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Note: This tutorial assumes that you have completed the previous tutorials: beginner level tutorials.

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ゴールコールバックメソッドを使ったシンプルなアクションサーバを書く

Description: このチュートリアルは、simple_action_serverライブラリを使ってデータを平均化するアクションサーバについて扱います。この例はROSノードから入ってくるデータを処理またはデータに反応するためのアクションの使い方を示します。アクションサーバはROSノードからのデータを平均化し、ゴールはサンプル数で平均し、平均するサンプルの数であり、フィードバックはサンプル数、サンプルデータ、現在の平均、および現在の標準偏差で、結果はサンプルの要求された数の平均と標準偏差です。

Tutorial Level: INTERMEDIATE

Next Tutorial: スレッドのシンプルなアクションクライアントを書く

Contents

Creating the Action Messages
Writing a Simple Server
1. The Code
2. The Code Explained
Compiling and Running the Action
Sending a Goal to the Action Server

Creating the Action Messages

Before writing an action it is important to define the goal, result, and feedback messages. The action messages are generated automatically from the .action file, for more information on action files see the actionlib documentation. This file defines the type and format of the goal, result, and feedback topics for the action. Create learning_actionlib/action/Averaging.action in your favorite editor, and place the following inside it:

#goal definition
int32 samples
---
#result definition
float32 mean
float32 std_dev
---
#feedback
int32 sample
float32 data
float32 mean
float32 std_dev

To manually generate the message files from this file:

$ roscd learning_actionlib
$ rosrun actionlib genaction.py . Averaging.action

You will see:

```
Generating for action Averaging
```

To automatically generate the message files during the make process, add the following to CMakeLists.txt (before the rosbuild_init call)

rosbuild_find_ros_package(actionlib_msgs)
include(${actionlib_msgs_PACKAGE_PATH}/cmake/actionbuild.cmake)
genaction()

Writing a Simple Server

First, create learning_actionlib/src/averaging_server.cpp in your favorite editor, and place the following inside it:

The Code

   1 #include <ros/ros.h>
   2 #include <std_msgs/Float32.h>
   3 #include <actionlib/server/simple_action_server.h>
   4 #include <learning_actionlib/AveragingAction.h>
   5 
   6 class AveragingAction
   7 {
   8 public:
   9     
  10   AveragingAction(std::string name) : 
  11     as_(nh_, name, false),
  12     action_name_(name)
  13   {
  14     //register the goal and feeback callbacks
  15     as_.registerGoalCallback(boost::bind(&AveragingAction::goalCB, this));
  16     as_.registerPreemptCallback(boost::bind(&AveragingAction::preemptCB, this));
  17 
  18     //subscribe to the data topic of interest
  19     sub_ = nh_.subscribe("/random_number", 1, &AveragingAction::analysisCB, this);
  20     as_.start();
  21   }
  22 
  23   ~AveragingAction(void)
  24   {
  25   }
  26 
  27   void goalCB()
  28   {
  29     // reset helper variables
  30     data_count_ = 0;
  31     sum_ = 0;
  32     sum_sq_ = 0;
  33     // accept the new goal
  34     goal_ = as_.acceptNewGoal()->samples;
  35   }
  36 
  37   void preemptCB()
  38   {
  39     ROS_INFO("%s: Preempted", action_name_.c_str());
  40     // set the action state to preempted
  41     as_.setPreempted();
  42   }
  43 
  44   void analysisCB(const std_msgs::Float32::ConstPtr& msg)
  45   {
  46     // make sure that the action hasn't been canceled
  47     if (!as_.isActive())
  48       return;
  49     
  50     data_count_++;
  51     feedback_.sample = data_count_;
  52     feedback_.data = msg->data;
  53     //compute the std_dev and mean of the data 
  54     sum_ += msg->data;
  55     feedback_.mean = sum_ / data_count_;
  56     sum_sq_ += pow(msg->data, 2);
  57     feedback_.std_dev = sqrt(fabs((sum_sq_/data_count_) - pow(feedback_.mean, 2)));
  58     as_.publishFeedback(feedback_);
  59 
  60     if(data_count_ > goal_) 
  61     {
  62       result_.mean = feedback_.mean;
  63       result_.std_dev = feedback_.std_dev;
  64 
  65       if(result_.mean < 5.0)
  66       {
  67         ROS_INFO("%s: Aborted", action_name_.c_str());
  68         //set the action state to aborted
  69         as_.setAborted(result_);
  70       }
  71       else 
  72       {
  73         ROS_INFO("%s: Succeeded", action_name_.c_str());
  74         // set the action state to succeeded
  75         as_.setSucceeded(result_);
  76       }
  77     } 
  78   }
  79 
  80 protected:
  81     
  82   ros::NodeHandle nh_;
  83   actionlib::SimpleActionServer<learning_actionlib::AveragingAction> as_;
  84   std::string action_name_;
  85   int data_count_, goal_;
  86   float sum_, sum_sq_;
  87   learning_actionlib::AveragingFeedback feedback_;
  88   learning_actionlib::AveragingResult result_;
  89   ros::Subscriber sub_;
  90 };
  91 
  92 int main(int argc, char** argv)
  93 {
  94   ros::init(argc, argv, "averaging");
  95 
  96   AveragingAction averaging(ros::this_node::getName());
  97   ros::spin();
  98 
  99   return 0;
 100 }

The Code Explained

Now, let's break down the code piece by piece.

   1 #include <ros/ros.h>
   2 #include <std_msgs/Float32.h>
   3 #include <actionlib/server/simple_action_server.h>
   4

actionlib/server/simple_action_server.h is the action library used from implementing simple actions.

   4 #include <learning_actionlib/AveragingAction.h>
   5

This includes action message generated from the Averaging.action file show above. This is a header generated automatically from the AveragingAction.msg file. For more information on message definitions, see the msg page.

   6 class AveragingAction
   7 {
   8 public:
   9     
  10   AveragingAction(std::string name) : 
  11     as_(nh_, name, false),
  12     action_name_(name)
  13   {
  14     //register the goal and feeback callbacks
  15     as_.registerGoalCallback(boost::bind(&AveragingAction::goalCB, this));
  16     as_.registerPreemptCallback(boost::bind(&AveragingAction::preemptCB, this));

In the action constructor, an action server is created. The action server takes arguments of a node handle, name of the action, and optionally an executeCB. In this example the action server is created without the arguments for the executeCB. Instead the goal and preempt callbacks are registered with the action server in the constructor for the action after the action server has been constructed.

  18     //subscribe to the data topic of interest
  19     sub_ = nh_.subscribe("/random_number", 1, &AveragingAction::analysisCB, this);
  20     as_.start();
  21   }

Here a callback is setup for the data that will be processed by the action and the action server is started.

