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419 lines (318 loc) · 18.6 KB
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#pragma once
#include <iostream>
template<class Planner, class Workspace, class Agent>
void go_COMMON(const InstanceFileMap &args, Planner &planner,
const Workspace &workspace, const Agent &agent,
const typename Agent::State &start, const typename Agent::State &goal) {
#ifdef WITHGRAPHICS
bool firstIteration = true;
auto lambda = [&]() {
// std::cin.ignore();
start.draw();
goal.draw();
agent.drawMesh(start);
workspace.draw();
planner.query(start, goal, 1000, firstIteration);
firstIteration = false;
};
OpenGLWrapper::getOpenGLWrapper().runWithCallback(lambda, args);
#else
planner.query(start, goal);
planner.dfpairs();
#endif
}
template<class Workspace, class Agent>
void go_RRT(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "RRT");
// typedef flann::KDTreeSingleIndexParams KDTreeType;
typedef flann::KDTreeIndexParams KDTreeType;
typedef FLANN_KDTreeWrapper<KDTreeType, typename Agent::DistanceEvaluator, typename Agent::Edge> KDTree;
typedef UniformSampler<Workspace, Agent, KDTree> USampler;
typedef GoalBiasSampler<Agent, USampler> GBSampler;
typedef TreeInterface<Agent, KDTree, GBSampler> TreeInterface;
typedef RRT<Workspace, Agent, TreeInterface> Planner;
/* planner config */
KDTreeType kdtreeType(1);
KDTree kdtree(kdtreeType, agent.getDistanceEvaluator(), agent.getTreeStateSize());
USampler uniformsampler(workspace, agent, kdtree);
double goalBias = args.exists("Goal Bias") ? args.doubleVal("Goal Bias") : 0;
dfpair(stdout, "goal bias", "%g", goalBias);
GBSampler goalbiassampler(uniformsampler, goal, goalBias);
TreeInterface treeInterface(kdtree, goalbiassampler);
Planner planner(workspace, agent, treeInterface, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go_RRTConnect(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "RRT-Connect");
// typedef flann::KDTreeSingleIndexParams KDTreeType;
typedef flann::KDTreeIndexParams KDTreeType;
typedef FLANN_KDTreeWrapper<KDTreeType, typename Agent::DistanceEvaluator, typename Agent::Edge> KDTree;
typedef UniformSampler<Workspace, Agent, KDTree> USampler;
typedef GoalBiasSampler<Agent, USampler> GBSampler;
typedef TreeInterface<Agent, KDTree, GBSampler> TreeInterface;
typedef RRTConnect<Workspace, Agent, TreeInterface> Planner;
/* planner config */
KDTreeType kdtreeType(1);
KDTree kdtree(kdtreeType, agent.getDistanceEvaluator(), agent.getTreeStateSize());
USampler uniformsampler(workspace, agent, kdtree);
double goalBias = args.exists("Goal Bias") ? args.doubleVal("Goal Bias") : 0;
dfpair(stdout, "goal bias", "%g", goalBias);
GBSampler goalbiassampler(uniformsampler, goal, goalBias);
TreeInterface treeInterface(kdtree, goalbiassampler);
Planner planner(workspace, agent, treeInterface, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go_FBiasedRRT(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "FBiased RRT");
typedef PRMLite<Workspace, Agent> PRMLite;
// typedef flann::KDTreeSingleIndexParams KDTreeType;
typedef flann::KDTreeIndexParams KDTreeType;
typedef FLANN_KDTreeWrapper<KDTreeType, typename Agent::DistanceEvaluator, typename Agent::Edge> KDTree;
typedef FBiasedSampler<Workspace, Agent, KDTree, PRMLite> Sampler;
typedef GoalBiasSampler<Agent, Sampler> GBSampler;
typedef TreeInterface<Agent, KDTree, GBSampler> TreeInterface;
typedef RRT<Workspace, Agent, TreeInterface> Planner;
/* planner config */
unsigned int numberOfPRMVertices = stol(args.value("Number Of PRM Vertices"));
unsigned int numberOfNearestNeighborEdgeConsiderations = stol(args.value("Nearest Neighbors To Consider In PRM Edge Construction"));
double prmCollisionCheckDT = args.doubleVal("PRM Collision Check DT");
PRMLite prmLite(workspace, agent, numberOfPRMVertices, numberOfNearestNeighborEdgeConsiderations, prmCollisionCheckDT);
KDTreeType kdtreeType(1);
KDTree kdtree(kdtreeType, agent.getDistanceEvaluator(), agent.getTreeStateSize());
