Intelligentes Missionsmanagement für autonome mobile Systeme (English shop)

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Torsten Pfützenreuter’s dissertation “Intelligentes Missionsmanagement für autonome mobile Systeme” investigates knowledge-based methods for the autonomous planning, monitoring, and adaptation of complex missions performed by mobile robotic systems. The research focuses on enabling autonomous vehicles to respond independently to unexpected events, technical failures, and changing environmental conditions without continuous communication with a human operator. The proposed intelligent mission management framework integrates mission planning, rule-based mission monitoring, and dynamic mission replanning into a unified control structure. Particular emphasis is placed on knowledge representation, inference mechanisms, and expert systems that identify deviations from planned operations and initiate appropriate corrective actions. The developed methods enable the insertion, deletion, and modification of mission plan elements while supporting resource-constrained mission optimization. Additional investigations address geographical mission validation and route modification using digital terrain models, triangulated irregular networks, and graph-based algorithms. The framework is implemented and evaluated using two autonomous platforms: the small mobile robot MauSI and the autonomous underwater vehicle DeepC. Experimental scenarios demonstrate how mission plans can be adapted in response to sensor failures, malfunctioning vehicle components, and limited operational resources. The dissertation contributes to the development of intelligent robotic control architectures and demonstrates how knowledge-based decision-making can improve the adaptability, operational reliability, and autonomy of mobile robotic systems.