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Task planning methods guarantee to find the best action to perform at any time, with a in-depth analysis of the actions' state space. This comes at the cost of an high computational time and subsequent slow reaction time. On the other hand, well designed hand-made procedures encoded in models such as [Hierarchical Finite State Machines](http://wiki.ros.org/smach) or [Behavior Trees](http://wiki.ros.org/decision_making) are indeed able to perform a task while monitoring a restricted set of conditions, at the cost of a lower generality with respect to a planning-based system.
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Within SkiROS, the use of task planning is dosed in order to not impact on the reaction time of the system, but sufficiently to extend its ranges of applications. Task planning is invoked whenever the scheduling mechanism is not able to cope with the run-time failures. It ensures that the execution will be recovered from the most convenient step or that the robot will require for human support leaving the system in a secure state.
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Within SkiROS, the use of task planning is dosed in order to not impact on the reaction time of the system, but sufficiently to extend its ranges of applications. Task planning is invoked whenever the scheduling mechanism is not able to cope with the run-time failures. It ensures that the execution will be recovered from the most convenient point or that the robot will require for human support leaving the system in a secure state.
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### What else?
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Task planning and scheduling relies upon the constant monitoring of some relevant conditions in the environment, that allows to decide which is the most appropriate action to perform at any time. These condition variables form a pool of knowledge that tends inevitably to become complex. In fact, the more information is stored in this knowledge pool the more knowledgeable decisions can be taken.
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Task planning and scheduling relies upon the constant monitoring of some relevant conditions in the environment, that allows to decide which is the most appropriate action to perform at any time. These condition variables form a base of knowledge that tends inevitably grow in complexity. In fact, the more information is stored in this knowledge base the more knowledgeable decisions can be taken.
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SkiROS provides a set of interfaces to extend the knowledge base and apply reasoning algorithms to ground the symbols to real-world features.
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SkiROS provides a set of interfaces to extend the knowledge base and run reasoning algorithms to ground symbols to the real-world.
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# Tutorials
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* [[Installation|install]]
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