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a_star.cpp
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a_star.cpp
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// Copyright (c) 2020, Samsung Research America
// Copyright (c) 2020, Applied Electric Vehicles Pty Ltd
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License. Reserved.
#include <omp.h>
#include <ompl/base/ScopedState.h>
#include <ompl/base/spaces/DubinsStateSpace.h>
#include <ompl/base/spaces/ReedsSheppStateSpace.h>
#include <cmath>
#include <stdexcept>
#include <memory>
#include <algorithm>
#include <limits>
#include <type_traits>
#include <chrono>
#include <thread>
#include <utility>
#include <vector>
#include "nav2_smac_planner/a_star.hpp"
using namespace std::chrono; // NOLINT
namespace nav2_smac_planner
{
template<typename NodeT>
AStarAlgorithm<NodeT>::AStarAlgorithm(
const MotionModel & motion_model,
const SearchInfo & search_info)
: _traverse_unknown(true),
_max_iterations(0),
_x_size(0),
_y_size(0),
_search_info(search_info),
_goal_coordinates(Coordinates()),
_start(nullptr),
_goal(nullptr),
_motion_model(motion_model)
{
_graph.reserve(100000);
}
template<typename NodeT>
AStarAlgorithm<NodeT>::~AStarAlgorithm()
{
}
template<typename NodeT>
void AStarAlgorithm<NodeT>::initialize(
const bool & allow_unknown,
int & max_iterations,
const int & max_on_approach_iterations,
const float & lookup_table_size,
const unsigned int & dim_3_size)
{
_traverse_unknown = allow_unknown;
_max_iterations = max_iterations;
_max_on_approach_iterations = max_on_approach_iterations;
NodeT::precomputeDistanceHeuristic(lookup_table_size, _motion_model, dim_3_size, _search_info);
_dim3_size = dim_3_size;
}
template<>
void AStarAlgorithm<Node2D>::initialize(
const bool & allow_unknown,
int & max_iterations,
const int & max_on_approach_iterations,
const float & /*lookup_table_size*/,
const unsigned int & dim_3_size)
{
_traverse_unknown = allow_unknown;
_max_iterations = max_iterations;
_max_on_approach_iterations = max_on_approach_iterations;
if (dim_3_size != 1) {
throw std::runtime_error("Node type Node2D cannot be given non-1 dim 3 quantization.");
}
_dim3_size = dim_3_size;
}
template<typename NodeT>
void AStarAlgorithm<NodeT>::setCollisionChecker(GridCollisionChecker * collision_checker)
{
_collision_checker = collision_checker;
_costmap = collision_checker->getCostmap();
unsigned int x_size = _costmap->getSizeInCellsX();
unsigned int y_size = _costmap->getSizeInCellsY();
clearGraph();
if (getSizeX() != x_size || getSizeY() != y_size) {
_x_size = x_size;
_y_size = y_size;
NodeT::initMotionModel(_motion_model, _x_size, _y_size, _dim3_size, _search_info);
}
}
template<typename NodeT>
typename AStarAlgorithm<NodeT>::NodePtr AStarAlgorithm<NodeT>::addToGraph(
const unsigned int & index)
{
// Emplace will only create a new object if it doesn't already exist.
// If an element exists, it will return the existing object, not create a new one.
