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<div class="title">StateSampling.py</div> </div>
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<div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 <span class="comment">#!/usr/bin/env python</span>
<a name="l00002"></a>00002
<a name="l00003"></a>00003 <span class="comment">######################################################################</span>
<a name="l00004"></a>00004 <span class="comment"># Software License Agreement (BSD License)</span>
<a name="l00005"></a>00005 <span class="comment">#</span>
<a name="l00006"></a>00006 <span class="comment"># Copyright (c) 2010, Rice University</span>
<a name="l00007"></a>00007 <span class="comment"># All rights reserved.</span>
<a name="l00008"></a>00008 <span class="comment">#</span>
<a name="l00009"></a>00009 <span class="comment"># Redistribution and use in source and binary forms, with or without</span>
<a name="l00010"></a>00010 <span class="comment"># modification, are permitted provided that the following conditions</span>
<a name="l00011"></a>00011 <span class="comment"># are met:</span>
<a name="l00012"></a>00012 <span class="comment">#</span>
<a name="l00013"></a>00013 <span class="comment"># * Redistributions of source code must retain the above copyright</span>
<a name="l00014"></a>00014 <span class="comment"># notice, this list of conditions and the following disclaimer.</span>
<a name="l00015"></a>00015 <span class="comment"># * Redistributions in binary form must reproduce the above</span>
<a name="l00016"></a>00016 <span class="comment"># copyright notice, this list of conditions and the following</span>
<a name="l00017"></a>00017 <span class="comment"># disclaimer in the documentation and/or other materials provided</span>
<a name="l00018"></a>00018 <span class="comment"># with the distribution.</span>
<a name="l00019"></a>00019 <span class="comment"># * Neither the name of the Rice University nor the names of its</span>
<a name="l00020"></a>00020 <span class="comment"># contributors may be used to endorse or promote products derived</span>
<a name="l00021"></a>00021 <span class="comment"># from this software without specific prior written permission.</span>
<a name="l00022"></a>00022 <span class="comment">#</span>
<a name="l00023"></a>00023 <span class="comment"># THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS</span>
<a name="l00024"></a>00024 <span class="comment"># "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT</span>
<a name="l00025"></a>00025 <span class="comment"># LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS</span>
<a name="l00026"></a>00026 <span class="comment"># FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE</span>
<a name="l00027"></a>00027 <span class="comment"># COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,</span>
<a name="l00028"></a>00028 <span class="comment"># INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,</span>
<a name="l00029"></a>00029 <span class="comment"># BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;</span>
<a name="l00030"></a>00030 <span class="comment"># LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER</span>
<a name="l00031"></a>00031 <span class="comment"># CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT</span>
<a name="l00032"></a>00032 <span class="comment"># LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN</span>
<a name="l00033"></a>00033 <span class="comment"># ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE</span>
<a name="l00034"></a>00034 <span class="comment"># POSSIBILITY OF SUCH DAMAGE.</span>
<a name="l00035"></a>00035 <span class="comment">######################################################################</span>
<a name="l00036"></a>00036
