void SlamGMapping::writeOdomMsgtoFile(tf::Stamped<tf::Transform>& odom_pose){
tf::Quaternion quat = odom_pose.getRotation();
fwrite(&quat,sizeof(tf::Quaternion),1,odomfile);
fwrite(&odom_pose.getOrigin(),sizeof(tf::Vector3),1,odomfile);
/*
tfScalar x = odom_pose.getRotation().getX();
tfScalar y = odom_pose.getRotation().getY();
tfScalar z = odom_pose.getRotation().getZ();
tfScalar w = odom_pose.getRotation().getW();
fwrite(&x,sizeof(tfScalar),1,odomfile);
fwrite(&y,sizeof(tfScalar),1,odomfile);
fwrite(&z,sizeof(tfScalar),1,odomfile);
fwrite(&w,sizeof(tfScalar),1,odomfile);
x = odom_pose.getOrigin().getX();
y = odom_pose.getOrigin().getY();
z = odom_pose.getOrigin().getZ();
fwrite(&x,sizeof(tfScalar),1,odomfile);
fwrite(&y,sizeof(tfScalar),1,odomfile);
fwrite(&z,sizeof(tfScalar),1,odomfile);*/
}
void publishTransform(tf::Stamped<tf::Pose> _odom2map)
{
/*
double x, y, th;
x = odom2map.getOrigin()[0];
y = odom2map.getOrigin()[1];
th = tf::getYaw(odom2map.getRotation());
ROS_INFO("x = %f y = %f th = %f",
(x), (y), (th));
* */
tf::Transform tfom = tf::Transform(tf::Quaternion(_odom2map.getRotation()),
tf::Point(_odom2map.getOrigin()));
tf::StampedTransform map_trans_stamped(tfom.inverse(), current_time, mapFrame, odomFrame);
tf_broadcaster->sendTransform(map_trans_stamped);
/*
//first, we'll publish the transform over tf
geometry_msgs::TransformStamped map_trans;
mtx.lock();
map_trans.header.stamp = current_time;
mtx.unlock();
map_trans.header.frame_id = mapFrame;
map_trans.child_frame_id = odomFrame;
map_trans.transform.translation.x = coords.x;
map_trans.transform.translation.y = coords.y;
map_trans.transform.translation.z = 0.0;
geometry_msgs::Quaternion quat = tf::createQuaternionMsgFromYaw(coords.th);
map_trans.transform.rotation = quat;
//send the transform
tf_broadcaster->sendTransform(map_trans);
*/
}
double ExploreFrontier::getOrientationChange(const Frontier& frontier, const tf::Stamped<tf::Pose>& robot_pose){
double robot_yaw = tf::getYaw(robot_pose.getRotation());
double robot_atan2 = atan2(robot_pose.getOrigin().y() + sin(robot_yaw), robot_pose.getOrigin().x() + cos(robot_yaw));
double frontier_atan2 = atan2(frontier.pose.position.x, frontier.pose.position.y);
double orientation_change = robot_atan2 - frontier_atan2;
ROS_DEBUG("Orientation change: %.3f", orientation_change * (180.0 / M_PI));
return orientation_change;
}
void SlamGMapping::writePoseStamped(tf::Stamped<tf::Pose> & pose_stamped){
//fwrite(&pose_stamped.frame_id_,sizeof();
tf::Quaternion quat = pose_stamped.getRotation();
fwrite(&quat,sizeof(tf::Quaternion),1,posestampedfile);
fwrite(&pose_stamped.getOrigin(),sizeof(tf::Vector3),1,posestampedfile);
}
double getGoalOrientationAngleDifference(const tf::Stamped<tf::Pose>& global_pose, double goal_th) {
double yaw = tf::getYaw(global_pose.getRotation());
return angles::shortest_angular_distance(yaw, goal_th);
}
const std::vector<geometry_msgs::PoseStamped>& global_plan,
const costmap_2d::Costmap2D& costmap __attribute__ ( (unused)),
const std::string& global_frame,
tf::Stamped<tf::Pose>& global_pose,
const nav_msgs::Odometry& base_odom,
double rot_stopped_vel, double trans_stopped_vel,
double xy_goal_tolerance, double yaw_goal_tolerance)
{
//we assume the global goal is the last point in the global plan
tf::Stamped<tf::Pose> goal_pose;
getGoalPose (tf, global_plan, global_frame, goal_pose);
double goal_x = goal_pose.getOrigin().getX();
double goal_y = goal_pose.getOrigin().getY();
double goal_th = tf::getYaw (goal_pose.getRotation());
//check to see if we've reached the goal position
if (getGoalPositionDistance (global_pose, goal_x, goal_y) <= xy_goal_tolerance)
{
//check to see if the goal orientation has been reached
if (fabs (getGoalOrientationAngleDifference (global_pose, goal_th)) <= yaw_goal_tolerance)
{
//make sure that we're actually stopped before returning success
if (stopped (base_odom, rot_stopped_vel, trans_stopped_vel))
return true;
}
}
return false;
}
/*
* given the current state of the robot, find a good trajectory
*/
base_local_planner::Trajectory EDWAPlanner::findBestPath(
tf::Stamped<tf::Pose> global_pose,
tf::Stamped<tf::Pose> global_vel,
tf::Stamped<tf::Pose>& drive_velocities,
std::vector<geometry_msgs::Point> footprint_spec) {
obstacle_costs_.setFootprint(footprint_spec);
//make sure that our configuration doesn't change mid-run
boost::mutex::scoped_lock l(configuration_mutex_);