return &(_graph.emplace(index, NodeT(index)).first->second);
}
template<>
void AStarAlgorithm<Node2D>::setStart(
const unsigned int & mx,
const unsigned int & my,
const unsigned int & dim_3)
{
if (dim_3 != 0) {
throw std::runtime_error("Node type Node2D cannot be given non-zero starting dim 3.");
}
_start = addToGraph(Node2D::getIndex(mx, my, getSizeX()));
}
template<typename NodeT>
void AStarAlgorithm<NodeT>::setStart(
const unsigned int & mx,
const unsigned int & my,
const unsigned int & dim_3)
{
_start = addToGraph(NodeT::getIndex(mx, my, dim_3));
_start->setPose(
Coordinates(
static_cast<float>(mx),
static_cast<float>(my),
static_cast<float>(dim_3)));
}
template<>
void AStarAlgorithm<Node2D>::setGoal(
const unsigned int & mx,
const unsigned int & my,
const unsigned int & dim_3)
{
if (dim_3 != 0) {
throw std::runtime_error("Node type Node2D cannot be given non-zero goal dim 3.");
}
_goal = addToGraph(Node2D::getIndex(mx, my, getSizeX()));
_goal_coordinates = Node2D::Coordinates(mx, my);
}
template<typename NodeT>
void AStarAlgorithm<NodeT>::setGoal(
const unsigned int & mx,
const unsigned int & my,
const unsigned int & dim_3)
{
_goal = addToGraph(NodeT::getIndex(mx, my, dim_3));
typename NodeT::Coordinates goal_coords(
static_cast<float>(mx),
static_cast<float>(my),
static_cast<float>(dim_3));
if (!_search_info.cache_obstacle_heuristic || goal_coords != _goal_coordinates) {
NodeT::resetObstacleHeuristic(_costmap, mx, my);
}
_goal_coordinates = goal_coords;
_goal->setPose(_goal_coordinates);
}
template<typename NodeT>
bool AStarAlgorithm<NodeT>::areInputsValid()
{
// Check if graph was filled in
if (_graph.empty()) {
throw std::runtime_error("Failed to compute path, no costmap given.");
}
// Check if points were filled in
if (!_start || !_goal) {
throw std::runtime_error("Failed to compute path, no valid start or goal given.");
}
// Check if ending point is valid
if (getToleranceHeuristic() < 0.001 &&
!_goal->isNodeValid(_traverse_unknown, _collision_checker))
{
throw std::runtime_error("Failed to compute path, goal is occupied with no tolerance.");
}
// Check if starting point is valid
if (!_start->isNodeValid(_traverse_unknown, _collision_checker)) {
throw std::runtime_error("Starting point in lethal space! Cannot create feasible plan.");
}
return true;
}
template<typename NodeT>
bool AStarAlgorithm<NodeT>::createPath(
CoordinateVector & path, int & iterations,
const float & tolerance)
{
_tolerance = tolerance;
_best_heuristic_node = {std::numeric_limits<float>::max(), 0};
clearQueue();
if (!areInputsValid()) {
return false;
}
// 0) Add starting point to the open set
addNode(0.0, getStart());
getStart()->setAccumulatedCost(0.0);
// Optimization: preallocate all variables
NodePtr current_node = nullptr;
NodePtr neighbor = nullptr;
NodePtr expansion_result = nullptr;
float g_cost = 0.0;
NodeVector neighbors;
int approach_iterations = 0;
NeighborIterator neighbor_iterator;
int analytic_iterations = 0;
int closest_distance = std::numeric_limits<int>::max();
// Given an index, return a node ptr reference if its collision-free and valid
const unsigned int max_index = getSizeX() * getSizeY() * getSizeDim3();
NodeGetter neighborGetter =
[&, this](const unsigned int & index, NodePtr & neighbor_rtn) -> bool
{
if (index < 0 || index >= max_index) {
return false;
}
neighbor_rtn = addToGraph(index);
return true;
};
while (iterations < getMaxIterations() && !_queue.empty()) {
// 1) Pick Nbest from O s.t. min(f(Nbest)), remove from queue