<a name="l00037"></a>00037 <span class="comment"># Author: Mark Moll</span>
<a name="l00038"></a>00038
<a name="l00039"></a>00039 <span class="keywordflow">try</span>:
<a name="l00040"></a>00040 <span class="keyword">from</span> ompl <span class="keyword">import</span> base <span class="keyword">as</span> ob
<a name="l00041"></a>00041 <span class="keyword">from</span> ompl <span class="keyword">import</span> geometric <span class="keyword">as</span> og
<a name="l00042"></a>00042 <span class="keywordflow">except</span>:
<a name="l00043"></a>00043 <span class="comment"># if the ompl module is not in the PYTHONPATH assume it is installed in a</span>
<a name="l00044"></a>00044 <span class="comment"># subdirectory of the parent directory called "py-bindings."</span>
<a name="l00045"></a>00045 <span class="keyword">from</span> os.path <span class="keyword">import</span> basename, abspath, dirname, join
<a name="l00046"></a>00046 <span class="keyword">import</span> sys
<a name="l00047"></a>00047 sys.path.insert(0, join(dirname(dirname(abspath(__file__))),<span class="stringliteral">'py-bindings'</span>))
<a name="l00048"></a>00048 <span class="keyword">from</span> ompl <span class="keyword">import</span> base <span class="keyword">as</span> ob
<a name="l00049"></a>00049 <span class="keyword">from</span> ompl <span class="keyword">import</span> geometric <span class="keyword">as</span> og
<a name="l00050"></a>00050 <span class="keyword">from</span> time <span class="keyword">import</span> sleep
<a name="l00051"></a>00051 <span class="keyword">from</span> math <span class="keyword">import</span> fabs
<a name="l00052"></a>00052
<a name="l00053"></a>00053 <span class="comment">## @cond IGNORE</span>
<a name="l00054"></a>00054
<a name="l00055"></a>00055 <span class="comment"># This is a problem-specific sampler that automatically generates valid</span>
<a name="l00056"></a>00056 <span class="comment"># states; it doesn't need to call SpaceInformation::isValid. This is an</span>
<a name="l00057"></a>00057 <span class="comment"># example of constrained sampling. If you can explicitly describe the set valid</span>
<a name="l00058"></a>00058 <span class="comment"># states and can draw samples from it, then this is typically much more</span>
<a name="l00059"></a>00059 <span class="comment"># efficient than generating random samples from the entire state space and</span>
<a name="l00060"></a>00060 <span class="comment"># checking for validity.</span>
<a name="l00061"></a>00061 <span class="keyword">class </span>MyValidStateSampler(ob.ValidStateSampler):
<a name="l00062"></a>00062 <span class="keyword">def </span>__init__(si):
<a name="l00063"></a>00063 super(MyValidStateSampler, self).__init__(si)
<a name="l00064"></a>00064 self.name_ = <span class="stringliteral">"my sampler"</span>
<a name="l00065"></a>00065 self.rng_ = RNG()
<a name="l00066"></a>00066
<a name="l00067"></a>00067 <span class="comment"># Generate a sample in the valid part of the R^3 state space.</span>
<a name="l00068"></a>00068 <span class="comment"># Valid states satisfy the following constraints: </span>
<a name="l00069"></a>00069 <span class="comment"># -1<= x,y,z <=1</span>
<a name="l00070"></a>00070 <span class="comment"># if .25 <= z <= .5, then |x|>.8 and |y|>.8</span>
<a name="l00071"></a>00071 <span class="keyword">def </span>sample(state):
<a name="l00072"></a>00072 z = self.rng_.uniformReal(-1,1)
<a name="l00073"></a>00073
<a name="l00074"></a>00074 <span class="keywordflow">if</span> z>.25 <span class="keywordflow">and</span> z<.5:
<a name="l00075"></a>00075 x = self.rng_.uniformReal(0,1.8)
<a name="l00076"></a>00076 y = self.rng_.uniformReal(0,.2)
<a name="l00077"></a>00077 i = self.rng_.uniformInt(0,3)
<a name="l00078"></a>00078 <span class="keywordflow">if</span> i==0:
