1、move_bae框架
参考：http://wiki.ros.org/move_base
1.主体介绍：
move_base是ROS下关于机器人路径规划的中心枢纽。
它通过订阅激光雷达、map地图、amcl的定位等数据，然后规划出全局和局部路径，再将路径转化为机器人的速度信息，最终实现机器人导航。
首先左上角的amcl左上角的amcl模块是ROS的导航定位模块，amcl也叫自适应蒙特卡罗定位，amcl通过订阅scan、map和tf信息，发布出机器人的pose，以供move_base使用。
左下角odom，即机器人里程计信息。
右上角，map，顾名思义，我们需要的先验地图信息，一般通过slam建图后保存，map_server包通过解析slam建好的地图并发布出去。
右下角的传感器topic则在局部路径规划时起到作用，这部分就是costmap包起到的作用了，costmap为代价地图，目前主要的有inflation_layer、obstacle_layer、static_layer、voxel_layer四个plugins，分别为膨胀层、障碍物层、静态层和体素层。一般我们的全局路径需要静态层和膨胀层，因为全局规划应该只考虑到地图信息，所以一般都是静态的，而局部路径规划则需要考虑到实时的障碍物信息，所以需要障碍物层和膨胀层，
2.构成
它由如下三部分构成：
- 实现一个actionlib，用于设定目标位置。
- 连接global planer和local planner，用于实现导航。可以通过重新实现nav_core::BaseGlobalPlanner 和 nav_core::BaseLocalPlanner 的接口，修改global planer和local planner的导航策略
- 维护两个two costmaps，一个为global planer，另一个为local planner
- recovery_behaviors: 如果找不到一条到达目标的路径，它就会运行一些清理机制，把地图中一些无效障碍物清理掉，重新制定路径。recovery_behaviors默认策略：
recovery_behaviors默认策略：
2、move_base参数
1. costmap_common_params.yaml
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max_obstacle_height: 5.0 # assume something like an arm is mounted on top of the robot # Obstacle Cost Shaping (http://wiki.ros.org/costmap_2d/hydro/inflation) robot_radius: 0.20 # 机器人底盘形状，圆形使用robot_radius，非圆形使用footprint(kobuki: 0.18) # footprint: [[x0, y0], [x1, y1], ... [xn, yn]] # if the robot is not circular map_type: voxel obstacle_layer: enabled: true max_obstacle_height: 0.6 origin_z: 0.0 z_resolution: 0.2 z_voxels: 10 unknown_threshold: 15 mark_threshold: 0 combination_method: 1 track_unknown_space: true #true needed for disabling global path planning through unknown space obstacle_range: 2.5 raytrace_range: 3.0 origin_z: 0.0 z_resolution: 0.2 z_voxels: 10 publish_voxel_map: false observation_sources: scan bump scan: data_type: LaserScan topic: scan marking: true clearing: true min_obstacle_height: 0.25 max_obstacle_height: 0.35 inf_is_valid: true bump: data_type: PointCloud2 topic: mobile_base/sensors/bumper_pointcloud marking: true clearing: false min_obstacle_height: 0.0 max_obstacle_height: 0.15 #camera: #data_type: PointCloud2 #topic: camera/depth/points #marking: true #clearing: true #min_obstacle_height: 0.4 #max_obstacle_height: 0.8 # for debugging only, let's you see the entire voxel grid #cost_scaling_factor and inflation_radius were now moved to the inflation_layer ns inflation_layer: enabled: true cost_scaling_factor: 5.0 # exponential rate at which the obstacle cost drops off (default: 10) inflation_radius: 0.5 # max. distance from an obstacle at which costs are incurred for planning paths. static_layer: enabled: true
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2. global_costmap_params.yaml
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global_costmap: global_frame: /map robot_base_frame: /base_footprint update_frequency: 1.0 #基于传感器数据更新的频率，降低频率可以减少CPU负载。合理值在1～5 publish_frequency: 0.5 #对于global static map,通常不需要持续发布 static_map: true transform_tolerance: 0.5 #TF坐标系更新容忍的最大延迟,单位秒 plugins: - {name: static_layer, type: "costmap_2d::StaticLayer"} - {name: obstacle_layer, type: "costmap_2d::VoxelLayer"} - {name: inflation_layer, type: "costmap_2d::InflationLayer"}
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由于在全局路径规划中，我们只应该考虑静态地图，即我们之前slam建立好的地图，所以我们把static_map设为true，rolling_window设为false，再看plugins:有两个层，静态层和膨胀层，这样就完成了global的yaml的配置，如果你需要把障碍物层也添加到全局地图中，plugins加入，同时别忘了在下面声明数据来源。
3. local_costmap_params.yaml