current_node = getNextNode();
// We allow for nodes to be queued multiple times in case
// shorter paths result in it, but we can visit only once
if (current_node->wasVisited()) {
continue;
}
iterations++;
// 2) Mark Nbest as visited
current_node->visited();
// 2.1) Use an analytic expansion (if available) to generate a path
expansion_result = nullptr;
expansion_result = tryAnalyticExpansion(
current_node, neighborGetter, analytic_iterations, closest_distance);
if (expansion_result != nullptr) {
current_node = expansion_result;
}
// 3) Check if we're at the goal, backtrace if required
if (isGoal(current_node)) {
return backtracePath(current_node, path);
} else if (_best_heuristic_node.first < getToleranceHeuristic()) {
// Optimization: Let us find when in tolerance and refine within reason
approach_iterations++;
if (approach_iterations >= getOnApproachMaxIterations()) {
return backtracePath(&_graph.at(_best_heuristic_node.second), path);
}
}
// 4) Expand neighbors of Nbest not visited
neighbors.clear();
current_node->getNeighbors(neighborGetter, _collision_checker, _traverse_unknown, neighbors);
for (neighbor_iterator = neighbors.begin();
neighbor_iterator != neighbors.end(); ++neighbor_iterator)
{
neighbor = *neighbor_iterator;
// 4.1) Compute the cost to go to this node
g_cost = current_node->getAccumulatedCost() + current_node->getTraversalCost(neighbor);
// 4.2) If this is a lower cost than prior, we set this as the new cost and new approach
if (g_cost < neighbor->getAccumulatedCost()) {
neighbor->setAccumulatedCost(g_cost);
neighbor->parent = current_node;
// 4.3) Add to queue with heuristic cost
addNode(g_cost + getHeuristicCost(neighbor), neighbor);
}
}
}
return false;
}
template<typename NodeT>
bool AStarAlgorithm<NodeT>::isGoal(NodePtr & node)
{
return node == getGoal();
}
template<>
bool AStarAlgorithm<Node2D>::backtracePath(NodePtr node, CoordinateVector & path)
{
if (!node->parent) {
return false;
}
NodePtr current_node = node;
while (current_node->parent) {
path.push_back(
Node2D::getCoords(
current_node->getIndex(), getSizeX(), getSizeDim3()));
current_node = current_node->parent;
}
return path.size() > 0;
}
template<typename NodeT>
bool AStarAlgorithm<NodeT>::backtracePath(NodePtr node, CoordinateVector & path)
{
if (!node->parent) {
return false;
}
NodePtr current_node = node;
while (current_node->parent) {
path.push_back(current_node->pose);
current_node = current_node->parent;
}
return path.size() > 0;
}
template<typename NodeT>
typename AStarAlgorithm<NodeT>::NodePtr & AStarAlgorithm<NodeT>::getStart()
{
return _start;
}
template<typename NodeT>
typename AStarAlgorithm<NodeT>::NodePtr & AStarAlgorithm<NodeT>::getGoal()
{
return _goal;
}
template<>
typename AStarAlgorithm<Node2D>::NodePtr AStarAlgorithm<Node2D>::getNextNode()
{
NodeBasic<Node2D> node = _queue.top().second;
_queue.pop();
return node.graph_node_ptr;
}
template<typename NodeT>
typename AStarAlgorithm<NodeT>::NodePtr AStarAlgorithm<NodeT>::getNextNode()
{
NodeBasic<NodeT> node = _queue.top().second;
_queue.pop();
// We only want to override the node's pose if it has not yet been visited
// to prevent the case that a node has been queued multiple times and
// a new branch is overriding one of lower cost already visited.
if (!node.graph_node_ptr->wasVisited()) {
node.graph_node_ptr->pose = node.pose;
}
return node.graph_node_ptr;
}
template<>
void AStarAlgorithm<Node2D>::addNode(const float & cost, NodePtr & node)
{
NodeBasic<Node2D> queued_node(node->getIndex());