  27   void goalCB()
  28   {
  29     // reset helper variables
  30     data_count_ = 0;
  31     sum_ = 0;
  32     sum_sq_ = 0;
  33     // accept the new goal
  34     goal_ = as_.acceptNewGoal()->samples;
  35   }

Here is the goalCB function referenced in the constructor. The callback function returns nothing and takes no arguments. When the goalCB is called the action needs to accept the goal and store any important information. If you need to see the goal before you accept it, see the SimpleActionServer(ExecuteCallbackMethod) tutorial.

  37   void preemptCB()
  38   {
  39     ROS_INFO("%s: Preempted", action_name_.c_str());
  40     // set the action state to preempted
  41     as_.setPreempted();
  42   }

This action is event driven, the action code only runs when the callbacks occur therefore a preempt callback is created to ensure that the action responds promptly to a cancel request. The callback function takes no arguments and sets preempted on the action server.

  44   void analysisCB(const std_msgs::Float32::ConstPtr& msg)
  45   {
  46     // make sure that the action hasn't been canceled
  47     if (!as_.isActive())
  48       return;

Here the analysis callback takes the message format of the subscribed data channel and checks that the action is still in an active state before continuing to process the data.

  50     data_count_++;
  51     feedback_.sample = data_count_;
  52     feedback_.data = msg->data;
  53     //compute the std_dev and mean of the data 
  54     sum_ += msg->data;
  55     feedback_.mean = sum_ / data_count_;
  56     sum_sq_ += pow(msg->data, 2);
  57     feedback_.std_dev = sqrt(fabs((sum_sq_/data_count_) - pow(feedback_.mean, 2)));
  58     as_.publishFeedback(feedback_);

Here the relevant data is put into the feedback variable and then published on the feedback channel provided by the action server.

  60     if(data_count_ > goal_) 
  61     {
  62       result_.mean = feedback_.mean;
  63       result_.std_dev = feedback_.std_dev;
  64 
  65       if(result_.mean < 5.0)
  66       {
  67         ROS_INFO("%s: Aborted", action_name_.c_str());
  68         //set the action state to aborted
  69         as_.setAborted(result_);
  70       }
  71       else 
  72       {
  73         ROS_INFO("%s: Succeeded", action_name_.c_str());
  74         // set the action state to succeeded
  75         as_.setSucceeded(result_);
  76       }
  77     } 
  78   }

Once adequate data has been collected, the action server is set to success or failure. This will deactivate the action server and the analysisCB function will return immediately as discussed previously.

  80 protected:
  81     
  82   ros::NodeHandle nh_;
  83   actionlib::SimpleActionServer<learning_actionlib::AveragingAction> as_;
  84   std::string action_name_;
  85   int data_count_, goal_;
  86   float sum_, sum_sq_;
  87   learning_actionlib::AveragingFeedback feedback_;
  88   learning_actionlib::AveragingResult result_;
  89   ros::Subscriber sub_;
  90 };

These are the protected variables of the action class. The node handle is constructed and passed into the action server during construction of the action. The action server is constructed in the constructor of the action and has been discussed above. The feedback and result messages are created for publishing in the action. The subscriber is also constructed to save the node handle.

  92 int main(int argc, char** argv)
  93 {
  94   ros::init(argc, argv, "averaging");
  95 
  96   AveragingAction averaging(ros::this_node::getName());
  97   ros::spin();
  98 
  99   return 0;
 100 }

Finally the main function, creates the action and spins the node. The action will be running and waiting to receive goals.

Compiling and Running the Action

Add the following line to your CMakeLists.txt file:

rosbuild_add_executable(averaging_server src/averaging_server.cpp)

After you have made the executable, start a roscore in a new terminal

$ roscore

And then run the action server:

$ rosrun learning_actionlib averaging_server

You will see something similar to:

[ INFO] 1250790662.410962000: Started node [/averaging], pid [29267], bound on [aqy], xmlrpc port [39746], tcpros port [49573], logging to [~/ros/ros/log/averaging_29267.log], using [real] time

To check that your action is running properly list topics being published:

$ rostopic list -v

You will see something similar to:

Published topics:
 * /averaging/status [actionlib_msgs/GoalStatusArray] 1 publisher
 * /averaging/result [actionlib_tutorials/AveragingActionResult] 1 publisher
 * /averaging/feedback [actionlib_tutorials/AveragingActionFeedback] 1 publisher
 * /rosout [roslib/Log] 1 publisher
 * /rosout_agg [roslib/Log] 1 publisher

Subscribed topics:
 * /time [unknown type] 2 subscribers
 * /rosout [roslib/Log] 1 subscriber
 * /averaging/goal [unknown type] 1 subscriber
 * /averaging/cancel [unknown type] 1 subscriber

Alternatively you can look at the nodes:

$ rxgraph

This shows that your action server is publishing the feedback, status, and result channels as expected and subscribed to the goal and cancel channels as expected. The action server is up and running properly.

Sending a Goal to the Action Server

For the next step in using your action, you need to Ctrl-C the action server and write a threaded simple action client.

Contents

Creating the Action Messages
Writing a Simple Server
1. The Code
2. The Code Explained
Compiling and Running the Action
Sending a Goal to the Action Server

Creating the Action Messages

#goal definition
int32 samples
---
#result definition
float32 mean
float32 std_dev
---
#feedback
int32 sample
float32 data
float32 mean
float32 std_dev

To manually generate the message files from this file:

$ roscd learning_actionlib
$ rosrun actionlib genaction.py . Averaging.action

You will see:

```
Generating for action Averaging
```

To automatically generate the message files during the make process, add the following to CMakeLists.txt (before the rosbuild_init call)

rosbuild_find_ros_package(actionlib_msgs)
include(${actionlib_msgs_PACKAGE_PATH}/cmake/actionbuild.cmake)
genaction()

Writing a Simple Server

First, create learning_actionlib/src/averaging_server.cpp in your favorite editor, and place the following inside it:

