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Applying unmanned aerial vehicles (UAV) has benefits for many different use-cases.
Existing implementations of ground control stations (GCS) to manage UAVs in such scenarios already provide some support for the operation of multi-unit systems, i.e., ensembles.
However, since they are usually designed for the operation of only one copter at once, this is often not sufficient to react quickly in dangerous situations, e.g., search and rescue scenarios.
To address this problem, we propose an approach for easy observation and control of complete autonomous UAV ensembles:
The Intention of our approach is to greatly reduce the number of personnel required for the operation of an UAV ensemble.
Thereby, we generate the possibility for rapid intervention in potentially dangerous situations in order to prevent damage to the UAVs and the environment.
In this paper, we present a software architecture for this safety-critical multi UAV ground control station including a fully implemented prototype which we also tested in a realistic environment.
The application of autonomous mobile robots can improve many situations of our daily lives. Robots can enhance working conditions, provide innovative techniques for different research disciplines, and support rescue forces in an emergency. In particular, flying robots have already shown their potential in many use-cases when cooperating in ensembles. Exploiting this potential requires sophisticated measures for the goal-oriented, application-specific programming of flying ensembles and the coordinated execution of so defined programs. Because different goals require different robots providing different capabilities, several software approaches emerged recently that focus on specifically designed robots. These approaches often incorporate autonomous planning, scheduling, optimization, and reasoning attributable to classic artificial intelligence. This allows for the goal-oriented instruction of ensembles, but also leads to inefficiencies if ensembles grow large or face uncertainty in the environment. By leaving the detailed planning of executions to individuals and foregoing optimality and goal-orientation, the selforganization paradigm can compensate for these drawbacks by scalability and robustness.
In this thesis, we combine the advantageous properties of autonomous planning with that of self-organization in an approach to Mission Programming for Flying Ensembles. Furthermore, we overcome the current way of thinking about how mobile robots should be designed. Rather than assuming fixed-design robots, we assume that robots are modifiable in terms of their hardware at run-time. While using such robots enables their application in many different use cases, it also requires new software approaches for dealing with this flexible design. The contributions of this thesis thus are threefold. First, we provide a layered reference architecture for physically reconfigurable robot ensembles. Second, we provide a solution for programming missions for ensembles consisting of such robots in a goal-oriented fashion that provides measures for instructing individual robots or entire ensembles as desired in the specific use case. Third, we provide multiple self-organization mechanisms to deal with the system’s flexible design while executing such missions. Combining different self-organization mechanisms ensures that ensembles satisfy the static requirements of missions. We provide additional self-organization mechanisms for coordinating the execution in ensembles ensuring they meet the dynamic requirements of a mission. Furthermore, we provide a solution for integrating goal-oriented swarm behavior into missions using a general pattern we have identified for trajectory-modification-based swarm behavior. Using that pattern, we can modify, quantify, and further process the emergent effect of varying swarm behavior in a mission by changing only the parameters of its implementation. We evaluate results theoretically and practically in different case studies by deploying our techniques to simulated and real hardware.
To ease teaching self-organizing systems design, we implemented the AntNet routing algorithm for real-world application using educational robots called ActivityBot. Using line sensors and ultrasonic distance sensors, the robotic ants traverse a tiled graph printed on paper, collectively converging to the shortest path. In our descriptions, we address the challenges to face when employing such self-organizing systems on educational hardware and provide a video on YouTube https://youtu.be/JFduHJ0o0UM.
Through the mechanisms of the Semantic Web, it is possible not only to describe web content syntactically but also to relate it semantically. The properties and capabilities of hardware, instead, are hidden in documents, code documentations, repository descriptions, etc. This paper presents a methodology and architecture that can be used to describe and relate the properties and capabilities of hardware. The decentralized storage of the descriptions on a hardware adapter allows the information to be evaluated at runtime. For domain-specific applications a Model-Domain-Domainmodel Architecture (MDDM) is presented so that code can also be executed at runtime using these hardware descriptions. The architecture is presented using a home automation system with single-board computers and microcontrollers, in which sensors and actuators can be exchanged and integrated.
