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31	Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas Braßel, Hannes, Zeh, Thomas, Fricke, Hartmut, Eltner, Anette 12 August 2024 (has links) With unmanned aerial vehicle(s) (UAV), swift responses to urgent needs (such as search and rescue missions or medical deliveries) can be realized. Simultaneously, legislators are establishing so-called geographical zones, which restrict UAV operations to mitigate air and ground risks to third parties. These geographical zones serve particular safety interests but they may also hinder the efficient usage of UAVs in time-critical missions with range-limiting battery capacities. In this study, we address a facility location problem for up to two UAV hangars and combine it with a routing problem of a standard UAV mission to consider geographical zones as restricted areas, battery constraints, and the impact of wind to increase the robustness of the solution. To this end, water rescue missions are used exemplary, for which positive and negative location factors for UAV hangars and areas of increased drowning risk as demand points are derived from open-source georeferenced data. Optimum UAV mission trajectories are computed with an A* algorithm, considering five different restriction scenarios. As this pathfinding is very time-consuming, binary occupancy grids and image-processing algorithms accelerate the computation by identifying either entirely inaccessible or restriction-free connections beforehand. For the optimum UAV hangar locations, we maximize accessibility while minimizing the service times to the hotspots, resulting in a decrease from the average service time of 570.4 s for all facility candidates to 351.1 s for one and 287.2 s for two optimum UAV hangar locations. info:eu-repo/classification/ddc/620 ddc:620
32	Distributed Algorithms for Multi-robot Autonomy Zehui Lu (18953791) 02 July 2024 (has links) <p dir="ltr">Autonomous robots can perform dangerous and tedious tasks, eliminating the need for human involvement. To deploy an autonomous robot in the field, a typical planning and control hierarchy is used, consisting of a high-level planner, a mid-level motion planner, and a low-level tracking controller. In applications such as simultaneous localization and mapping, package delivery, logistics, and surveillance, a group of autonomous robots can be more efficient and resilient than a single robot. However, deploying a multi-robot team by directly aggregating each robot's planning hierarchy into a larger, centralized hierarchy faces challenges related to scalability, resilience, and real-time computation. Distributed algorithms offer a promising solution for introducing effective coordination within a network of robots, addressing these issues. This thesis explores the application of distributed algorithms in multi-robot systems, focusing on several essential components required to enable distributed multi-robot coordination, both in general terms and for specific applications.</p> Automation engineering Control engineering Field robotics Manufacturing robotics Electrical machines and drives Distributed systems and algorithms Dynamical systems in applications Theoretical and applied mechanics Optimisation Distributed Algorithms Multi Agent System Multi Robot System Distributed Optimization Mission Planning Co-design Algorithms Model Predictive Control Mobile Manipulator Motion Planning and Control Differentiable Dynamics Modeling Motor Co-design

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Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

Distributed Algorithms for Multi-robot Autonomy