ROS Navigation Stack之dwa_local

本文主要是介绍ROS Navigation Stack之dwa_local_planner源码分析，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

DWA和base_local_planner的关系

在base_local_planner包中有两个文件叫trajectory_planner.cpp 以及对应的ros实现，其和DWA是同一层的。
由于nav_core提供了统一的接口，因此我们可以先看看统一的接口有哪些，那我们便知道每一个算法里比较重要的函数有哪些。

nav_core包里的base_local_planner.h文件

//最为关键的地方，计算机器人下一刻的速度
virtual bool computeVelocityCommands(geometry_msgs::Twist& cmd_vel) = 0;
//判断是否到达目标点
virtual bool isGoalReached() = 0;
//加载全局路径
virtual bool setPlan(const std::vector<geometry_msgs::PoseStamped>& plan) = 0;
//初始化
virtual void initialize(std::string name, tf::TransformListener* tf, costmap_2d::Costmap2DROS* costmap_ros) = 0;

下面我们就先看看base_local_planner的computeVelocityCommands的主要实现框架

bool TrajectoryPlannerROS::computeVelocityCommands(geometry_msgs::Twist& cmd_vel)
{//检查初始化、检查是否已经到达目标点...略transformGlobalPlan(*tf_, global_plan_, global_pose, *costmap_, global_frame_, transformed_plan);//如果已经到达目标点，姿态还没到if (xy_tolerance_latch_ || (getGoalPositionDistance(global_pose, goal_x, goal_y) <= xy_goal_tolerance_)) {tc_->updatePlan(transformed_plan);//所以这个函数里最关键的子函数是findBestPathTrajectory path = tc_->findBestPath(global_pose, robot_vel, drive_cmds);return true;}tc_->updatePlan(transformed_plan);Trajectory path = tc_->findBestPath(global_pose, robot_vel, drive_cmds);//然后又是转换，然后就发布出速度了...
}

接下来我们看一下TrajectoryPlanner的findBestPath的实现框架，Come on~

Trajectory TrajectoryPlanner::findBestPath(tf::Stamped<tf::Pose> global_pose, tf::Stamped<tf::Pose> global_vel,tf::Stamped<tf::Pose>& drive_velocities)
{//...Trajectory best = createTrajectories(pos[0], pos[1], pos[2], vel[0], vel[1], vel[2],acc_lim_x_, acc_lim_y_, acc_lim_theta_);//...
}

顺藤摸瓜，一睹createTrajectories的内部实现，这个函数是轨迹采样算法，可以说是一个非常关键的函数。

Trajectory TrajectoryPlanner::createTrajectories(double x, double y, double theta,double vx, double vy, double vtheta,double acc_x, double acc_y, double acc_theta) 
{//检查最终点是否是有效的，判断变量在updatePlan中被赋值if( final_goal_position_valid_ ){double final_goal_dist = hypot( final_goal_x_ - x, final_goal_y_ - y );max_vel_x = min( max_vel_x, final_goal_dist / sim_time_ );}//是否使用dwa算法， sim_peroid_是1/controller_frequency_，暂时不清楚sim_period_和sim_time_的区别if (dwa_){max_vel_x = max(min(max_vel_x, vx + acc_x * sim_period_), min_vel_x_);min_vel_x = max(min_vel_x_, vx - acc_x * sim_period_);max_vel_theta = min(max_vel_th_, vtheta + acc_theta * sim_period_);min_vel_theta = max(min_vel_th_, vtheta - acc_theta * sim_period_);}else{max_vel_x = max(min(max_vel_x, vx + acc_x * sim_time_), min_vel_x_);min_vel_x = max(min_vel_x_, vx - acc_x * sim_time_);max_vel_theta = min(max_vel_th_, vtheta + acc_theta * sim_time_);min_vel_theta = max(min_vel_th_, vtheta - acc_theta * sim_time_);}//...先忽略其中的逻辑，只要知道按照不同的规则生成路径，调用的子函数是generateTrajectory
}

这个子函数的作用就是生成路径，并且评分

void TrajectoryPlanner::generateTrajectory
{//主要有两大作用：//生成路径和速度vx_i = computeNewVelocity(vx_samp, vx_i, acc_x, dt);vy_i = computeNewVelocity(vy_samp, vy_i, acc_y, dt);vtheta_i = computeNewVelocity(vtheta_samp, vtheta_i, acc_theta, dt);//计算位置x_i = computeNewXPosition(x_i, vx_i, vy_i, theta_i, dt);y_i = computeNewYPosition(y_i, vx_i, vy_i, theta_i, dt);theta_i = computeNewThetaPosition(theta_i, vtheta_i, dt);//对路径进行评分if (!heading_scoring_) {//cost = pdist_scale_ * path_dist + goal_dist * gdist_scale_ + occdist_scale_ * occ_cost;} else {cost = occdist_scale_ * occ_cost + pdist_scale_ * path_dist + 0.3 * heading_diff + goal_dist * gdist_scale_;}//这里的顺序与源码不同，我觉得总分来看更有组织性//该轨迹与全局路径的相对距离path_dist = path_map_(cell_x, cell_y).target_dist;//距离目标点距离goal_dist = goal_map_(cell_x, cell_y).target_dist;//离障碍物距离double footprint_cost = footprintCost(x_i, y_i, theta_i);occ_cost = std::max(std::max(occ_cost, footprint_cost), double(costmap_.getCost(cell_x, cell_y)));
}