The Code

   1 #include <ros/ros.h>
   2 #include <std_msgs/Float32.h>
   3 #include <actionlib/server/simple_action_server.h>
   4 #include <learning_actionlib/AveragingAction.h>
   5 
   6 class AveragingAction
   7 {
   8 public:
   9     
  10   AveragingAction(std::string name) : 
  11     as_(nh_, name, false),
  12     action_name_(name)
  13   {
  14     //register the goal and feeback callbacks
  15     as_.registerGoalCallback(boost::bind(&AveragingAction::goalCB, this));
  16     as_.registerPreemptCallback(boost::bind(&AveragingAction::preemptCB, this));
  17 
  18     //subscribe to the data topic of interest
  19     sub_ = nh_.subscribe("/random_number", 1, &AveragingAction::analysisCB, this);
  20     as_.start();
  21   }
  22 
  23   ~AveragingAction(void)
  24   {
  25   }
  26 
  27   void goalCB()
  28   {
  29     // reset helper variables
  30     data_count_ = 0;
  31     sum_ = 0;
  32     sum_sq_ = 0;
  33     // accept the new goal
  34     goal_ = as_.acceptNewGoal()->samples;
  35   }
  36 
  37   void preemptCB()
  38   {
  39     ROS_INFO("%s: Preempted", action_name_.c_str());
  40     // set the action state to preempted
  41     as_.setPreempted();
  42   }
  43 
  44   void analysisCB(const std_msgs::Float32::ConstPtr& msg)
  45   {
  46     // make sure that the action hasn't been canceled
  47     if (!as_.isActive())
  48       return;
  49     
  50     data_count_++;
  51     feedback_.sample = data_count_;
  52     feedback_.data = msg->data;
  53     //compute the std_dev and mean of the data 
  54     sum_ += msg->data;
  55     feedback_.mean = sum_ / data_count_;
  56     sum_sq_ += pow(msg->data, 2);
  57     feedback_.std_dev = sqrt(fabs((sum_sq_/data_count_) - pow(feedback_.mean, 2)));
  58     as_.publishFeedback(feedback_);
  59 
  60     if(data_count_ > goal_) 
  61     {
  62       result_.mean = feedback_.mean;
  63       result_.std_dev = feedback_.std_dev;
  64 
  65       if(result_.mean < 5.0)
  66       {
  67         ROS_INFO("%s: Aborted", action_name_.c_str());
  68         //set the action state to aborted
  69         as_.setAborted(result_);
  70       }
  71       else 
  72       {
  73         ROS_INFO("%s: Succeeded", action_name_.c_str());
  74         // set the action state to succeeded
  75         as_.setSucceeded(result_);
  76       }
  77     } 
  78   }
  79 
  80 protected:
  81     
  82   ros::NodeHandle nh_;
  83   actionlib::SimpleActionServer<learning_actionlib::AveragingAction> as_;
  84   std::string action_name_;
  85   int data_count_, goal_;
  86   float sum_, sum_sq_;
  87   learning_actionlib::AveragingFeedback feedback_;
  88   learning_actionlib::AveragingResult result_;
  89   ros::Subscriber sub_;
  90 };
  91 
  92 int main(int argc, char** argv)
  93 {
  94   ros::init(argc, argv, "averaging");
  95 
  96   AveragingAction averaging(ros::this_node::getName());
  97   ros::spin();
  98 
  99   return 0;
 100 }

The Code Explained

Now, let's break down the code piece by piece.

   1 #include <ros/ros.h>
   2 #include <std_msgs/Float32.h>
   3 #include <actionlib/server/simple_action_server.h>
   4

actionlib/server/simple_action_server.h is the action library used from implementing simple actions.

   4 #include <learning_actionlib/AveragingAction.h>
   5

   6 class AveragingAction
   7 {
   8 public:
   9     
  10   AveragingAction(std::string name) : 
  11     as_(nh_, name, false),
  12     action_name_(name)
  13   {
  14     //register the goal and feeback callbacks
  15     as_.registerGoalCallback(boost::bind(&AveragingAction::goalCB, this));
  16     as_.registerPreemptCallback(boost::bind(&AveragingAction::preemptCB, this));

  18     //subscribe to the data topic of interest
  19     sub_ = nh_.subscribe("/random_number", 1, &AveragingAction::analysisCB, this);
  20     as_.start();
  21   }

Here a callback is setup for the data that will be processed by the action and the action server is started.

  27   void goalCB()
  28   {
  29     // reset helper variables
  30     data_count_ = 0;
  31     sum_ = 0;
  32     sum_sq_ = 0;
  33     // accept the new goal
  34     goal_ = as_.acceptNewGoal()->samples;
  35   }

  37   void preemptCB()
  38   {
  39     ROS_INFO("%s: Preempted", action_name_.c_str());
  40     // set the action state to preempted
  41     as_.setPreempted();
  42   }

  44   void analysisCB(const std_msgs::Float32::ConstPtr& msg)
  45   {
  46     // make sure that the action hasn't been canceled
  47     if (!as_.isActive())
  48       return;

Here the analysis callback takes the message format of the subscribed data channel and checks that the action is still in an active state before continuing to process the data.

  50     data_count_++;
  51     feedback_.sample = data_count_;
  52     feedback_.data = msg->data;
  53     //compute the std_dev and mean of the data 
  54     sum_ += msg->data;
  55     feedback_.mean = sum_ / data_count_;
  56     sum_sq_ += pow(msg->data, 2);
  57     feedback_.std_dev = sqrt(fabs((sum_sq_/data_count_) - pow(feedback_.mean, 2)));
  58     as_.publishFeedback(feedback_);

Here the relevant data is put into the feedback variable and then published on the feedback channel provided by the action server.

  60     if(data_count_ > goal_) 
  61     {
  62       result_.mean = feedback_.mean;
  63       result_.std_dev = feedback_.std_dev;
  64 
  65       if(result_.mean < 5.0)
  66       {
  67         ROS_INFO("%s: Aborted", action_name_.c_str());
  68         //set the action state to aborted
  69         as_.setAborted(result_);
  70       }
  71       else 
  72       {
  73         ROS_INFO("%s: Succeeded", action_name_.c_str());
  74         // set the action state to succeeded
  75         as_.setSucceeded(result_);
  76       }
  77     } 
  78   }

  80 protected:
  81     
  82   ros::NodeHandle nh_;
  83   actionlib::SimpleActionServer<learning_actionlib::AveragingAction> as_;
  84   std::string action_name_;
  85   int data_count_, goal_;
  86   float sum_, sum_sq_;
  87   learning_actionlib::AveragingFeedback feedback_;
  88   learning_actionlib::AveragingResult result_;
  89   ros::Subscriber sub_;
  90 };

  92 int main(int argc, char** argv)
  93 {
  94   ros::init(argc, argv, "averaging");
  95 
  96   AveragingAction averaging(ros::this_node::getName());
  97   ros::spin();
  98 
  99   return 0;
 100 }

Finally the main function, creates the action and spins the node. The action will be running and waiting to receive goals.