<a name="l00079"></a>00079 state[0]=x-1
<a name="l00080"></a>00080 state[1]=y-1
<a name="l00081"></a>00081 <span class="keywordflow">elif</span> i==1:
<a name="l00082"></a>00082 state[0]=x-.8
<a name="l00083"></a>00083 state[1]=y+.8
<a name="l00084"></a>00084 <span class="keywordflow">elif</span> i==2:
<a name="l00085"></a>00085 state[0]=y-1
<a name="l00086"></a>00086 state[1]=x-1
<a name="l00087"></a>00087 <span class="keywordflow">elif</span> i==3:
<a name="l00088"></a>00088 state[0]=y+.8
<a name="l00089"></a>00089 state[1]=x-.8
<a name="l00090"></a>00090 <span class="keywordflow">else</span>:
<a name="l00091"></a>00091 state[0] = self.rng_.uniformReal(-1,1)
<a name="l00092"></a>00092 state[1] = self.rng_.uniformReal(-1,1)
<a name="l00093"></a>00093 state[2] = z
<a name="l00094"></a>00094 <span class="keywordflow">return</span> <span class="keyword">True</span>
<a name="l00095"></a>00095
<a name="l00096"></a>00096 <span class="comment">## @endcond</span>
<a name="l00097"></a>00097
<a name="l00098"></a>00098 <span class="comment"># This function is needed, even when we can write a sampler like the one</span>
<a name="l00099"></a>00099 <span class="comment"># above, because we need to check path segments for validity</span>
<a name="l00100"></a>00100 <span class="keyword">def </span>isStateValid(spaceInformation, state):
<a name="l00101"></a>00101 <span class="comment"># Let's pretend that the validity check is computationally relatively</span>
<a name="l00102"></a>00102 <span class="comment"># expensive to emphasize the benefit of explicitly generating valid</span>
<a name="l00103"></a>00103 <span class="comment"># samples</span>
<a name="l00104"></a>00104 sleep(.001)
<a name="l00105"></a>00105 <span class="comment"># Valid states satisfy the following constraints: </span>
<a name="l00106"></a>00106 <span class="comment"># -1<= x,y,z <=1</span>
<a name="l00107"></a>00107 <span class="comment"># if .25 <= z <= .5, then |x|>.8 and |y|>.8</span>
<a name="l00108"></a>00108 <span class="keywordflow">return</span> <span class="keywordflow">not</span> (fabs(state[0]<.8) <span class="keywordflow">and</span> fabs(state[1]<.8) <span class="keywordflow">and</span>
<a name="l00109"></a>00109 state[2]>.25 <span class="keywordflow">and</span> state[2]<.5)
<a name="l00110"></a>00110
<a name="l00111"></a>00111 <span class="comment"># return an obstacle-based sampler</span>
<a name="l00112"></a>00112 <span class="keyword">def </span>allocOBValidStateSampler(si):
<a name="l00113"></a>00113 <span class="comment"># we can perform any additional setup / configuration of a sampler here, </span>
<a name="l00114"></a>00114 <span class="comment"># but there is nothing to tweak in case of the ObstacleBasedValidStateSampler.</span>
<a name="l00115"></a>00115 <span class="keywordflow">return</span> ob.ValidStateSamplerPtr(ob.ObstacleBasedValidStateSampler(si))
<a name="l00116"></a>00116
<a name="l00117"></a>00117 <span class="comment"># return an instance of my sampler</span>
<a name="l00118"></a>00118 <span class="keyword">def </span>allocMyValidStateSampler(si):
<a name="l00119"></a>00119 <span class="keywordflow">return</span> ob.ValidStateSamplerPtr(MyValidStateSampler(si))
<a name="l00120"></a>00120
<a name="l00121"></a>00121 <span class="keyword">def </span>plan(samplerIndex):
<a name="l00122"></a>00122 <span class="comment"># construct the state space we are planning in</span>
<a name="l00123"></a>00123 space = ob.RealVectorStateSpace(3)
<a name="l00124"></a>00124
<a name="l00125"></a>00125 <span class="comment"># set the bounds</span>
<a name="l00126"></a>00126 bounds = ob.RealVectorBounds(3)
<a name="l00127"></a>00127 bounds.setLow(-1)
<a name="l00128"></a>00128 bounds.setHigh(1)
