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6 results for “Mission Planning”
Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning
<p>Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning</p> <p>Data contains shapefiles that can be used in any geoinformation system (GIS).</p> <p><strong>If you use these data, please cite BOTH the <em>JOURNAL NAME</em> publication and the Zenodo dataset.</strong></p> <p>Iqbal, W., Head III, J. W., van der Bogert, C. H., Frueh, T., Henriksen, M., Bickel, V., Kring, D., Hiesinger, H., Scott, D. R., & Heyer, T. (2024). Slopes along Apollo EVAs: Astronaut experience as input for future mission planning. Acta Astronautica, 223, 184-196. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.actaastro.2024.07.006" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.actaastro.2024.07.006</a></p> <p>Iqbal, W., Head, J. W., van der Bogert, C., Frueh, T., Henriksen, M., Bickel, V., Kring, D., Hiesinger, H., Scott, D. R., & Heyer, T. (2024). Complementary data for Iqbal et al. (2024): Slopes along Apollo EVAs: Astronaut experience as input for future mission planning [Data set]. In Acta Astronautica (Bd. 223, S. 184–196). Zenodo. <a href="https://doi.org/10.5281/zenodo.13790204" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13790204</a></p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Structure</p> <p>-> File "Apollo_Traverses_Iqbal_24" - It includes six subfolders for each landing site that contain the shapefiles of traverses.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact <a href="mailto:lwueller@uni-muenster.de" rel="noopener noreferrer nofollow">iqbalw@uni-muenster.de</a></p> <p>Wajiha Iqbal, Institut für Planetologie, Universität Münster, Germany.</p>
FlyPaw: Optimized route planning for scientific UAV missions
Open the record for dataset details and reuse information.
Adaptive planning for assistive-care robotic missions - use case 1
<p>Videos of scenarios A and B from Use Case 1 (UC1), recreated in a digital environment using the <a href="https://gazebosim.org/home">Gazebo</a> simulator and our adaptive path-finding framework.</p>
A Two-time-level Model for Mission and Flight Planning of an Inhomogeneous Fleet of Unmanned Aerial Vehicles
<p>We consider the mission and flight planning problem for an inhomogeneous fleet of unmanned aerial vehicles (UAVs). Therein, the mission planning problem of assigning targets to a fleet of UAVs and the flight planning problem of finding optimal flight trajectories between a given set of waypoints are combined into one model and solved simultaneously. Thus, trajectories of an inhomogeneous fleet of UAVs have to be specified such that the sum of waypoint-related scores is maximized, considering technical and environmental constraints. Several aspects of an existing basic model are expanded to achieve a more detailed solution. A two-level time grid approach is presented to smooth the computed trajectories. The three-dimensional mission area can contain convex-shaped restricted airspaces and convex subareas where wind affects the flight trajectories. Furthermore, the flight dynamics are related to the mass change, due to fuel consumption, and the operating range of every UAV is altitude-dependent. A class of benchmark instances for collision avoidance is adapted and expanded to fit our model and we prove an upper bound on its objective value. Finally, the presented features and results are tested and discussed on several test instances using GUROBI as a state-of-the-art numerical solver.</p>
Mission Plans Synthesized by TAMAA for 4 Autonomous Agents
<p>These are figures of the mission-plan synthesizing result obtained by running TAMAA. The experimental scenario is shown in "example.png". The path shown in "TAMAA-4 agents-path 1.png" is shorter and the agents do not go to the charging point, whereas the path in "TAMAA-4 agents-path 2.png" is longer and the agents have been to the charging point before it finishes the job.</p>
A Modeling Framework for Prognostic Decision Making and its Application to UAV Mission Planning
The goal of prognostic decision making (PDM) is to utilize information on anticipated system health changes in selecting future actions. One of the key challenges in PDM is finding a sufficiently expressive yet compact mathematical representation of the system for use with decision optimization algorithms. In this paper we describe a general modeling approach for a class of PDM problems with non-linear system degradation processes and uncertainties in state estimation, action effects, and future operating conditions. The approach is based on continuous Partially Observable Markov Decision Processes (POMDPs) used in conjunction with 'black box' system simulations. The proposed modeling framework can be cast into simpler representations, depending on which sources of uncertainty are being included. The approach is illustrated with a mission planning case study for an unmanned aerial vehicle (UAV). In the case study a PDM system is tasked with optimizing the vehicle route after an in-flight component fault is detected. A stochastic algorithm (based on particle filtering) is used for decision optimization, with a second, deterministic algorithm providing a performance evaluation baseline. Both algorithms utilize a UAV physics simulator for generating predictions of future vehicle states. Performance benchmarking is done on a set of mission scenarios of increasing complexity.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.