Compiling and Running the Action

Add the following line to your CMakeLists.txt file:

rosbuild_add_executable(averaging_server src/averaging_server.cpp)

After you have made the executable, start a roscore in a new terminal

$ roscore

And then run the action server:

$ rosrun learning_actionlib averaging_server

You will see something similar to:

[ INFO] 1250790662.410962000: Started node [/averaging], pid [29267], bound on [aqy], xmlrpc port [39746], tcpros port [49573], logging to [~/ros/ros/log/averaging_29267.log], using [real] time

To check that your action is running properly list topics being published:

$ rostopic list -v

You will see something similar to:

Published topics:
 * /averaging/status [actionlib_msgs/GoalStatusArray] 1 publisher
 * /averaging/result [actionlib_tutorials/AveragingActionResult] 1 publisher
 * /averaging/feedback [actionlib_tutorials/AveragingActionFeedback] 1 publisher
 * /rosout [roslib/Log] 1 publisher
 * /rosout_agg [roslib/Log] 1 publisher

Subscribed topics:
 * /time [unknown type] 2 subscribers
 * /rosout [roslib/Log] 1 subscriber
 * /averaging/goal [unknown type] 1 subscriber
 * /averaging/cancel [unknown type] 1 subscriber

Alternatively you can look at the nodes:

$ rxgraph

Sending a Goal to the Action Server

For the next step in using your action, you need to Ctrl-C the action server and write a threaded simple action client.

Contents

Creating the Action Messages
Writing a Simple Server
1. The Code
2. The Code Explained
Compiling and Running the Action
Sending a Goal to the Action Server

Creating the Action Messages

#goal definition
int32 samples
---
#result definition
float32 mean
float32 std_dev
---
#feedback
int32 sample
float32 data
float32 mean
float32 std_dev

To manually generate the message files from this file:

$ roscd learning_actionlib
$ rosrun actionlib_msgs genaction.py . Averaging.action

You will see:

```
Generating for action Averaging
```

To automatically generate the message files during the make process, add the following to CMakeLists.txt (before the rosbuild_init call)

rosbuild_find_ros_package(actionlib_msgs)
include(${actionlib_msgs_PACKAGE_PATH}/cmake/actionbuild.cmake)
genaction()

Writing a Simple Server

First, create learning_actionlib/src/averaging_server.cpp in your favorite editor, and place the following inside it:

The Code

   1 #include <ros/ros.h>
   2 #include <std_msgs/Float32.h>
   3 #include <actionlib/server/simple_action_server.h>
   4 #include <learning_actionlib/AveragingAction.h>
   5 
   6 class AveragingAction
   7 {
   8 public:
   9     
  10   AveragingAction(std::string name) : 
  11     as_(nh_, name, false),
  12     action_name_(name)
  13   {
  14     //register the goal and feeback callbacks
  15     as_.registerGoalCallback(boost::bind(&AveragingAction::goalCB, this));
  16     as_.registerPreemptCallback(boost::bind(&AveragingAction::preemptCB, this));
  17 
  18     //subscribe to the data topic of interest
  19     sub_ = nh_.subscribe("/random_number", 1, &AveragingAction::analysisCB, this);
  20     as_.start();
  21   }
  22 
  23   ~AveragingAction(void)
  24   {
  25   }
  26 
  27   void goalCB()
  28   {
  29     // reset helper variables
  30     data_count_ = 0;
  31     sum_ = 0;
  32     sum_sq_ = 0;
  33     // accept the new goal
  34     goal_ = as_.acceptNewGoal()->samples;
  35   }
  36 
  37   void preemptCB()
  38   {
  39     ROS_INFO("%s: Preempted", action_name_.c_str());
  40     // set the action state to preempted
  41     as_.setPreempted();
  42   }
  43 
  44   void analysisCB(const std_msgs::Float32::ConstPtr& msg)
  45   {
  46     // make sure that the action hasn't been canceled
  47     if (!as_.isActive())
  48       return;
  49     
  50     data_count_++;
  51     feedback_.sample = data_count_;
  52     feedback_.data = msg->data;
  53     //compute the std_dev and mean of the data 
  54     sum_ += msg->data;
  55     feedback_.mean = sum_ / data_count_;
  56     sum_sq_ += pow(msg->data, 2);
  57     feedback_.std_dev = sqrt(fabs((sum_sq_/data_count_) - pow(feedback_.mean, 2)));
  58     as_.publishFeedback(feedback_);
  59 
  60     if(data_count_ > goal_) 
  61     {
  62       result_.mean = feedback_.mean;
  63       result_.std_dev = feedback_.std_dev;
  64 
  65       if(result_.mean < 5.0)
  66       {
  67         ROS_INFO("%s: Aborted", action_name_.c_str());
  68         //set the action state to aborted
  69         as_.setAborted(result_);
  70       }
  71       else 
  72       {
  73         ROS_INFO("%s: Succeeded", action_name_.c_str());
  74         // set the action state to succeeded
  75         as_.setSucceeded(result_);
  76       }
  77     } 
  78   }
  79 
  80 protected:
  81     
  82   ros::NodeHandle nh_;
  83   actionlib::SimpleActionServer<learning_actionlib::AveragingAction> as_;
  84   std::string action_name_;
  85   int data_count_, goal_;
  86   float sum_, sum_sq_;
  87   learning_actionlib::AveragingFeedback feedback_;
  88   learning_actionlib::AveragingResult result_;
  89   ros::Subscriber sub_;
  90 };
  91 
  92 int main(int argc, char** argv)
  93 {
  94   ros::init(argc, argv, "averaging");
  95 
  96   AveragingAction averaging(ros::this_node::getName());
  97   ros::spin();
  98 
  99   return 0;
 100 }

The Code Explained

Now, let's break down the code piece by piece.

   1 #include <ros/ros.h>
   2 #include <std_msgs/Float32.h>
   3 #include <actionlib/server/simple_action_server.h>
   4

actionlib/server/simple_action_server.h is the action library used from implementing simple actions.

   4 #include <learning_actionlib/AveragingAction.h>
   5

   6 class AveragingAction
   7 {
   8 public:
   9     
  10   AveragingAction(std::string name) : 
  11     as_(nh_, name, false),
  12     action_name_(name)
  13   {
  14     //register the goal and feeback callbacks
  15     as_.registerGoalCallback(boost::bind(&AveragingAction::goalCB, this));
  16     as_.registerPreemptCallback(boost::bind(&AveragingAction::preemptCB, this));

  18     //subscribe to the data topic of interest
  19     sub_ = nh_.subscribe("/random_number", 1, &AveragingAction::analysisCB, this);
  20     as_.start();
  21   }

Here a callback is setup for the data that will be processed by the action and the action server is started.

