Decentralized context models for cooperative and autonomous mobile robots
Publication date
2026-09-01
Document type
Dissertation
Cumulative Thesis
✅
Author
Advisor
Referee
Granting institution
Helmut-Schmidt-Universität/Universität der Bundeswehr Hamburg
Exam date
2026-08-21
Organisational unit
Publisher
Universitätsbibliothek der HSU/UniBw H
Contains the following part
Part of the university bibliography
✅
File(s)
Language
English
Abstract
Autonomous mobile robots (AMR) are increasingly being deployed in real-world applications such as logistics, inspection, monitoring, and other demanding operations. Their capabilities become particularly valuable in cooperative settings, where heterogeneous AMRs can combine complementary strengths across different modalities (land, water, air). However, scalable cooperation requires more than navigation, task execution, and ad hoc message exchange. It requires a shared, machine-processable understanding of the current situation that is precise enough to support planning and coordination. Additionally, it must be robust enough to remain usable under uncertainty, partial connectivity, and changing mission conditions.
This dissertation addresses the development of decentralized context models for cooperative AMR teams. While related research has contributed important advances in context-aware systems, ontology-based modeling, and decentralized coordination, the engineering landscape remains fragmented. Existing approaches rarely connect the systematic identification of mission-relevant context, the formal design of interoperable context representations, and the decentralized implementation and use of context information within one coherent process.
To close this gap, the dissertation develops a structured engineering support framework that treats decentralized context models as explicit engineering artifacts across the phases of analysis, design, and implementation. In the analysis phase, a context identification method is introduced to derive and structure relevant context information from mission scenarios, stakeholder requirements, and system behavior. In the design phase, the work contributes design principles for distributed context modeling, a platform- and technology-independent information model, and an interoperable communication concept for cooperative AMR teams. In the implementation phase, the dissertation realizes decentralized context models through selective information distribution and uncertainty-aware consensus formation.
The proposed framework and its research artifacts are evaluated through case studies and simulations involving heterogeneous teams of unmanned aerial, ground, and surface vehicles in representative cooperative scenarios. The results show that the presented approach provides a reusable methodological and architectural foundation for building decentralized context models that remain semantically consistent, interoperable, and resilient under imperfect communication and uncertainty. By systematically connecting context analysis, formal model design, and decentralized runtime realization, the dissertation establishes decentralized context models as a practical integration artifact for cooperative AMR teams.
This dissertation addresses the development of decentralized context models for cooperative AMR teams. While related research has contributed important advances in context-aware systems, ontology-based modeling, and decentralized coordination, the engineering landscape remains fragmented. Existing approaches rarely connect the systematic identification of mission-relevant context, the formal design of interoperable context representations, and the decentralized implementation and use of context information within one coherent process.
To close this gap, the dissertation develops a structured engineering support framework that treats decentralized context models as explicit engineering artifacts across the phases of analysis, design, and implementation. In the analysis phase, a context identification method is introduced to derive and structure relevant context information from mission scenarios, stakeholder requirements, and system behavior. In the design phase, the work contributes design principles for distributed context modeling, a platform- and technology-independent information model, and an interoperable communication concept for cooperative AMR teams. In the implementation phase, the dissertation realizes decentralized context models through selective information distribution and uncertainty-aware consensus formation.
The proposed framework and its research artifacts are evaluated through case studies and simulations involving heterogeneous teams of unmanned aerial, ground, and surface vehicles in representative cooperative scenarios. The results show that the presented approach provides a reusable methodological and architectural foundation for building decentralized context models that remain semantically consistent, interoperable, and resilient under imperfect communication and uncertainty. By systematically connecting context analysis, formal model design, and decentralized runtime realization, the dissertation establishes decentralized context models as a practical integration artifact for cooperative AMR teams.
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