Robots that create alternative plans against failures

Automated action planning is crucial for efficient execution of mobile robot missions. Automated planners use complete domain descriptions to construct plans. Nevertheless, there is usually a gap between the real world and its representation. Therefore, there is another source of uncertainty for mobile robot systems due to the impossibility of perfectly representing action descriptions (e.g., preconditions and effects) in all circumstances. Incomplete domain representations may lead a planner to fail constructing a valid plan when unforeseen events are encountered. We investigate these types of situations, especially the failure cases and how robots can recover from real-time execution failures. The main focus of our research is to design a dynamic planning framework which can generate alternative plans by applying generic updates in the domain representation when the execution of a plan fails. Our proposed method constructs new feasible plans by using the updated domain representations even if the outcomes of the operators are partially known in advance or feasible plans are not possible with the original representation of the domain. Besides updating the domain representation, our method manipulates the planner by using a reasoning mechanism so that it chooses more relevant actions to recover from failures. This is achieved by considering the effects of the failed action and trying to accomplish these effects with alternative actions.