queued_node.graph_node_ptr = node;
_queue.emplace(cost, queued_node);
}
template<typename NodeT>
void AStarAlgorithm<NodeT>::addNode(const float & cost, NodePtr & node)
{
NodeBasic<NodeT> queued_node(node->getIndex());
queued_node.pose = node->pose;
queued_node.graph_node_ptr = node;
_queue.emplace(cost, queued_node);
}
template<typename NodeT>
float AStarAlgorithm<NodeT>::getHeuristicCost(const NodePtr & node)
{
const Coordinates node_coords =
NodeT::getCoords(node->getIndex(), getSizeX(), getSizeDim3());
float heuristic = NodeT::getHeuristicCost(
node_coords, _goal_coordinates, _costmap);
if (heuristic < _best_heuristic_node.first) {
_best_heuristic_node = {heuristic, node->getIndex()};
}
return heuristic;
}
template<typename NodeT>
void AStarAlgorithm<NodeT>::clearQueue()
{
NodeQueue q;
std::swap(_queue, q);
}
template<typename NodeT>
void AStarAlgorithm<NodeT>::clearGraph()
{
Graph g;
std::swap(_graph, g);
_graph.reserve(100000);
}
template<typename NodeT>
int & AStarAlgorithm<NodeT>::getMaxIterations()
{
return _max_iterations;
}
template<typename NodeT>
int & AStarAlgorithm<NodeT>::getOnApproachMaxIterations()
{
return _max_on_approach_iterations;
}
template<typename NodeT>
float & AStarAlgorithm<NodeT>::getToleranceHeuristic()
{
return _tolerance;
}
template<typename NodeT>
unsigned int & AStarAlgorithm<NodeT>::getSizeX()
{
return _x_size;
}
template<typename NodeT>
unsigned int & AStarAlgorithm<NodeT>::getSizeY()
{
return _y_size;
}
template<typename NodeT>
unsigned int & AStarAlgorithm<NodeT>::getSizeDim3()
{
return _dim3_size;
}
template<typename NodeT>
typename AStarAlgorithm<NodeT>::NodePtr AStarAlgorithm<NodeT>::tryAnalyticExpansion(
const NodePtr & current_node, const NodeGetter & getter, int & analytic_iterations,
int & closest_distance)
{
// This must be a NodeHybrid or NodeLattice if we are using these motion models
if (_motion_model == MotionModel::DUBIN || _motion_model == MotionModel::REEDS_SHEPP ||
_motion_model == MotionModel::STATE_LATTICE)
{
// See if we are closer and should be expanding more often
const Coordinates node_coords =
NodeT::getCoords(current_node->getIndex(), getSizeX(), getSizeDim3());
closest_distance = std::min(
closest_distance,
static_cast<int>(NodeT::getHeuristicCost(node_coords, _goal_coordinates, _costmap)));
// We want to expand at a rate of d/expansion_ratio,
// but check to see if we are so close that we would be expanding every iteration
// If so, limit it to the expansion ratio (rounded up)
int desired_iterations = std::max(
static_cast<int>(closest_distance / _search_info.analytic_expansion_ratio),
static_cast<int>(std::ceil(_search_info.analytic_expansion_ratio)));
// If we are closer now, we should update the target number of iterations to go
analytic_iterations =
std::min(analytic_iterations, desired_iterations);
// Always run the expansion on the first run in case there is a
// trivial path to be found
if (analytic_iterations <= 0) {
// Reset the counter and try the analytic path expansion
analytic_iterations = desired_iterations;
AnalyticExpansionNodes analytic_nodes = getAnalyticPath(current_node, getter);
if (!analytic_nodes.empty()) {
// If we have a valid path, attempt to refine it
NodePtr node = current_node;
NodePtr test_node = current_node;
AnalyticExpansionNodes refined_analytic_nodes;
for (int i = 0; i < 8; i++) {
// Attempt to create better paths in 5 node increments, need to make sure
// they exist for each in order to do so (maximum of 40 points back).
