Contents
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.
actionlib/server/simple_action_server.h is the action library used from implementing simple actions.
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.
Here a callback is setup for the data that will be processed by the action and the action server is started.
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.
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.
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.
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.