double omega = args.doubleVal("FBias Omega");
double stateRadius = args.doubleVal("FBias State Selection Radius");
dfpair(stdout, "omega", "%g", omega);
dfpair(stdout, "state selection radius", "%g", stateRadius);
Sampler sampler(workspace, agent, kdtree, prmLite, start, goal, stateRadius, omega);
double goalBias = args.exists("Goal Bias") ? args.doubleVal("Goal Bias") : 0;
dfpair(stdout, "goal bias", "%g", goalBias);
GBSampler goalbiassampler(sampler, goal, goalBias);
TreeInterface treeInterface(kdtree, goalbiassampler);
Planner planner(workspace, agent, treeInterface, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go_FBiasedShellRRT(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "FBiased Shell RRT");
typedef PRMLite<Workspace, Agent> PRMLite;
// typedef flann::KDTreeSingleIndexParams KDTreeType;
typedef flann::KDTreeIndexParams KDTreeType;
typedef FLANN_KDTreeWrapper<KDTreeType, typename Agent::DistanceEvaluator, typename Agent::Edge> KDTree;
typedef FBiasedShellSampler<Workspace, Agent, KDTree, PRMLite> Sampler;
// typedef GoalBiasSampler<Agent, Sampler> GBSampler;
// typedef TreeInterface<Agent, KDTree, GBSampler> TreeInterface;
typedef Shell<Agent, KDTree, Sampler> TreeInterface;
typedef RRT<Workspace, Agent, TreeInterface> Planner;
/* planner config */
unsigned int numberOfPRMVertices = stol(args.value("Number Of PRM Vertices"));
unsigned int numberOfNearestNeighborEdgeConsiderations = stol(args.value("Nearest Neighbors To Consider In PRM Edge Construction"));
double prmCollisionCheckDT = args.doubleVal("PRM Collision Check DT");
PRMLite prmLite(workspace, agent, numberOfPRMVertices, numberOfNearestNeighborEdgeConsiderations, prmCollisionCheckDT);
KDTreeType kdtreeType(1);
KDTree kdtree(kdtreeType, agent.getDistanceEvaluator(), agent.getTreeStateSize());
double shellPreference = args.doubleVal("Shell Preference");
dfpair(stdout, "shell preference", "%g", shellPreference);
double omega = args.doubleVal("FBias Omega");
double stateRadius = args.doubleVal("FBias State Selection Radius");
dfpair(stdout, "omega", "%g", omega);
dfpair(stdout, "state selection radius", "%g", stateRadius);
double shellDepth = args.doubleVal("Shell Depth");
dfpair(stdout, "shell depth", "%g", shellDepth);
Sampler sampler(workspace, agent, kdtree, prmLite, start, goal, stateRadius, shellDepth, omega, shellPreference);
// double goalBias = args.exists("Goal Bias") ? args.doubleVal("Goal Bias") : 0;
// dfpair(stdout, "goal bias", "%g", goalBias);
// GBSampler goalbiassampler(sampler, goal, goalBias);
TreeInterface treeInterface(kdtree, sampler, shellDepth);
Planner planner(workspace, agent, treeInterface, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go_EST(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "EST");
typedef EST<Workspace, Agent> Planner;
Planner planner(workspace, agent, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go_ESTBIDIR(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "EST");
// clock_t startT = clock();
typedef ESTBidirectional<Workspace, Agent> Planner;
Planner planner(workspace, agent, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go_KPIECE(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "KPIECE");
typedef KPIECE<Workspace, Agent> Planner;
Planner planner(workspace, agent, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go_SSTPPRM(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "SST + PPRM");
typedef PRMLite<Workspace, Agent> PRMLite;
typedef PlakuTreeInterface<Workspace, Agent, PRMLite> PlakuTreeInterfaceT;
typedef SST<Workspace, Agent, PlakuTreeInterfaceT, PlakuTreeInterfaceT> SSTTreeInterface;
typedef RRT<Workspace, Agent, SSTTreeInterface> Planner;
unsigned int numberOfPRMVertices = stol(args.value("Number Of PRM Vertices"));
unsigned int numberOfNearestNeighborEdgeConsiderations = stol(args.value("Nearest Neighbors To Consider In PRM Edge Construction"));
double prmCollisionCheckDT = args.doubleVal("PRM Collision Check DT");
