====== Lab07 - Asymptotically Optimal Randomized Sampling-based Path Planning ======
^ Motivations and Goals ^
| Become familiar with advanced methods of randomized sampling-based motion planning |
| Be able to plan the path using the framework of the [[courses:b4m36uir:tutorials:ompl_tut|Open Motion Planning Library]] |
^ Tasks ([[courses:b4m36uir:internal:instructions:lab07|teacher]]) ^
| Implement the RRT-based planning approach using the OMPL framework |
^ Lab resources ^
| {{ :courses:b4m36uir:labs:ompl_example.zip | OMPL example instance}}|
===== Open Motion Planning Library =====
The [[http://ompl.kavrakilab.org/index.html|Open Motion Planning Library]] contains implementations of many [[http://ompl.kavrakilab.org/planners.html|sampling-based algorithms]] such as PRM, RRT, EST, SBL, KPIECE, SyCLOP, and several variants of these.
Details on the installation of the OMPL library are at [[courses:b4m36uir:tutorials:ompl_tut|Open Motion Planning Library]] tutorial.
Below is a simple annotated example of motion planning in continuous space:
#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
from ompl import base as ob
from ompl import geometric as og
def plot_points(points, specs='r'):
"""
method to plot the points
Parameters
----------
points: list(float, float)
list of the pint coordinates
"""
x_val = [x[0] for x in points]
y_val = [x[1] for x in points]
plt.plot(x_val, y_val, specs)
plt.draw()
def isStateValid(state):
"""
method for collision detection checking
Parameters
----------
state : list(float)
configuration to be checked for collision
Returns
-------
bool
False if there is collision, True otherwise
"""
return (state[0] >= state[1])
def plan(start, goal):
#set dimensions of the problem to be solved
stateSpace = ob.RealVectorStateSpace() #set state space
stateSpace.addDimension(0.0, 10.0) #set width
stateSpace.addDimension(0.0, 10.0) #set height
#create a simple setup object
task = og.SimpleSetup(stateSpace)
#set methods for collision detection and resolution of the checking
task.setStateValidityChecker(ob.StateValidityCheckerFn(isStateValid))
task.getSpaceInformation().setStateValidityCheckingResolution(0.001)
#setting start and goal positions
start_pose = ob.State(task.getStateSpace())
goal_pose = ob.State(task.getStateSpace())
start_pose()[0] = start[0]
start_pose()[1] = start[1]
goal_pose()[0] = goal[0]
goal_pose()[1] = goal[1]
task.setStartAndGoalStates(start_pose, goal_pose)
#setting particular planner from the supported planners
info = task.getSpaceInformation()
planner = og.RRT(info) # RRT, RRTConnect, FMT, ...
task.setPlanner(planner)
#find the solution
solution = task.solve()
#simplify the solution
if solution:
task.simplifySolution()
#retrieve found path
plan = task.getSolutionPath()
#extract path and plot it
path = []
for i in range(plan.getStateCount()):
path.append((plan.getState(i)[0], plan.getState(i)[1]))
plot_points(path,'r')
plt.show()
if __name__ == "__main__":
plan([0, 0], [10, 10])
Besides the setup of the planner, the programmer needs to deliver the validity checking function. In the problem of robotic motion planning this function usually consists of collision checking which can be based on different approaches.\\
Further information of the planner can be obtained using access through PlannerData object as demonstrated below.
#retrieve planner data
plannerdata = ob.PlannerData(info)
planner.getPlannerData(plannerdata)
#getting vertices
nv = plannerdata.numVertices()
g = []
for i in range(0, nv):
vert = plannerdata.getVertex(i)
g.append((vert.getState()[0], vert.getState()[1]))
#getting edges
e = []
for i in range(0, nv):
for j in range(0, nv):
if plannerdata.edgeExists(i, j):
e.append((g[i], g[j]))
#show edges
plot_edges(e)
plt.show()
def plot_edges(edges):
"""
method to plot the edges of the randomized sampling-based path planning approach graph
Parameters
----------
edges: list((float,float),(float,float))
list of edges (coordinate1, coordinate2)
"""
ed = np.asarray(edges)
for edge in ed:
plt.plot([edge[0,0], edge[1,0]],[edge[0,1], edge[1,1]],'g',linewidth=0.6)
plt.draw()
===== Motion planning benchmarking =====
A set of [[https://parasol.tamu.edu/dsmft/benchmarks/|motion planning benchmarking examples]] available online.