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Task planning » History » Version 3

Rafael Bailon-Ruiz, 2020-09-10 16:59

1 3 Rafael Bailon-Ruiz
h1. Task planning
2 2 Florian Seguin
3 1 Florian Seguin
The Planner module can be used to create a Plan using a modified version of "Dana Nau's pyhop":http://www.cs.umd.edu/projects/shop/index.html. The plan created is then translated into a sequence of mission types objects from the base module. This sequence can be sent to the autopilot and executed from there.
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Right now, the module is under heavy development and thus may contain bugs, missing features, wrong models... 
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h2. HTN, pyhop and plans
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Hierarchical task network (HTN) is a planification method where every action is structured. There are three main parts that compose the network :
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- Primitive tasks : they represent roughly an action of the UAV
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- Compound tasks : they represent a sequence of actions ordered. The compound task is only feasible when all actions can be done in the order given by the compound task 
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- Decision tasks : they represent a choice between different ways of doing the task. A decision task is feasible whenever a way of doing the task is feasible.
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These actions are then structured into a tree, where each leaf is a primitive task and every other node is a decision or a compound task.
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For modeling our tree of decisions and solving we use a modified version of pyhop, a planner written by Dana Nau.
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We need to first create a description of our "world", and define each primitive, compound and decision tasks.
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<pre><code class="python"># Creating primitive tasks
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def simple_task1(state, goal):
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    # Doing stuff with state
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    return state
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def simple_task2(state, goal):
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    # Doing other stuff with state
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    return state
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# Creating compound tasks
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def compound_task1(state, goal):
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    return [('simple_task1', goal), ('simple_task2', goal)]
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def compound_task2(state, goal):
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    return [('simple_task2', goal), ('simple_task1', goal)]
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# Pyhop needs to know the primitive tasks
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pyhop.declare_operators(simple_task1, simple_task2)
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# You can declare your decisions methods this way
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pyhop.declare_methods('objective', compound_task1, compound_task2)
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</code></pre>
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Once the "world" is described, we can try to solve the problem.
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<pre><code class="python"># We solve the problem by calling solve method of pyhop
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pyhop.solve(state, ['objective', goal])
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</code></pre>
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Using our precedent examples, we can draw our tree and explore it. Instead of returning a plan after one is found, it explores always all the tree and returns the plan with a the minimum cost based on a criteria. Since we are working with UAVs, these criterias can be the battery ('bat') or the time ('time').
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h2. Translation and transmitting plans to the UAV
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To translate and transmit a plan to the UAV, we use the PlanManager class. The PlanManager class is a centralized class where every UAV using a specific plugin (the PlanUpdater plugin) is registered. We will get into it later.
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Since it is centralized, the class itself is a Singleton, meaning there can only be one instance of the object at time. It contains the current state of the UAVs, and other objects related to UAVs.
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To create a plan, you can use the method create_plan.
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<pre><code class="python">def create_plan(self, uavId, objective, goalParams, homeParams, minimizeBy='bat', verbose=0):
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"""
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Create a plan using the different informations passed in parameters. The
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plan is created via a PlanObjective object. Translates the plan and
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updates the uavsPlan attribute
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Parameters
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----------
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uavId : int                                                                                                                                      
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    The id of the UAV
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objective : str
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    The string of the objective we want to fulfill
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goalParams : dict
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    The goal parameters (i.e. the position of the cloud we target)                                                                                                                             
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homeParams : dict
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    The home parameters (i.e. the position where UAVs are launched)
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minimizeBy : str
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    The parameter to minimize (time, bat...)
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verbose : int
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    Used for printing
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"""
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    initState = {}
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    for uId, uStatus in self.uavsStatus.items():
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        initState[uId] = copy.deepcopy(self.uavsStatus[uId]).to_dict()                                                                               
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        initState[uId].update(self.uavsParameters[uId])                                                                                              
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    planObject = PlanObjective(uavId, objective,
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                               state=initState, goal=goalParams, home=homeParams)
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    self.uavsPlan[uavId] = self.translate_plan(uavId, planObject.solve(minimizeBy, verbose=verbose))  
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</code></pre>
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Note : we use an object called PlanObjective to translate our plan into pyhop goal and state objects. We also translate immediately the plan into a sequence of nephelae.mission.types.MissionTypes.
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Once translated, the plan is stored into the associated variable. When a UAV is accepting or deleting a plan computed, this variable becomes empty. 
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**Note that if another plan is computed for the same UAV, the previous plan that was contained into the associated variable is erased and replaced by the new one**.
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h2. Executing a UAV plan 
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After a plan has been accepted by the UAV (note that you need to plug the PlanUpdater plugin to the UAV to accept/reject plans), it is stored inside a dedicated variable called currentPlan. This plan can be accepted and sent to the autopilot.
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To accept a plan you can use the __accept_plan()__ method of the PlanUpdater plugin.
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To reject a plan you can use the __reject_plan()__ method of the PlanUpdater plugin.
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To send the missions to the autopilot and update the executingPlan variable, you can use the __execute_plan()__ method of the PlanUpdater plugin.
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The indices of the missions are duplicated and stored into another variable called executingPlan. This variable is then updated periodically with the missionStatus messages emitting from the autopilot.
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The callback used to update the executingPlan :
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<pre><code class="python">def mission_status_callback(self, missionStatus):
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"""
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Callback called whenever a message missionStatus is received. Updates
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the executingPlan attribute by popping missions from it
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(Note : the callback seems buggy to me, TBD)
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Parameters
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----------
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missionStatus : dict
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    The messages containing the current indexes of the missions being
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    executed and the remaining time of the current mission.
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"""
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    index_list = missionStatus['index_list']
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    remaining_time = missionStatus['remaining_time']
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    popped_missions = []
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        for index in self.executingPlan: # Popping loop : removing finished tasks
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            if index not in index_list:
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                popped_missions.append(self.pop_plan().missionId)
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        self.remainingTime = remaining_time
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        print(popped_missions)
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</code></pre>
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This callback is contained inside the PlanUpdater plugin. Note that it might contain some bugs (for instance, a discrepancy between an old missionStatus message and a new executingPlan could empty the executingPlan variable).