  27   void goalCB()
  28   {
  29     // reset helper variables
  30     data_count_ = 0;
  31     sum_ = 0;
  32     sum_sq_ = 0;
  33     // accept the new goal
  34     goal_ = as_.acceptNewGoal()->samples;
  35   }

  37   void preemptCB()
  38   {
  39     ROS_INFO("%s: Preempted", action_name_.c_str());
  40     // set the action state to preempted
  41     as_.setPreempted();
  42   }

  44   void analysisCB(const std_msgs::Float32::ConstPtr& msg)
  45   {
  46     // make sure that the action hasn't been canceled
  47     if (!as_.isActive())
  48       return;

Here the analysis callback takes the message format of the subscribed data channel and checks that the action is still in an active state before continuing to process the data.

  50     data_count_++;
  51     feedback_.sample = data_count_;
  52     feedback_.data = msg->data;
  53     //compute the std_dev and mean of the data 
  54     sum_ += msg->data;
  55     feedback_.mean = sum_ / data_count_;
  56     sum_sq_ += pow(msg->data, 2);
  57     feedback_.std_dev = sqrt(fabs((sum_sq_/data_count_) - pow(feedback_.mean, 2)));
  58     as_.publishFeedback(feedback_);

Here the relevant data is put into the feedback variable and then published on the feedback channel provided by the action server.

  60     if(data_count_ > goal_) 
  61     {
  62       result_.mean = feedback_.mean;
  63       result_.std_dev = feedback_.std_dev;
  64 
  65       if(result_.mean < 5.0)
  66       {
  67         ROS_INFO("%s: Aborted", action_name_.c_str());
  68         //set the action state to aborted
  69         as_.setAborted(result_);
  70       }
  71       else 
  72       {
  73         ROS_INFO("%s: Succeeded", action_name_.c_str());
  74         // set the action state to succeeded
  75         as_.setSucceeded(result_);
  76       }
  77     } 
  78   }

  80 protected:
  81     
  82   ros::NodeHandle nh_;
  83   actionlib::SimpleActionServer<learning_actionlib::AveragingAction> as_;
  84   std::string action_name_;
  85   int data_count_, goal_;
  86   float sum_, sum_sq_;
  87   learning_actionlib::AveragingFeedback feedback_;
  88   learning_actionlib::AveragingResult result_;
  89   ros::Subscriber sub_;
  90 };

  92 int main(int argc, char** argv)
  93 {
  94   ros::init(argc, argv, "averaging");
  95 
  96   AveragingAction averaging(ros::this_node::getName());
  97   ros::spin();
  98 
  99   return 0;
 100 }

Finally the main function, creates the action and spins the node. The action will be running and waiting to receive goals.

Compiling and Running the Action

Add the following line to your CMakeLists.txt file:

rosbuild_add_executable(averaging_server src/averaging_server.cpp)

After you have made the executable, start a roscore in a new terminal

$ roscore

And then run the action server:

$ rosrun learning_actionlib averaging_server

You will see something similar to:

[ INFO] 1250790662.410962000: Started node [/averaging], pid [29267], bound on [aqy], xmlrpc port [39746], tcpros port [49573], logging to [~/ros/ros/log/averaging_29267.log], using [real] time

To check that your action is running properly list topics being published:

$ rostopic list -v

You will see something similar to:

Published topics:
 * /averaging/status [actionlib_msgs/GoalStatusArray] 1 publisher
 * /averaging/result [actionlib_tutorials/AveragingActionResult] 1 publisher
 * /averaging/feedback [actionlib_tutorials/AveragingActionFeedback] 1 publisher
 * /rosout [roslib/Log] 1 publisher
 * /rosout_agg [roslib/Log] 1 publisher

Subscribed topics:
 * /time [unknown type] 2 subscribers
 * /rosout [roslib/Log] 1 subscriber
 * /averaging/goal [unknown type] 1 subscriber
 * /averaging/cancel [unknown type] 1 subscriber

Alternatively you can look at the nodes:

$ rxgraph

Sending a Goal to the Action Server

For the next step in using your action, you need to Ctrl-C the action server and write a threaded simple action client.

Contents

Creating the Action Messages
Writing a Simple Server
1. The Code
2. The Code Explained
Compiling and Running the Action
Sending a Goal to the Action Server

Creating the Action Messages

#goal definition
int32 samples
---
#result definition
float32 mean
float32 std_dev
---
#feedback
int32 sample
float32 data
float32 mean
float32 std_dev

To manually generate the message files from this file:

$ roscd learning_actionlib
$ rosrun actionlib_msgs genaction.py -o msg/ action/Averaging.action

You will see:

```
Generating for action Averaging
```

To automatically generate the message files during the make process, add the following to CMakeLists.txt (before the rosbuild_init call)

rosbuild_find_ros_package(actionlib_msgs)
include(${actionlib_msgs_PACKAGE_PATH}/cmake/actionbuild.cmake)
genaction()

Writing a Simple Server

First, create learning_actionlib/src/averaging_server.cpp in your favorite editor, and place the following inside it:

The Code

   1 #include <ros/ros.h>
   2 #include <std_msgs/Float32.h>
   3 #include <actionlib/server/simple_action_server.h>
   4 #include <learning_actionlib/AveragingAction.h>
   5 
   6 class AveragingAction
   7 {
   8 public:
   9     
  10   AveragingAction(std::string name) : 
  11     as_(nh_, name, false),
  12     action_name_(name)
  13   {
  14     //register the goal and feeback callbacks
  15     as_.registerGoalCallback(boost::bind(&AveragingAction::goalCB, this));
  16     as_.registerPreemptCallback(boost::bind(&AveragingAction::preemptCB, this));
  17 
  18     //subscribe to the data topic of interest
  19     sub_ = nh_.subscribe("/random_number", 1, &AveragingAction::analysisCB, this);
  20     as_.start();
  21   }
  22 
  23   ~AveragingAction(void)
  24   {
  25   }
  26 
  27   void goalCB()
  28   {
  29     // reset helper variables
  30     data_count_ = 0;
  31     sum_ = 0;
  32     sum_sq_ = 0;
  33     // accept the new goal
  34     goal_ = as_.acceptNewGoal()->samples;
  35   }
  36 
  37   void preemptCB()
  38   {
  39     ROS_INFO("%s: Preempted", action_name_.c_str());
  40     // set the action state to preempted
  41     as_.setPreempted();
  42   }
  43 
  44   void analysisCB(const std_msgs::Float32::ConstPtr& msg)
  45   {
  46     // make sure that the action hasn't been canceled
  47     if (!as_.isActive())
  48       return;
  49     
  50     data_count_++;
  51     feedback_.sample = data_count_;
  52     feedback_.data = msg->data;
  53     //compute the std_dev and mean of the data 
  54     sum_ += msg->data;
  55     feedback_.mean = sum_ / data_count_;
  56     sum_sq_ += pow(msg->data, 2);
  57     feedback_.std_dev = sqrt(fabs((sum_sq_/data_count_) - pow(feedback_.mean, 2)));
  58     as_.publishFeedback(feedback_);
  59 
  60     if(data_count_ > goal_) 
  61     {
  62       result_.mean = feedback_.mean;
  63       result_.std_dev = feedback_.std_dev;
  64 
  65       if(result_.mean < 5.0)
  66       {
  67         ROS_INFO("%s: Aborted", action_name_.c_str());
  68         //set the action state to aborted
  69         as_.setAborted(result_);
  70       }
  71       else 
  72       {
  73         ROS_INFO("%s: Succeeded", action_name_.c_str());
  74         // set the action state to succeeded
  75         as_.setSucceeded(result_);
  76       }
  77     } 
  78   }
  79 
  80 protected:
  81     
  82   ros::NodeHandle nh_;
  83   actionlib::SimpleActionServer<learning_actionlib::AveragingAction> as_;
  84   std::string action_name_;
  85   int data_count_, goal_;
  86   float sum_, sum_sq_;
  87   learning_actionlib::AveragingFeedback feedback_;
  88   learning_actionlib::AveragingResult result_;
  89   ros::Subscriber sub_;
  90 };
  91 
  92 int main(int argc, char** argv)
  93 {
  94   ros::init(argc, argv, "averaging");
  95 
  96   AveragingAction averaging(ros::this_node::getName());
  97   ros::spin();
  98 
  99   return 0;
 100 }

