_START_Y=0.5
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SIM_START_V=0.
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SIM_START_H=0.
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L=0.3
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from mpc_config import Params
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P=Params()
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# Classes
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class MPC():
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def __init__(self):
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# State for the robot mathematical model [x,y,heading]
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self.state = [SIM_START_X, SIM_START_Y, SIM_START_V, SIM_START_H]
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self.opt_u = np.zeros((P.M,P.T))
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self.opt_u[0,:] = 0.5 #m/ss
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self.opt_u[1,:] = np.radians(0) #rad/s
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# Interpolated Path to follow given waypoints
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#self.path = compute_path_from_wp([0,10,12,2,4,14],[0,0,2,10,12,12])
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self.path = compute_path_from_wp([0,3,4,6,10,12,13,13,6,1,0],
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[0,0,2,4,3,3,-1,-2,-6,-2,-2],P.path_tick)
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# Sim help vars
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self.sim_time=0
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self.x_history=[]
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self.y_history=[]
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self.v_history=[]
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self.h_history=[]
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self.a_history=[]
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self.d_history=[]
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self.predicted=None
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#Initialise plot
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plt.style.use("ggplot")
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self.fig = plt.figure()
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plt.ion()
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plt.show()
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def predict_motion(self):
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'''
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'''
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predicted=np.zeros(self.opt_u.shape)
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x=self.state[0]
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y=self.state[1]
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v=self.state[2]
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th=self.state[3]
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for idx,a,delta in zip(range(len(self.opt_u[0,:])),self.opt_u[0,:],self.opt_u[1,:]):
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x = x+v*np.cos(th)*P.dt
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y = y+v*np.sin(th)*P.dt
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v = v+a*P.dt
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th = th + v*np.tan(delta)/L*P.dt
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predicted[0,idx]=x
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predicted[1,idx]=y
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self.predicted = predicted
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def run(self):
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'''
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'''
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self.plot_sim()
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input("Press Enter to continue...")
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while 1:
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if self.state is not None:
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if np.sqrt((self.state[0]-self.path[0,-1])**2+(self.state[1]-self.path[1,-1])**2)<0.1:
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print("Success! Goal Reached")
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input("Press Enter to continue...")
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return
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#optimization loop
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start=time.time()
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self.opt_u = optimize(self.state,
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self.opt_u,
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self.path)
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# print("CVXPY Optimization Time: {:.4f}s".format(time.time()-start))
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self.update_sim(self.opt_u[0,1],self.opt_u[1,1])
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self.predict_motion()
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self.plot_sim()
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def update_sim(self,acc,steer):
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'''
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Updates state.
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:param lin_v: float
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:param ang_v: float
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'''
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self.state[0] = self.state[0] +self.state[2]*np.cos(self.state[3])*P.dt
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self.state[1] = self.state[1] +self.state[2]*np.sin(self.state[3])*P.dt
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self.state[2] = self.state[2] +acc*P.dt
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self.state[3] = self.state[3] + self.state[2]*np.tan(steer)/L*P.dt
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def plot_sim(self):
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self.sim_time = self.sim_time+P.dt
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self.x_history.append(self.state[0])
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self.y_history.append(self.state[1])
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self.v_history.append(self.state[2])
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self.h_history.append(self.state[3])
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self.a_history.append(self.opt_u[0,1])
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self.d_history.append(self.opt_u[1,1])
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plt.clf()
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grid = plt.GridSpec(2, 3)
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plt.subplot(grid[0:2, 0:2])
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plt.title("MPC Simulation \n" + "Simulation elapsed time {}s".format(self.sim_time))
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plt.plot(self.path[0,:],self.path[1,:], c='tab:orange',
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marker=".",
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label="reference track")
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plt.plot(self.x_history, self.y_history, c='tab:blue',
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marker=".",
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alpha=0.5,
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label="vehicle trajectory")
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if self.predicted is not None:
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plt.plot(self.predicted[0,:], self.predicted[1,:], c='tab:green',
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marker="+",
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alpha=0.5,
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label="mpc opt trajectory")
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# plt.plot(self.x_history[-1], self.y_history[-1], c='tab:blue',
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# marker=".",
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# markersize=12,
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# label="vehicle position")
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# plt.arrow(self.x_history[-1],
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# self.y_history[-1],
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# np.cos(self.h_history[-1]),
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# np.sin(self.h_history[-1]),
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# color='tab:blue',
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# width=0.2,
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# head_length=0.5,
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# label="heading")
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plot_car(self.x_history[-1], self.y_history[-1], self.h_history[-1])
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plt.ylabel('map y')
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plt.yticks(np.arange(min(self.path[1,:])-1., max(self.path[1,:]+1.)+1, 1.0))
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plt.xlabel('map x')
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plt.xticks(np.arange(min(self.path[0,:])-1., max(self.path[0,:]+1.)+1, 1.0))
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plt.axis("equal")
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#plt.legend()
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plt.subplot(grid[0, 2])
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#plt.title("Linear Velocity {} m/s".format(self.v_history[-1]))
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plt.plot(self.a_history,c='tab:orange')
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locs, _ = plt.xticks()
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plt.xticks(locs[1:], locs[1:]*P.dt)
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plt.ylabel('a(t) [m/ss]')
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plt.xlabel('t [s]')
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plt.subplot(grid[1, 2])
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#plt.title("Angular Velocity {} m/s".format(self.w_history[-1]))
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plt.plot(np.degrees(self.d_history),c='tab:orange')
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plt.ylabel('gamma(t) [deg]')
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locs, _ = plt.xticks()
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plt.xticks(locs[1:], locs[1:]*P.dt)
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plt.xlabel('t [s]')
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plt.tight_layout()
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plt.draw()
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plt.pause(0.1)
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def plot_car(x, y, yaw):
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LENGTH = 0.5 # [m]
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WIDTH = 0.25 # [m]
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OFFSET = LENGTH # [m]
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outline = np.array([[-OFFSET, (LENGTH - OFFSET), (LENGTH - OFFSET), -OFFSET, -OFFSET],
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[WIDTH / 2, WIDTH / 2, - WIDTH / 2, -WIDTH / 2, WIDTH / 2]])
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Rotm = np.array([[np.cos(yaw), np.sin(yaw)],
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[-np.sin(yaw), np.cos(yaw)]])
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outline = (outline.T.dot(Rotm)).T
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outline[0, :] += x
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outline[1, :] += y
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plt.plot(np.array(outline[0, :]).flatten(), np.array(outline[1, :]).flatten(), 'tab:blue')
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def do_sim():
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sim=MPC()
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try:
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sim.run()
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except Exception as e:
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sys.exit(e)
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if __name__ == '__main__':
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do_sim()
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