PRMLite prmLite(workspace, agent, numberOfPRMVertices, numberOfNearestNeighborEdgeConsiderations, prmCollisionCheckDT);
double alpha = args.doubleVal("Plaku Alpha Value");
double b = args.doubleVal("Plaku b Value");
double stateRadius = args.doubleVal("Plaku PRM State Selection Radius");
double goalBias = args.exists("Goal Bias") ? args.doubleVal("Goal Bias") : 0;
dfpair(stdout, "goal bias", "%g", goalBias);
PlakuTreeInterfaceT plakuTreeInterface(workspace, agent, prmLite, start, goal, alpha, b, stateRadius, goalBias);
double sstRadius = args.doubleVal("SST Radius");
double sstResize = args.doubleVal("SST Resize Threshold");
SSTTreeInterface sstTreeInterface(workspace, agent, plakuTreeInterface, plakuTreeInterface, sstRadius, sstResize);
Planner planner(workspace, agent, sstTreeInterface, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go_SSTGridPPRM(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "SST Grid + PPRM");
typedef PRMLite<Workspace, Agent> PRMLite;
typedef PlakuTreeInterface<Workspace, Agent, PRMLite> PlakuTreeInterfaceT;
typedef SST_Grid<Workspace, Agent, PlakuTreeInterfaceT, PlakuTreeInterfaceT> SSTTreeInterface;
typedef RRT<Workspace, Agent, SSTTreeInterface> Planner;
unsigned int numberOfPRMVertices = stol(args.value("Number Of PRM Vertices"));
unsigned int numberOfNearestNeighborEdgeConsiderations = stol(args.value("Nearest Neighbors To Consider In PRM Edge Construction"));
double prmCollisionCheckDT = args.doubleVal("PRM Collision Check DT");
PRMLite prmLite(workspace, agent, numberOfPRMVertices, numberOfNearestNeighborEdgeConsiderations, prmCollisionCheckDT);
double alpha = args.doubleVal("Plaku Alpha Value");
double b = args.doubleVal("Plaku b Value");
double stateRadius = args.doubleVal("Plaku PRM State Selection Radius");
double goalBias = args.exists("Goal Bias") ? args.doubleVal("Goal Bias") : 0;
dfpair(stdout, "goal bias", "%g", goalBias);
PlakuTreeInterfaceT plakuTreeInterface(workspace, agent, prmLite, start, goal, alpha, b, stateRadius, goalBias);
double sstRadius = args.doubleVal("SST Radius");
double sstResize = args.doubleVal("SST Resize Threshold");
SSTTreeInterface sstTreeInterface(workspace, agent, plakuTreeInterface, plakuTreeInterface, sstRadius, sstResize);
Planner planner(workspace, agent, sstTreeInterface, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go_PPRM(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "PPRM");
typedef PRMLite<Workspace, Agent> PRMLite;
typedef PlakuTreeInterface<Workspace, Agent, PRMLite> PlakuTreeInterfaceT;
typedef RRT<Workspace, Agent, PlakuTreeInterfaceT> Planner;
unsigned int numberOfPRMVertices = stol(args.value("Number Of PRM Vertices"));
unsigned int numberOfNearestNeighborEdgeConsiderations = stol(args.value("Nearest Neighbors To Consider In PRM Edge Construction"));
double prmCollisionCheckDT = args.doubleVal("PRM Collision Check DT");
PRMLite prmLite(workspace, agent, numberOfPRMVertices, numberOfNearestNeighborEdgeConsiderations, prmCollisionCheckDT);
double alpha = args.doubleVal("Plaku Alpha Value");
double b = args.doubleVal("Plaku b Value");
double stateRadius = args.doubleVal("Plaku PRM State Selection Radius");
double goalBias = args.exists("Goal Bias") ? args.doubleVal("Goal Bias") : 0;
dfpair(stdout, "goal bias", "%g", goalBias);
PlakuTreeInterfaceT plakuTreeInterface(workspace, agent, prmLite, start, goal, alpha, b, stateRadius, goalBias);
// #ifdef WITHGRAPHICS
// bool firstIteration = true;
// auto lambda = [&]() {
// plakuTreeInterface.draw();
// workspace.draw();
// };
// OpenGLWrapper::getOpenGLWrapper().runWithCallback(lambda, args);
// #endif
Planner planner(workspace, agent, plakuTreeInterface, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go_NewSearch(const InstanceFileMap &args, const Agent &agent, const Workspace &workspace,
const typename Agent::State &start, const typename Agent::State &goal) {
dfpair(stdout, "planner", "%s", "New Search");
typedef flann::KDTreeIndexParams KDTreeType;