Eigen::Vector3f pos(global_pose.getOrigin().getX(), global_pose.getOrigin().getY(), tf::getYaw(global_pose.getRotation()));
Eigen::Vector3f vel(global_vel.getOrigin().getX(), global_vel.getOrigin().getY(), tf::getYaw(global_vel.getRotation()));
geometry_msgs::PoseStamped goal_pose = global_plan_.back();
Eigen::Vector3f goal(goal_pose.pose.position.x, goal_pose.pose.position.y, tf::getYaw(goal_pose.pose.orientation));
base_local_planner::LocalPlannerLimits limits = planner_util_->getCurrentLimits();
// prepare cost functions and generators for this run
generator_.initialise(pos,
vel,
goal,
&limits,
vsamples_);
energy_costs_.setLastSpeeds(global_vel.getOrigin().getX(), global_vel.getOrigin().getY(), tf::getYaw(global_vel.getRotation()));
result_traj_.cost_ = -7;
// find best trajectory by sampling and scoring the samples
std::vector<base_local_planner::Trajectory> all_explored;
scored_sampling_planner_.findBestTrajectory(result_traj_, &all_explored);
if(publish_traj_pc_)
{
base_local_planner::MapGridCostPoint pt;
traj_cloud_->points.clear();
traj_cloud_->width = 0;
traj_cloud_->height = 0;
std_msgs::Header header;
pcl_conversions::fromPCL(traj_cloud_->header, header);
header.stamp = ros::Time::now();
traj_cloud_->header = pcl_conversions::toPCL(header);
for(std::vector<base_local_planner::Trajectory>::iterator t=all_explored.begin(); t != all_explored.end(); ++t)
{
if(t->cost_<0)
continue;
// Fill out the plan
for(unsigned int i = 0; i < t->getPointsSize(); ++i) {
double p_x, p_y, p_th;
t->getPoint(i, p_x, p_y, p_th);
pt.x=p_x;
pt.y=p_y;
pt.z=0;
pt.path_cost=p_th;
pt.total_cost=t->cost_;
traj_cloud_->push_back(pt);
}
}
traj_cloud_pub_.publish(*traj_cloud_);
}
// verbose publishing of point clouds
if (publish_cost_grid_pc_) {
//we'll publish the visualization of the costs to rviz before returning our best trajectory
map_viz_.publishCostCloud(planner_util_->getCostmap());
}
// debrief stateful scoring functions
oscillation_costs_.updateOscillationFlags(pos, &result_traj_, planner_util_->getCurrentLimits().min_trans_vel);
//if we don't have a legal trajectory, we'll just command zero
if (result_traj_.cost_ < 0) {
drive_velocities.setIdentity();
} else {
tf::Vector3 start(result_traj_.xv_, result_traj_.yv_, 0);
drive_velocities.setOrigin(start);
tf::Matrix3x3 matrix;
matrix.setRotation(tf::createQuaternionFromYaw(result_traj_.thetav_));
drive_velocities.setBasis(matrix);
}
return result_traj_;
}
void EDWAPlanner::updatePlanAndLocalCosts(
tf::Stamped<tf::Pose> global_pose,
const std::vector<geometry_msgs::PoseStamped>& new_plan) {
global_plan_.resize(new_plan.size());
for (unsigned int i = 0; i < new_plan.size(); ++i) {
global_plan_[i] = new_plan[i];
}
// costs for going away from path
path_costs_.setTargetPoses(global_plan_);
// costs for not going towards the local goal as much as possible
goal_costs_.setTargetPoses(global_plan_);
// alignment costs
geometry_msgs::PoseStamped goal_pose = global_plan_.back();
Eigen::Vector3f pos(global_pose.getOrigin().getX(), global_pose.getOrigin().getY(), tf::getYaw(global_pose.getRotation()));
double sq_dist =
(pos[0] - goal_pose.pose.position.x) * (pos[0] - goal_pose.pose.position.x) +
(pos[1] - goal_pose.pose.position.y) * (pos[1] - goal_pose.pose.position.y);
// we want the robot nose to be drawn to its final position
// (before robot turns towards goal orientation), not the end of the
// path for the robot center. Choosing the final position after
// turning towards goal orientation causes instability when the
// robot needs to make a 180 degree turn at the end
std::vector<geometry_msgs::PoseStamped> front_global_plan = global_plan_;
double angle_to_goal = atan2(goal_pose.pose.position.y - pos[1], goal_pose.pose.position.x - pos[0]);
front_global_plan.back().pose.position.x = front_global_plan.back().pose.position.x +
forward_point_distance_ * cos(angle_to_goal);
front_global_plan.back().pose.position.y = front_global_plan.back().pose.position.y + forward_point_distance_ *
sin(angle_to_goal);
goal_front_costs_.setTargetPoses(front_global_plan);
// keeping the nose on the path
if (sq_dist > forward_point_distance_ * forward_point_distance_ * cheat_factor_) {
double resolution = planner_util_->getCostmap()->getResolution();
alignment_costs_.setScale(resolution * pdist_scale_ * 0.5);
// costs for robot being aligned with path (nose on path, not ju
alignment_costs_.setTargetPoses(global_plan_);
} else {
// once we are close to goal, trying to keep the nose close to anything destabilizes behavior.