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local_costmap: global_frame: odom robot_base_frame: /base_footprint update_frequency: 5.0 基于传感器数据更新的频率，降低频率可以减少CPU负载 publish_frequency: 2.0 发布地图的更新频率， 1HZ是足够好，除非机器人移动速度更快 static_map: false rolling_window: true width: 4.0 rolling局部地图宽 height: 4.0 rolling局部地图长 resolution: 0.05 分辨率要与使用地图YAML文件中描述的分辨率一致 transform_tolerance: 0.5 TF坐标系更新容忍的最大延迟,单位秒 plugins: - {name: obstacle_layer, type: "costmap_2d::VoxelLayer"} - {name: inflation_layer, type: "costmap_2d::InflationLayer"}
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这里由于是局部层，所以我们要把static_map设为fasle，我们并不希望使用静态的地图，因为这不利于我们的局部避障，rollung_window设为true。下面的plugins就为各个层的设置了，在局部路径规划中我们只需要考虑障碍物的信息就可以了，不需要考虑我们之前slam建立的静态地图，所有我们添加了两个图层，即障碍物层和障碍物的膨胀层，下面这个很重要，我们需要声明我们的障碍物层的数据来源，即scan，要把topic对应你的scan的topic，否则你可能发现rviz里的local_costmap没有数据，这就是你没有声明的原因。最后两行为设置膨胀层的半径大小。
4. dwa_local_planner_params.yaml
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DWAPlannerROS: # Robot Configuration Parameters - Kobuki max_vel_x: 0.5 # 0.55 min_vel_x: 0.0 max_vel_y: 0.0 # diff drive robot min_vel_y: 0.0 # diff drive robot max_trans_vel: 0.5 # choose slightly less than the base's capability min_trans_vel: 0.1 # this is the min trans velocity when there is negligible rotational velocity trans_stopped_vel: 0.1 # Warning! # do not set min_trans_vel to 0.0 otherwise dwa will always think translational velocities # are non-negligible and small in place rotational velocities will be created. max_rot_vel: 5.0 # choose slightly less than the base's capability min_rot_vel: 0.4 # this is the min angular velocity when there is negligible translational velocity rot_stopped_vel: 0.4 acc_lim_x: 1.0 # maximum is theoretically 2.0, but we acc_lim_theta: 2.0 acc_lim_y: 0.0 # diff drive robot # Goal Tolerance Parameters yaw_goal_tolerance: 0.3 # 0.05 xy_goal_tolerance: 0.15 # 0.10 # latch_xy_goal_tolerance: false # Forward Simulation Parameters sim_time: 1.0 # 1.7 vx_samples: 6 # 3 vy_samples: 1 # diff drive robot, there is only one sample vtheta_samples: 20 # 20 # Trajectory Scoring Parameters path_distance_bias: 64.0 # 32.0 - weighting for how much it should stick to the global path plan goal_distance_bias: 24.0 # 24.0 - wighting for how much it should attempt to reach its goal occdist_scale: 0.5 # 0.01 - weighting for how much the controller should avoid obstacles forward_point_distance: 0.325 # 0.325 - how far along to place an additional scoring point stop_time_buffer: 0.2 # 0.2 - amount of time a robot must stop in before colliding for a valid traj. scaling_speed: 0.25 # 0.25 - absolute velocity at which to start scaling the robot's footprint max_scaling_factor: 0.2 # 0.2 - how much to scale the robot's footprint when at speed. # Oscillation Prevention Parameters oscillation_reset_dist: 0.05 # 0.05 - how far to travel before resetting oscillation flags # Debugging publish_traj_pc : true publish_cost_grid_pc: true global_frame_id: odom # Differential-drive robot configuration - necessary? # holonomic_robot: false
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5. move_base_params.yaml
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shutdown_costmaps: false controller_frequency: 5.0 controller_patience: 3.0 planner_frequency: 1.0 planner_patience: 5.0 oscillation_timeout: 10.0 oscillation_distance: 0.2 # local planner - default is trajectory rollout base_local_planner: "dwa_local_planner/DWAPlannerROS" base_global_planner: "navfn/NavfnROS" #alternatives: global_planner/GlobalPlanner, carrot_planner/CarrotPlanner