The Code Explained

Now, let's break down the code piece by piece.

   1 #include <ros/ros.h>
   2 #include <std_msgs/Float32.h>
   3 #include <actionlib/server/simple_action_server.h>
   4

actionlib/server/simple_action_server.h is the action library used from implementing simple actions.

   4 #include <learning_actionlib/AveragingAction.h>
   5

   6 class AveragingAction
   7 {
   8 public:
   9     
  10   AveragingAction(std::string name) : 
  11     as_(nh_, name, false),
  12     action_name_(name)
  13   {
  14     //register the goal and feeback callbacks
  15     as_.registerGoalCallback(boost::bind(&AveragingAction::goalCB, this));
  16     as_.registerPreemptCallback(boost::bind(&AveragingAction::preemptCB, this));

  18     //subscribe to the data topic of interest
  19     sub_ = nh_.subscribe("/random_number", 1, &AveragingAction::analysisCB, this);
  20     as_.start();
  21   }

Here a callback is setup for the data that will be processed by the action and the action server is started.

  27   void goalCB()
  28   {
  29     // reset helper variables
  30     data_count_ = 0;
  31     sum_ = 0;
  32     sum_sq_ = 0;
  33     // accept the new goal
  34     goal_ = as_.acceptNewGoal()->samples;
  35   }

  37   void preemptCB()
  38   {
  39     ROS_INFO("%s: Preempted", action_name_.c_str());
  40     // set the action state to preempted
  41     as_.setPreempted();
  42   }

  44   void analysisCB(const std_msgs::Float32::ConstPtr& msg)
  45   {
  46     // make sure that the action hasn't been canceled
  47     if (!as_.isActive())
  48       return;

Here the analysis callback takes the message format of the subscribed data channel and checks that the action is still in an active state before continuing to process the data.

  50     data_count_++;
  51     feedback_.sample = data_count_;
  52     feedback_.data = msg->data;
  53     //compute the std_dev and mean of the data 
  54     sum_ += msg->data;
  55     feedback_.mean = sum_ / data_count_;
  56     sum_sq_ += pow(msg->data, 2);
  57     feedback_.std_dev = sqrt(fabs((sum_sq_/data_count_) - pow(feedback_.mean, 2)));
  58     as_.publishFeedback(feedback_);

Here the relevant data is put into the feedback variable and then published on the feedback channel provided by the action server.

  60     if(data_count_ > goal_) 
  61     {
  62       result_.mean = feedback_.mean;
  63       result_.std_dev = feedback_.std_dev;
  64 
  65       if(result_.mean < 5.0)
  66       {
  67         ROS_INFO("%s: Aborted", action_name_.c_str());
  68         //set the action state to aborted
  69         as_.setAborted(result_);
  70       }
  71       else 
  72       {
  73         ROS_INFO("%s: Succeeded", action_name_.c_str());
  74         // set the action state to succeeded
  75         as_.setSucceeded(result_);
  76       }
  77     } 
  78   }

  80 protected:
  81     
  82   ros::NodeHandle nh_;
  83   actionlib::SimpleActionServer<learning_actionlib::AveragingAction> as_;
  84   std::string action_name_;
  85   int data_count_, goal_;
  86   float sum_, sum_sq_;
  87   learning_actionlib::AveragingFeedback feedback_;
  88   learning_actionlib::AveragingResult result_;
  89   ros::Subscriber sub_;
  90 };

  92 int main(int argc, char** argv)
  93 {
  94   ros::init(argc, argv, "averaging");
  95 
  96   AveragingAction averaging(ros::this_node::getName());
  97   ros::spin();
  98 
  99   return 0;
 100 }

Finally the main function, creates the action and spins the node. The action will be running and waiting to receive goals.

Compiling and Running the Action

Add the following line to your CMakeLists.txt file:

rosbuild_add_executable(averaging_server src/averaging_server.cpp)

After you have made the executable, start a roscore in a new terminal

$ roscore

And then run the action server:

$ rosrun learning_actionlib averaging_server

You will see something similar to:

[ INFO] 1250790662.410962000: Started node [/averaging], pid [29267], bound on [aqy], xmlrpc port [39746], tcpros port [49573], logging to [~/ros/ros/log/averaging_29267.log], using [real] time

To check that your action is running properly list topics being published:

$ rostopic list -v

You will see something similar to:

Published topics:
 * /averaging/status [actionlib_msgs/GoalStatusArray] 1 publisher
 * /averaging/result [actionlib_tutorials/AveragingActionResult] 1 publisher
 * /averaging/feedback [actionlib_tutorials/AveragingActionFeedback] 1 publisher
 * /rosout [roslib/Log] 1 publisher
 * /rosout_agg [roslib/Log] 1 publisher

Subscribed topics:
 * /time [unknown type] 2 subscribers
 * /rosout [roslib/Log] 1 subscriber
 * /averaging/goal [unknown type] 1 subscriber
 * /averaging/cancel [unknown type] 1 subscriber

Alternatively you can look at the nodes:

$ rxgraph

Sending a Goal to the Action Server

For the next step in using your action, you need to Ctrl-C the action server and write a threaded simple action client.