alignment_costs_.setScale(0.0);
}
}
bool goalOrientationReached(const tf::Stamped<tf::Pose>& global_pose, double goal_th, double yaw_goal_tolerance) {
double yaw = tf::getYaw(global_pose.getRotation());
return fabs(angles::shortest_angular_distance(yaw, goal_th)) <= yaw_goal_tolerance;
}
//given the current state of the robot, find a good trajectory
base_local_planner::Trajectory DWAPlanner::findBestPath(tf::Stamped<tf::Pose> global_pose, tf::Stamped<tf::Pose> global_vel,
tf::Stamped<tf::Pose>& drive_velocities){
//make sure that our configuration doesn't change mid-run
boost::mutex::scoped_lock l(configuration_mutex_);
//make sure to get an updated copy of the costmap before computing trajectories
costmap_ros_->getCostmapCopy(costmap_);
Eigen::Vector3f pos(global_pose.getOrigin().getX(), global_pose.getOrigin().getY(), tf::getYaw(global_pose.getRotation()));
Eigen::Vector3f vel(global_vel.getOrigin().getX(), global_vel.getOrigin().getY(), tf::getYaw(global_vel.getRotation()));
//reset the map for new operations
map_.resetPathDist();
front_map_.resetPathDist();
//make sure that we update our path based on the global plan and compute costs
map_.setPathCells(costmap_, global_plan_);
std::vector<geometry_msgs::PoseStamped> front_global_plan = global_plan_;
front_global_plan.back().pose.position.x = front_global_plan.back().pose.position.x + forward_point_distance_ * cos(tf::getYaw(front_global_plan.back().pose.orientation));
front_global_plan.back().pose.position.y = front_global_plan.back().pose.position.y + forward_point_distance_ * sin(tf::getYaw(front_global_plan.back().pose.orientation));
front_map_.setPathCells(costmap_, front_global_plan);
ROS_DEBUG_NAMED("dwa_local_planner", "Path/Goal distance computed");
//rollout trajectories and find the minimum cost one
base_local_planner::Trajectory best = computeTrajectories(pos, vel);
ROS_DEBUG_NAMED("dwa_local_planner", "Trajectories created");
//if we don't have a legal trajectory, we'll just command zero
if(best.cost_ < 0){
drive_velocities.setIdentity();
}
else{
tf::Vector3 start(best.xv_, best.yv_, 0);
drive_velocities.setOrigin(start);
tf::Matrix3x3 matrix;
matrix.setRotation(tf::createQuaternionFromYaw(best.thetav_));
drive_velocities.setBasis(matrix);
}
//we'll publish the visualization of the costs to rviz before returning our best trajectory
map_viz_.publishCostCloud();
return best;
}
void SlamGMapping::writeLaserPoseStamped(tf::Stamped<tf::Transform>& laser_pose){
tf::Quaternion quat = laser_pose.getRotation();
fwrite(&quat,sizeof(tf::Quaternion),1,laserposefile);
fwrite(&laser_pose.getOrigin(),sizeof(tf::Vector3),1,laserposefile);
}
/*
* given the current state of the robot, find a good trajectory
*/
base_local_planner::Trajectory DWAPlanner::findBestPath(
tf::Stamped<tf::Pose> global_pose,
tf::Stamped<tf::Pose> global_vel,
tf::Stamped<tf::Pose>& drive_velocities,
std::vector<geometry_msgs::Point> footprint_spec) {
obstacle_costs_.setFootprint(footprint_spec);
//make sure that our configuration doesn't change mid-run
boost::mutex::scoped_lock l(configuration_mutex_);
Eigen::Vector3f pos(global_pose.getOrigin().getX(), global_pose.getOrigin().getY(), tf::getYaw(global_pose.getRotation()));
Eigen::Vector3f vel(global_vel.getOrigin().getX(), global_vel.getOrigin().getY(), tf::getYaw(global_vel.getRotation()));
geometry_msgs::PoseStamped goal_pose = global_plan_.back();