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6. global_planner_params.yaml
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GlobalPlanner: # Also see: http://wiki.ros.org/global_planner old_navfn_behavior: false # Exactly mirror behavior of navfn, use defaults for other boolean parameters, default false use_quadratic: true # Use the quadratic approximation of the potential. Otherwise, use a simpler calculation, default true use_dijkstra: true # Use dijkstra's algorithm. Otherwise, A*, default true use_grid_path: false # Create a path that follows the grid boundaries. Otherwise, use a gradient descent method, default false allow_unknown: true # Allow planner to plan through unknown space, default true #Needs to have track_unknown_space: true in the obstacle / voxel layer (in costmap_commons_param) to work planner_window_x: 0.0 # default 0.0 planner_window_y: 0.0 # default 0.0 default_tolerance: 0.0 # If goal in obstacle, plan to the closest point in radius default_tolerance, default 0.0 publish_scale: 100 # Scale by which the published potential gets multiplied, default 100 planner_costmap_publish_frequency: 0.0 # default 0.0 lethal_cost: 253 # default 253 neutral_cost: 50 # default 50 cost_factor: 3.0 # Factor to multiply each cost from costmap by, default 3.0 publish_potential: true # Publish Potential Costmap (this is not like the navfn pointcloud2 potential), default true
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7. navfn_global_planner_params.yaml
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NavfnROS: visualize_potential: false #Publish potential for rviz as pointcloud2, not really helpful, default false allow_unknown: false #Specifies whether or not to allow navfn to create plans that traverse unknown space, default true #Needs to have track_unknown_space: true in the obstacle / voxel layer (in costmap_commons_param) to work planner_window_x: 0.0 #Specifies the x size of an optional window to restrict the planner to, default 0.0 planner_window_y: 0.0 #Specifies the y size of an optional window to restrict the planner to, default 0.0 default_tolerance: 0.0 #If the goal is in an obstacle, the planer will plan to the nearest point in the radius of default_tolerance, default 0.0 #The area is always searched, so could be slow for big values
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8. rviz显示
注意：要将rviz的fixed frame设成map，因为map才是global_frame_id。
rviz居然连粒子都可以显示，显示让我对amcl粒子更新有了更深刻的理解。
首先在参数表里面有几个比较重要的参数。
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~initial_pose_x (double, default: 0.0 meters) Initial pose mean (x), used to initialize filter with Gaussian distribution. ~initial_pose_y (double, default: 0.0 meters) Initial pose mean (y), used to initialize filter with Gaussian distribution. ~initial_pose_a (double, default: 0.0 radians) Initial pose mean (yaw), used to initialize filter with Gaussian distribution. ~initial_cov_xx (double, default: 0.5*0.5 meters) Initial pose covariance (x*x), used to initialize filter with Gaussian distribution. ~initial_cov_yy (double, default: 0.5*0.5 meters) Initial pose covariance (y*y), used to initialize filter with Gaussian distribution. ~initial_cov_aa (double, default: (π/12)*(π/12) radian) Initial pose covariance (yaw*yaw), used to initialize filter with Gaussian distribution.