if (test_node->parent && test_node->parent->parent && test_node->parent->parent->parent &&
test_node->parent->parent->parent->parent &&
test_node->parent->parent->parent->parent->parent)
{
test_node = test_node->parent->parent->parent->parent->parent;
refined_analytic_nodes = getAnalyticPath(test_node, getter);
if (refined_analytic_nodes.empty()) {
break;
}
analytic_nodes = refined_analytic_nodes;
node = test_node;
} else {
break;
}
}
return setAnalyticPath(node, analytic_nodes);
}
}
analytic_iterations--;
}
// No valid motion model - return nullptr
return NodePtr(nullptr);
}
template<typename NodeT>
typename AStarAlgorithm<NodeT>::AnalyticExpansionNodes AStarAlgorithm<NodeT>::getAnalyticPath(
const NodePtr & node,
const NodeGetter & node_getter)
{
static ompl::base::ScopedState<> from(node->motion_table.state_space), to(
node->motion_table.state_space), s(node->motion_table.state_space);
from[0] = node->pose.x;
from[1] = node->pose.y;
from[2] = node->pose.theta * node->motion_table.bin_size;
to[0] = _goal_coordinates.x;
to[1] = _goal_coordinates.y;
to[2] = _goal_coordinates.theta * node->motion_table.bin_size;
float d = node->motion_table.state_space->distance(from(), to());
// A move of sqrt(2) is guaranteed to be in a new cell
static const float sqrt_2 = std::sqrt(2.);
unsigned int num_intervals = std::floor(d / sqrt_2);
AnalyticExpansionNodes possible_nodes;
// When "from" and "to" are zero or one cell away,
// num_intervals == 0
possible_nodes.reserve(num_intervals); // We won't store this node or the goal
std::vector<double> reals;
// Pre-allocate
NodePtr prev(node);
unsigned int index = 0;
NodePtr next(nullptr);
float angle = 0.0;
Coordinates proposed_coordinates;
bool failure = false;
// Check intermediary poses (non-goal, non-start)
for (float i = 1; i < num_intervals; i++) {
node->motion_table.state_space->interpolate(from(), to(), i / num_intervals, s());
reals = s.reals();
angle = reals[2] / node->motion_table.bin_size;
while (angle < 0.0) {
angle += node->motion_table.num_angle_quantization_float;
}
while (angle >= node->motion_table.num_angle_quantization_float) {
angle -= node->motion_table.num_angle_quantization_float;
}
// Turn the pose into a node, and check if it is valid
index = NodeT::getIndex(
static_cast<unsigned int>(reals[0]),
static_cast<unsigned int>(reals[1]),
static_cast<unsigned int>(angle));
// Get the node from the graph
if (node_getter(index, next)) {
Coordinates initial_node_coords = next->pose;
proposed_coordinates = {static_cast<float>(reals[0]), static_cast<float>(reals[1]), angle};
next->setPose(proposed_coordinates);
if (next->isNodeValid(_traverse_unknown, _collision_checker) && next != prev) {
// Save the node, and its previous coordinates in case we need to abort
possible_nodes.emplace_back(next, initial_node_coords, proposed_coordinates);
prev = next;
} else {
// Abort
next->setPose(initial_node_coords);
failure = true;
break;
}
} else {
// Abort
failure = true;
break;
}
}
// Reset to initial poses to not impact future searches
for (const auto & node_pose : possible_nodes) {
const auto & n = node_pose.node;
n->setPose(node_pose.initial_coords);
}
if (failure) {
return AnalyticExpansionNodes();
}
return possible_nodes;
}
template<typename NodeT>
typename AStarAlgorithm<NodeT>::NodePtr AStarAlgorithm<NodeT>::setAnalyticPath(
const NodePtr & node,
const AnalyticExpansionNodes & expanded_nodes)
{
// Legitimate final path - set the parent relationships & poses
NodePtr prev = node;
for (const auto & node_pose : expanded_nodes) {
const auto & n = node_pose.node;
if (!n->wasVisited() && n->getIndex() != _goal->getIndex()) {
// Make sure this node has not been visited by the regular algorithm.
// If it has been, there is the (slight) chance that it is in the path we are expanding
// from, so we should skip it.
// Skipping to the next node will still create a kinematically feasible path.
n->parent = prev;
n->pose = node_pose.proposed_coords;
n->visited();
prev = n;
}
}
if (_goal != prev) {
_goal->parent = prev;
_goal->visited();
}
return _goal;
}
template<>
typename AStarAlgorithm<Node2D>::AnalyticExpansionNodes AStarAlgorithm<Node2D>::getAnalyticPath(
const NodePtr & node,
const NodeGetter & node_getter)
{
return AnalyticExpansionNodes();
}
template<>
typename AStarAlgorithm<Node2D>::NodePtr AStarAlgorithm<Node2D>::setAnalyticPath(
const NodePtr & node,
const AnalyticExpansionNodes & expanded_nodes)
{
return NodePtr(nullptr);
}
// Instantiate algorithm for the supported template types
template class AStarAlgorithm<Node2D>;
template class AStarAlgorithm<NodeHybrid>;
} // namespace nav2_smac_planner