double footstepHeuristicFollowPathLine(
SolverNode<FootstepState, FootstepGraph>::Ptr node, FootstepGraph::Ptr graph)
{
FootstepState::Ptr state = node->getState();
FootstepState::Ptr goal = graph->getGoal(state->getLeg());
Eigen::Vector3f goal_pos(goal->getPose().translation());
Eigen::Vector3f state_pos(state->getPose().translation());
Eigen::Vector3f state_mid_pos;
if (state->getLeg() == jsk_footstep_msgs::Footstep::LEFT) {
Eigen::Vector3f p(0, -0.1, 0);
state_mid_pos = state->getPose() * p;
} else { // Right
Eigen::Vector3f p(0, 0.1, 0);
state_mid_pos = state->getPose() * p;
}
double dist, to_goal, alp;
int idx;
Eigen::Vector3f foot;
dist = graph->heuristic_path_->distanceWithInfo(state_mid_pos, foot, to_goal, idx, alp);
//jsk_recognition_utils::FiniteLine::Ptr ln = graph->heuristic_path_->at(idx);
Eigen::Vector3f dir = graph->heuristic_path_->getDirection(idx);
Eigen::Quaternionf path_foot_rot;
path_foot_rot.setFromTwoVectors(state->getPose().matrix().block<3, 3>(0, 0) * Eigen::Vector3f::UnitX(),
dir);
double path_foot_theta = acos(path_foot_rot.w()) * 2;
if (path_foot_theta > M_PI) {
path_foot_theta = 2.0 * M_PI - path_foot_theta;
// foot_theta : [0, M_PI]
}
double step_cost = to_goal / graph->maxSuccessorDistance();
double follow_cost = dist / 0.02; // ???
double path_foot_rot_cost = path_foot_theta / graph->maxSuccessorRotation();
Eigen::Vector3f goal_diff = goal_pos - state_pos;
Eigen::Quaternionf goal_foot_rot;
goal_foot_rot.setFromTwoVectors(state->getPose().matrix().block<3, 3>(0, 0) * Eigen::Vector3f::UnitX(),
goal->getPose().matrix().block<3, 3>(0, 0) * Eigen::Vector3f::UnitX());
double goal_foot_theta = acos(goal_foot_rot.w()) * 2;
if (goal_foot_theta > M_PI) {
goal_foot_theta = 2.0 * M_PI - goal_foot_theta;
}
double goal_foot_rot_cost = goal_foot_theta / graph->maxSuccessorRotation();
//return step_cost + follow_cost + (4.0 * goal_foot_rot_cost) + (0.5 * path_foot_rot_cost);
return 2*(step_cost + follow_cost + (0.5 * path_foot_rot_cost));
}
double footstepHeuristicStraightRotation(
SolverNode<FootstepState, FootstepGraph>::Ptr node, FootstepGraph::Ptr graph)
{
FootstepState::Ptr state = node->getState();
FootstepState::Ptr goal = graph->getGoal(state->getLeg());
Eigen::Affine3f transform = state->getPose().inverse() * goal->getPose();
return (std::abs(transform.translation().norm() / graph->maxSuccessorDistance()) +
std::abs(Eigen::AngleAxisf(transform.rotation()).angle()) / graph->maxSuccessorRotation());
}
double footstepHeuristicStepCost(
SolverNode<FootstepState, FootstepGraph>::Ptr node, FootstepGraph::Ptr graph,
double first_rotation_weight,
double second_rotation_weight)
{
FootstepState::Ptr state = node->getState();
FootstepState::Ptr goal = graph->getGoal(state->getLeg());
Eigen::Vector3f goal_pos(goal->getPose().translation());
Eigen::Vector3f diff_pos(goal_pos - state->getPose().translation());
diff_pos[2] = 0.0; // Ignore z distance
Eigen::Quaternionf first_rot;
// Eigen::Affine3f::rotation is too slow because it calls SVD decomposition
first_rot.setFromTwoVectors(state->getPose().matrix().block<3, 3>(0, 0) * Eigen::Vector3f::UnitX(),
diff_pos.normalized());
Eigen::Quaternionf second_rot;
second_rot.setFromTwoVectors(diff_pos.normalized(),
goal->getPose().matrix().block<3, 3>(0, 0) * Eigen::Vector3f::UnitX());
// is it correct??
double first_theta = acos(first_rot.w()) * 2;
double second_theta = acos(second_rot.w()) * 2;
if (isnan(first_theta)) {
first_theta = 0;
}
if (isnan(second_theta)) {
second_theta = 0;
}
// acos := [0, M_PI]
if (first_theta > M_PI) {
first_theta = 2.0 * M_PI - first_theta;
}
if (second_theta > M_PI) {
second_theta = 2.0 * M_PI - second_theta;
}
//return (Eigen::Vector2f(diff_pos[0], diff_pos[1]).norm() / graph->maxSuccessorDistance()) + std::abs(diff_pos[2]) / 0.2 +
return (diff_pos.norm() / graph->maxSuccessorDistance()) + std::abs(diff_pos[2]) / 0.2 +
(first_theta * first_rotation_weight + second_theta * second_rotation_weight) / graph->maxSuccessorRotation();
}