<a name="l00129"></a>00129 space.setBounds(bounds)
<a name="l00130"></a>00130
<a name="l00131"></a>00131 <span class="comment"># define a simple setup class</span>
<a name="l00132"></a>00132 ss = og.SimpleSetup(space)
<a name="l00133"></a>00133
<a name="l00134"></a>00134 <span class="comment"># set state validity checking for this space</span>
<a name="l00135"></a>00135 ss.setStateValidityChecker(isStateValid)
<a name="l00136"></a>00136
<a name="l00137"></a>00137 <span class="comment"># create a start state</span>
<a name="l00138"></a>00138 start = ob.State(space)
<a name="l00139"></a>00139 start[0] = 0
<a name="l00140"></a>00140 start[1] = 0
<a name="l00141"></a>00141 start[2] = 0
<a name="l00142"></a>00142
<a name="l00143"></a>00143 <span class="comment"># create a goal state</span>
<a name="l00144"></a>00144 goal = ob.State(space)
<a name="l00145"></a>00145 goal[0] = 0
<a name="l00146"></a>00146 goal[1] = 0
<a name="l00147"></a>00147 goal[2] = 1
<a name="l00148"></a>00148
<a name="l00149"></a>00149 <span class="comment"># set the start and goal states;</span>
<a name="l00150"></a>00150 ss.setStartAndGoalStates(start, goal)
<a name="l00151"></a>00151
<a name="l00152"></a>00152 <span class="comment"># set sampler (optional; the default is uniform sampling)</span>
<a name="l00153"></a>00153 <span class="keywordflow">if</span> samplerIndex==1:
<a name="l00154"></a>00154 <span class="comment"># use obstacle-based sampling</span>
<a name="l00155"></a>00155 ss.getSpaceInformation().setValidStateSamplerAllocator(allocOBValidStateSampler)
<a name="l00156"></a>00156 <span class="keywordflow">elif</span> samplerIndex==2:
<a name="l00157"></a>00157 <span class="comment"># use my sampler</span>
<a name="l00158"></a>00158 ss.getSpaceInformation().setValidStateSamplerAllocator(allocMyValidStateSampler)
<a name="l00159"></a>00159
<a name="l00160"></a>00160 <span class="comment"># set the planner (optional)</span>
<a name="l00161"></a>00161 <span class="comment"># create a planner for the defined space</span>
<a name="l00162"></a>00162 planner = og.RRTConnect(ss.getSpaceInformation())
<a name="l00163"></a>00163 ss.setPlanner(planner)
<a name="l00164"></a>00164
<a name="l00165"></a>00165 <span class="comment"># attempt to solve the problem within ten seconds of planning time</span>
<a name="l00166"></a>00166 solved = ss.solve(10.0)
<a name="l00167"></a>00167 <span class="keywordflow">if</span> (solved):
<a name="l00168"></a>00168 <span class="keywordflow">print</span> <span class="stringliteral">"Found solution:"</span>
<a name="l00169"></a>00169 <span class="comment"># print the path to screen</span>
<a name="l00170"></a>00170 ss.simplifySolution()
<a name="l00171"></a>00171 <span class="keywordflow">print</span> ss.getSolutionPath()
<a name="l00172"></a>00172 <span class="keywordflow">else</span>:
<a name="l00173"></a>00173 <span class="keywordflow">print</span> <span class="stringliteral">"No solution found"</span>
<a name="l00174"></a>00174
<a name="l00175"></a>00175
<a name="l00176"></a>00176 <span class="keywordflow">if</span> __name__ == <span class="stringliteral">'__main__'</span>:
<a name="l00177"></a>00177 <span class="keywordflow">print</span> <span class="stringliteral">"Using default uniform sampler:"</span>
<a name="l00178"></a>00178 plan(0)
<a name="l00179"></a>00179 <span class="keywordflow">print</span> <span class="stringliteral">"\nUsing obstacle-based sampler:"</span>
<a name="l00180"></a>00180 plan(1)
<a name="l00181"></a>00181 <span class="keywordflow">print</span> <span class="stringliteral">"\nUsing my sampler:"</span>
<a name="l00182"></a>00182 plan(2)
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<a href="http://www.rice.edu">Rice University</a><br>
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