Contents

アクションメッセージを生成する
シンプルなサーバを書く
1. コード
2. コード解説
アクションをコンパイルし実行する
ゴールをアクションサーバに送る

アクションメッセージを生成する

actionを書く前に、ゴール、結果、およびフィードバックのメッセージを定義することは重要です。actionメッセージは.actionファイルから自動的に生成されます。actionファイルの詳細については、actionlibドキュメンテーションにあります。このファイルはactionについてのゴール、結果、およびフィードバックのトピックのタイプとフォーマットを定義します。エディタでlearning_actionlib/action/Averaging.actionを作成し、以下を書き込みます：

#goal definition
int32 samples
---
#result definition
float32 mean
float32 std_dev
---
#feedback
int32 sample
float32 data
float32 mean
float32 std_dev

このファイルから手動でメッセージファイルを生成するには：

$ roscd learning_actionlib
$ rosrun actionlib_msgs genaction.py -o msg/ action/Averaging.action

以下の出力が見えるでしょう：

```
Generating for action Averaging
```

makeプロセスで自動的にメッセージファイルを生成するために、以下をCMakeLists.txtに追加します：

find_package(catkin REQUIRED COMPONENTS actionlib std_msgs message_generation) 
add_action_files(DIRECTORY action FILES Averaging.action)
generate_messages(DEPENDENCIES std_msgs actionlib_msgs)

そして、catkin_makeを実行します:

$ cd %TOP_DIR_YOUR_CATKIN_WORKSPACE%
$ catkin_make

シンプルなサーバを書く

まず、エディタでlearning_actionlib/src/averaging_server.cppを作成し、以下を書き込みます：

コード

   1 #include <ros/ros.h>
   2 #include <std_msgs/Float32.h>
   3 #include <actionlib/server/simple_action_server.h>
   4 #include <learning_actionlib/AveragingAction.h>
   5 
   6 class AveragingAction
   7 {
   8 public:
   9     
  10   AveragingAction(std::string name) : 
  11     as_(nh_, name, false),
  12     action_name_(name)
  13   {
  14     //register the goal and feeback callbacks
  15     as_.registerGoalCallback(boost::bind(&AveragingAction::goalCB, this));
  16     as_.registerPreemptCallback(boost::bind(&AveragingAction::preemptCB, this));
  17 
  18     //subscribe to the data topic of interest
  19     sub_ = nh_.subscribe("/random_number", 1, &AveragingAction::analysisCB, this);
  20     as_.start();
  21   }
  22 
  23   ~AveragingAction(void)
  24   {
  25   }
  26 
  27   void goalCB()
  28   {
  29     // reset helper variables
  30     data_count_ = 0;
  31     sum_ = 0;
  32     sum_sq_ = 0;
  33     // accept the new goal
  34     goal_ = as_.acceptNewGoal()->samples;
  35   }
  36 
  37   void preemptCB()
  38   {
  39     ROS_INFO("%s: Preempted", action_name_.c_str());
  40     // set the action state to preempted
  41     as_.setPreempted();
  42   }
  43 
  44   void analysisCB(const std_msgs::Float32::ConstPtr& msg)
  45   {
  46     // make sure that the action hasn't been canceled
  47     if (!as_.isActive())
  48       return;
  49     
  50     data_count_++;
  51     feedback_.sample = data_count_;
  52     feedback_.data = msg->data;
  53     //compute the std_dev and mean of the data 
  54     sum_ += msg->data;
  55     feedback_.mean = sum_ / data_count_;
  56     sum_sq_ += pow(msg->data, 2);
  57     feedback_.std_dev = sqrt(fabs((sum_sq_/data_count_) - pow(feedback_.mean, 2)));
  58     as_.publishFeedback(feedback_);
  59 
  60     if(data_count_ > goal_) 
  61     {
  62       result_.mean = feedback_.mean;
  63       result_.std_dev = feedback_.std_dev;
  64 
  65       if(result_.mean < 5.0)
  66       {
  67         ROS_INFO("%s: Aborted", action_name_.c_str());
  68         //set the action state to aborted
  69         as_.setAborted(result_);
  70       }
  71       else 
  72       {
  73         ROS_INFO("%s: Succeeded", action_name_.c_str());
  74         // set the action state to succeeded
  75         as_.setSucceeded(result_);
  76       }
  77     } 
  78   }
  79 
  80 protected:
  81     
  82   ros::NodeHandle nh_;
  83   actionlib::SimpleActionServer<learning_actionlib::AveragingAction> as_;
  84   std::string action_name_;
  85   int data_count_, goal_;
  86   float sum_, sum_sq_;
  87   learning_actionlib::AveragingFeedback feedback_;
  88   learning_actionlib::AveragingResult result_;
  89   ros::Subscriber sub_;
  90 };
  91 
  92 int main(int argc, char** argv)
  93 {
  94   ros::init(argc, argv, "averaging");
  95 
  96   AveragingAction averaging(ros::this_node::getName());
  97   ros::spin();
  98 
  99   return 0;
 100 }

コード解説

では、一つずつコードを分析していきましょう。

   1 #include <ros/ros.h>
   2 #include <std_msgs/Float32.h>
   3 #include <actionlib/server/simple_action_server.h>
   4

actionlib/server/simple_action_server.hはシンプルなactionを実装する際に使えるactionライブラリです。

   4 #include <learning_actionlib/AveragingAction.h>
   5

これは上で示したAveraging.actionファイルで生成されたactionメッセージをインクルードします。これはAveragingAction.msg ファイルから自動的に生成されたヘッダファイルです。メッセージ定義の詳細は msg ページにあります。

   6 class AveragingAction
   7 {
   8 public:
   9     
  10   AveragingAction(std::string name) : 
  11     as_(nh_, name, false),
  12     action_name_(name)
  13   {
  14     //register the goal and feeback callbacks
  15     as_.registerGoalCallback(boost::bind(&AveragingAction::goalCB, this));
  16     as_.registerPreemptCallback(boost::bind(&AveragingAction::preemptCB, this));

actionのコンストラクタの中で、actionサーバは生成されます。actionサーバは、ノードハンドル、actionの名前、オプションでexecuteCB(コールバック)を引数として取ります。この例では、actionサーバはexecuteCBの引数無しで生成されます。そのかわりにゴールとpreemptコールバックは、actionサーバが構築された後にactionのコンストラクタの中でactionサーバに登録されます。

  18     //subscribe to the data topic of interest
  19     sub_ = nh_.subscribe("/random_number", 1, &AveragingAction::analysisCB, this);
  20     as_.start();
  21   }

ここではactionによって処理されるデータ用にコールバックを設定し、そしてactionサーバを開始しています。

  27   void goalCB()
  28   {
  29     // reset helper variables
  30     data_count_ = 0;
  31     sum_ = 0;
  32     sum_sq_ = 0;
  33     // accept the new goal
  34     goal_ = as_.acceptNewGoal()->samples;
  35   }