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这个代表了你初始化粒子时粒子分布的一个状态，注意要把方差设的大一些，要不所有例子上来就是一坨的就没法玩了。
参考：http://blog.csdn.net/chenxingwangzi/article/details/50038413
local map在rviz 中Add-> by topic-> local map
2、move_base启动
1.move_base.launch配置。
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<launch> <include file="$(find turtlebot_navigation)/launch/includes/velocity_smoother.launch.xml"/> <include file="$(find turtlebot_navigation)/launch/includes/safety_controller.launch.xml"/> <arg name="odom_frame_id" default="odom"/> <arg name="base_frame_id" default="base_footprint"/> <arg name="global_frame_id" default="map"/> <arg name="odom_topic" default="odom" /> <arg name="laser_topic" default="scan" /> <arg name="custom_param_file" default="$(find turtlebot_navigation)/param/dummy.yaml"/> <node pkg="move_base" type="move_base" respawn="false" name="move_base" output="screen"> <rosparam file="$(find turtlebot_navigation)/param/costmap_common_params.yaml" command="load" ns="global_costmap" /> <rosparam file="$(find turtlebot_navigation)/param/costmap_common_params.yaml" command="load" ns="local_costmap" /> <rosparam file="$(find turtlebot_navigation)/param/local_costmap_params.yaml" command="load" /> <rosparam file="$(find turtlebot_navigation)/param/global_costmap_params.yaml" command="load" /> <rosparam file="$(find turtlebot_navigation)/param/dwa_local_planner_params.yaml" command="load" /> <rosparam file="$(find turtlebot_navigation)/param/move_base_params.yaml" command="load" /> <rosparam file="$(find turtlebot_navigation)/param/global_planner_params.yaml" command="load" /> <rosparam file="$(find turtlebot_navigation)/param/navfn_global_planner_params.yaml" command="load" /> <!-- external params file that could be loaded into the move_base namespace --> <rosparam file="$(arg custom_param_file)" command="load" /> <!-- reset frame_id parameters using user input data --> <param name="global_costmap/global_frame" value="$(arg global_frame_id)"/> <param name="global_costmap/robot_base_frame" value="$(arg base_frame_id)"/> <param name="local_costmap/global_frame" value="$(arg odom_frame_id)"/> <param name="local_costmap/robot_base_frame" value="$(arg base_frame_id)"/> <param name="DWAPlannerROS/global_frame_id" value="$(arg odom_frame_id)"/> <remap from="cmd_vel" to="navigation_velocity_smoother/raw_cmd_vel"/> <remap from="odom" to="$(arg odom_topic)"/> <remap from="scan" to="$(arg laser_topic)"/> </node> </launch>
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2. velocity_smoother.launch配置：
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<launch> <node pkg="nodelet" type="nodelet" name="navigation_velocity_smoother" args="load yocs_velocity_smoother/VelocitySmootherNodelet mobile_base_nodelet_manager"> <rosparam file="$(find turtlebot_bringup)/param/defaults/smoother.yaml" command="load"/> <remap from="navigation_velocity_smoother/smooth_cmd_vel" to="cmd_vel_mux/input/navi"/> <!-- Robot velocity feedbacks; use the default base configuration --> <remap from="navigation_velocity_smoother/odometry" to="odom"/> <remap from="navigation_velocity_smoother/robot_cmd_vel" to="mobile_base/commands/velocity"/> </node> </launch>
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3.safety_controller.launch配置：
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<launch> <node pkg="nodelet" type="nodelet" name="kobuki_safety_controller" args="load kobuki_safety_controller/SafetyControllerNodelet mobile_base_nodelet_manager"> <remap from="kobuki_safety_controller/cmd_vel" to="cmd_vel_mux/input/safety_controller"/> <remap from="kobuki_safety_controller/events/bumper" to="mobile_base/events/bumper"/> <remap from="kobuki_safety_controller/events/cliff" to="mobile_base/events/cliff"/> <remap from="kobuki_safety_controller/events/wheel_drop" to="mobile_base/events/wheel_drop"/> </node> </launch>
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