Eigen::Vector3f goal(goal_pose.pose.position.x, goal_pose.pose.position.y, tf::getYaw(goal_pose.pose.orientation));
base_local_planner::LocalPlannerLimits limits = planner_util_->getCurrentLimits();
// prepare cost functions and generators for this run
generator_.initialise(pos,
vel,
goal,
&limits,
vsamples_);
result_traj_.cost_ = -7;
// find best trajectory by sampling and scoring the samples
scored_sampling_planner_.findBestTrajectory(result_traj_, NULL);
// verbose publishing of point clouds
if (publish_cost_grid_pc_) {
//we'll publish the visualization of the costs to rviz before returning our best trajectory
map_viz_.publishCostCloud(*(planner_util_->getCostmap()));
}
// debrief stateful scoring functions
oscillation_costs_.updateOscillationFlags(pos, &result_traj_, planner_util_->getCurrentLimits().min_trans_vel);
//if we don't have a legal trajectory, we'll just command zero
if (result_traj_.cost_ < 0) {
drive_velocities.setIdentity();
} else {
tf::Vector3 start(result_traj_.xv_, result_traj_.yv_, 0);
drive_velocities.setOrigin(start);
tf::Matrix3x3 matrix;
matrix.setRotation(tf::createQuaternionFromYaw(result_traj_.thetav_));
drive_velocities.setBasis(matrix);
}
return result_traj_;
}
//given the current state of the robot, find a good trajectory
Trajectory TrajectoryPlanner::findBestPath(tf::Stamped<tf::Pose> global_pose, tf::Stamped<tf::Pose> global_vel,
tf::Stamped<tf::Pose>& drive_velocities){
Eigen::Vector3f pos(global_pose.getOrigin().getX(), global_pose.getOrigin().getY(), tf::getYaw(global_pose.getRotation()));
Eigen::Vector3f vel(global_vel.getOrigin().getX(), global_vel.getOrigin().getY(), tf::getYaw(global_vel.getRotation()));
//reset the map for new operations
path_map_.resetPathDist();
goal_map_.resetPathDist();
//temporarily remove obstacles that are within the footprint of the robot
std::vector<base_local_planner::Position2DInt> footprint_list =
footprint_helper_.getFootprintCells(
pos,
footprint_spec_,
costmap_,
true);
//mark cells within the initial footprint of the robot
for (unsigned int i = 0; i < footprint_list.size(); ++i) {
path_map_(footprint_list[i].x, footprint_list[i].y).within_robot = true;
}
//make sure that we update our path based on the global plan and compute costs
path_map_.setTargetCells(costmap_, global_plan_);
goal_map_.setLocalGoal(costmap_, global_plan_);
ROS_DEBUG("Path/Goal distance computed");
//rollout trajectories and find the minimum cost one
Trajectory best = createTrajectories(pos[0], pos[1], pos[2],
vel[0], vel[1], vel[2],
acc_lim_x_, acc_lim_y_, acc_lim_theta_);
ROS_DEBUG("Trajectories created");
/*
//If we want to print a ppm file to draw goal dist
char buf[4096];
sprintf(buf, "base_local_planner.ppm");
FILE *fp = fopen(buf, "w");
if(fp){
fprintf(fp, "P3\n");
fprintf(fp, "%d %d\n", map_.size_x_, map_.size_y_);
fprintf(fp, "255\n");
for(int j = map_.size_y_ - 1; j >= 0; --j){
for(unsigned int i = 0; i < map_.size_x_; ++i){
int g_dist = 255 - int(map_(i, j).goal_dist);
int p_dist = 255 - int(map_(i, j).path_dist);
if(g_dist < 0)
g_dist = 0;
if(p_dist < 0)
p_dist = 0;
fprintf(fp, "%d 0 %d ", g_dist, 0);
}
fprintf(fp, "\n");
}
fclose(fp);
}
*/
if(best.cost_ < 0){
drive_velocities.setIdentity();
}
else{
tf::Vector3 start(best.xv_, best.yv_, 0);
drive_velocities.setOrigin(start);
tf::Matrix3x3 matrix;
matrix.setRotation(tf::createQuaternionFromYaw(best.thetav_));
drive_velocities.setBasis(matrix);
}
return best;
}