Robust Plan Execution Using Model-Based Reasoning

Autonomous mobile robots perform more tasks with increasing complexity, like exploring other planets. In order to be able to perform such tasks they have to have capabilities for planning and reasoning. For the calculation of a plan for a given goal there exist a number of suitable algorithms. However, if such a plan is executed on an autonomous mobile robot in a dynamic environment, a number of problems are likely to occur. Apart from the problems caused by the assumption used in the planning phase, problems arise though inaccurate sensing, acting and events that are not under the control of the robot. All these problems have in common that they cause an inconsistency between the intentions of the plan and the observed world. In this paper we propose model-based diagnosis as a method for the detection and categorization of such inconsistencies. The obtained knowledge about failures in plan execution and their root causes can be used to monitor plan execution. Such monitoring together with appropriate repair actions improves the robustness of the execution of plans in dynamic environments and, thus, improves the robustness of autonomous mobile robots.

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