これはgoalCB関数でコンストラクタ内で参照されます。このコールバック関数は戻り値が無く、引数も取りません。goalCBが呼ばれる時actionはゴールを受け付け、重要な情報を全て格納します。もし先にゴールを見る必要がある場合は、SimpleActionServer(ExecuteCallbackMethod) チュートリアルを参考にしてください。

  37   void preemptCB()
  38   {
  39     ROS_INFO("%s: Preempted", action_name_.c_str());
  40     // set the action state to preempted
  41     as_.setPreempted();
  42   }

このactionはイベンドドリブンで、actionのコードはコールバックが起きた時だけ走るので、preemptコールバックは、actionが取り消し要求へ即座に応答することを保証するために生成されます。コールバック関数は引数を取らず、actionサーバの状態をpreemptedに設定します。

  44   void analysisCB(const std_msgs::Float32::ConstPtr& msg)
  45   {
  46     // make sure that the action hasn't been canceled
  47     if (!as_.isActive())
  48       return;

analysisコールバックは購読するデータチャネルのメッセージフォーマットを取り、データを処理し続ける前にactionがまだアクティブな状態にあるかをチェックします。

  50     data_count_++;
  51     feedback_.sample = data_count_;
  52     feedback_.data = msg->data;
  53     //compute the std_dev and mean of the data 
  54     sum_ += msg->data;
  55     feedback_.mean = sum_ / data_count_;
  56     sum_sq_ += pow(msg->data, 2);
  57     feedback_.std_dev = sqrt(fabs((sum_sq_/data_count_) - pow(feedback_.mean, 2)));
  58     as_.publishFeedback(feedback_);

ここで、関連データはフィードバック変数に入れられ、それからactionサーバによって提供されたフィードバックチャンネルの上に配信されます。

  60     if(data_count_ > goal_) 
  61     {
  62       result_.mean = feedback_.mean;
  63       result_.std_dev = feedback_.std_dev;
  64 
  65       if(result_.mean < 5.0)
  66       {
  67         ROS_INFO("%s: Aborted", action_name_.c_str());
  68         //set the action state to aborted
  69         as_.setAborted(result_);
  70       }
  71       else 
  72       {
  73         ROS_INFO("%s: Succeeded", action_name_.c_str());
  74         // set the action state to succeeded
  75         as_.setSucceeded(result_);
  76       }
  77     } 
  78   }

適正なデータが収集されると、actionサーバが成功か失敗かをセットします。これでactionサーバは非活性化しますし、以前に議論したように、analysisCB関数はすぐに戻りを返すことになります。

  80 protected:
  81     
  82   ros::NodeHandle nh_;
  83   actionlib::SimpleActionServer<learning_actionlib::AveragingAction> as_;
  84   std::string action_name_;
  85   int data_count_, goal_;
  86   float sum_, sum_sq_;
  87   learning_actionlib::AveragingFeedback feedback_;
  88   learning_actionlib::AveragingResult result_;
  89   ros::Subscriber sub_;
  90 };

これらはactionクラスのprotectedな変数です。ノードハンドルは構築され、actionの構築中にactionサーバに渡されます。前述の通りactionのコンストラクタの中でactionサーバは構築されます。配信するためのフィードバックと結果のメッセージ変数がここで作られます。購読の変数もノードハンドルを保存する形で構築されます。

  92 int main(int argc, char** argv)
  93 {
  94   ros::init(argc, argv, "averaging");
  95 
  96   AveragingAction averaging(ros::this_node::getName());
  97   ros::spin();
  98 
  99   return 0;
 100 }

最後は、メイン関数です。メイン関数はactionを生成し、シングルスレッドのループを回します。actionは実行しながら、ゴールを受け取るのを待ちます。

アクションをコンパイルし実行する

CMakeLists.txtファイルに、以下の２行を加えます：

add_executable(averaging_server src/averaging_server.cpp)
target_link_libraries(averaging_server ${catkin_LIBRARIES})

catkin_makeで実行ファイルを生成したら、新しいターミナルでroscoreをスタートさせます。

$ roscore

そしてactionサーバを起動します：

$ rosrun learning_actionlib averaging_server

以下のような出力が見えるでしょう：

[ INFO] 1250790662.410962000: Started node [/averaging], pid [29267], bound on [aqy], xmlrpc port [39746], tcpros port [49573], logging to [~/ros/ros/log/averaging_29267.log], using [real] time

actionが正しく動いていることをチェックするために、配信されてるトピックをリストします：

$ rostopic list -v

以下の様な出力が見えるでしょう：

Published topics:
 * /averaging/feedback [learning_actionlib/AveragingActionFeedback] 1 publisher
 * /averaging/status [actionlib_msgs/GoalStatusArray] 1 publisher
 * /rosout [rosgraph_msgs/Log] 1 publisher
 * /averaging/result [learning_actionlib/AveragingActionResult] 1 publisher
 * /rosout_agg [rosgraph_msgs/Log] 1 publisher

Subscribed topics:
 * /random_number [std_msgs/Float32] 1 subscriber
 * /averaging/goal [learning_actionlib/AveragingActionGoal] 1 subscriber
 * /averaging/cancel [actionlib_msgs/GoalID] 1 subscriber
 * /rosout [rosgraph_msgs/Log] 1 subscriber

上記の代わりに、ノードを見ることができます：

$ rosrun rqt_graph rqt_graph &

これは、actionサーバーが、期待通りにフィードバック、ステータス、結果のチャネルを配信し、ゴールとcancelチャネルを購読していることを示しています。actionサーバは起動し、正しく動作しています。

ゴールをアクションサーバに送る

actionを使った次のステップのために、Ctrl-Cでactionサーバを停止しておく必要があります。次はwrite a threaded simple action clientです。

ROS 2 Documentation

Wiki

Page

User

ゴールコールバックメソッドを使ったシンプルなアクションサーバを書く

Creating the Action Messages

Writing a Simple Server

The Code

The Code Explained

Compiling and Running the Action

Sending a Goal to the Action Server

Creating the Action Messages

Writing a Simple Server

The Code

The Code Explained

Compiling and Running the Action

Sending a Goal to the Action Server

Creating the Action Messages

Writing a Simple Server

The Code

The Code Explained

Compiling and Running the Action

Sending a Goal to the Action Server

Creating the Action Messages

Writing a Simple Server

The Code

The Code Explained

Compiling and Running the Action

Sending a Goal to the Action Server

アクションメッセージを生成する

シンプルなサーバを書く

コード

コード解説

アクションをコンパイルし実行する

ゴールをアクションサーバに送る