typedef FLANN_KDTreeWrapper<KDTreeType, typename Agent::DistanceEvaluator, typename Agent::Edge> KDTree;
// typedef FrequencyTreeInterface<Agent> RegionManager;
typedef PRMLite<Workspace, Agent> PRMLite;
typedef NewTreeInterface<Workspace, Agent, KDTree, PRMLite> TreeInterface;
typedef RRT<Workspace, Agent, TreeInterface> Planner;
KDTreeType kdtreeType(1);
KDTree kdtree(kdtreeType, agent.getDistanceEvaluator(), agent.getTreeStateSize());
unsigned int numberOfPRMVertices = stol(args.value("Number Of PRM Vertices"));
unsigned int numberOfNearestNeighborEdgeConsiderations = stol(args.value("Nearest Neighbors To Consider In PRM Edge Construction"));
double prmCollisionCheckDT = args.doubleVal("PRM Collision Check DT");
PRMLite prmLite(workspace, agent, numberOfPRMVertices, numberOfNearestNeighborEdgeConsiderations, prmCollisionCheckDT);
// SimpleBestFirst discreteSearch;
double stateRadius = args.doubleVal("PRM State Selection Radius");
double goalBias = args.exists("Goal Bias") ? args.doubleVal("Goal Bias") : 0;
dfpair(stdout, "goal bias", "%g", goalBias);
TreeInterface treeInterface(workspace, agent, kdtree, prmLite, start, goal, stateRadius, goalBias);
Planner planner(workspace, agent, treeInterface, args);
go_COMMON<Planner, Workspace, Agent>(args, planner, workspace, agent, start, goal);
}
template<class Workspace, class Agent>
void go(const InstanceFileMap &args, const Workspace &workspace, const Agent &agent,
const typename Agent::State &start, const typename Agent::State &goal) {
clock_t startT = clock();
std::string planner = args.value("Planner");
if(planner.compare("RRT") == 0) {
go_RRT<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("RRT Connect") == 0) {
go_RRTConnect<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("FBiased RRT") == 0) {
go_FBiasedRRT<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("FBiased Shell RRT") == 0) {
go_FBiasedShellRRT<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("New Search") == 0) {
go_NewSearch<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("PPRM") == 0) {
go_PPRM<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("KPIECE") == 0) {
go_KPIECE<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("SST") == 0) {
go_SST<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("SST Grid") == 0) {
go_SSTGrid<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("SST + PPRM") == 0) {
go_SSTPPRM<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("SST Grid + PPRM") == 0) {
go_SSTGridPPRM<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("MRRT") == 0) {
go_MRRT<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("MRRT+S") == 0) {
go_MRRTPlusS<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("AO RRT") == 0) {
go_AORRT<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("AO RRT 2") == 0) {
go_AORRT2<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("EST") == 0) {
go_EST<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("EST Bidirectional") == 0) {
go_ESTBIDIR<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("AO EST") == 0) {
go_AOEST<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("Anytime RRT") == 0) {
go_AnytimeRRT<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("Anytime EST") == 0) {
go_AnytimeEST<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("Anytime Restarting EST") == 0) {
go_AnytimeRestartingEST<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("Anytime Bidirectional EST") == 0) {
go_AnytimeBidirectionalEST<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("Anytime PPRM") == 0) {
go_AnytimePPRM<Workspace, Agent>(args, agent, workspace, start, goal);
} else if(planner.compare("Anytime PPRM + SST") == 0) {
go_AnytimeSSTPPRM<Workspace, Agent>(args, agent, workspace, start, goal);
} else {
fprintf(stderr, "unreocognized planner: %s\n", planner.c_str());
}
clock_t endT = clock();
dfpair(stdout, "total solving time", "%g", (double)(endT-startT) / CLOCKS_PER_SEC);
}