With advances made in the fields of energy generation from renewable sources, airborne electrical propulsion, and autonomous system operation, much activity has been directed towards the development of so called high altitude pseudo satellites (HAPS) in recent years, with Zephyr (Airbus) and Aquila (Facebook) as prominent examples. Compared to classical orbital satellites, these are designed to require lower deployment costs and to offer a high flexibility in operational tasks and a long mission endurance. In the project StraVARIA, the goal was to develop a high-fidelity multiphysical simulation of such a HAPS, including a long-term mission planner, a reactive guidance system for weather avoidance, a flight control system with protections, a 6-DoF model with solar-electric propulsion system, and a comprehensive environment simulation with 4-D wind and turbulence. Due to the long mission duration, the mission planner and guidance system offer an increased autonomy level compared to standard operator controlled UAVs, however human input is still required for high level planning. The acausal and object-oriented modeling language Modelica has been used to create the integrated simulation model, enabling a modular and detailed modeling approach. By automatic code generation and optimization, simulation efficiency is improved, which is an important factor when considering long-term missions. Results of the integrated simulation show that missions like area surveillance and communication relay are possible whenever adverse weather conditions can be avoided. Ascending to and descending from mission altitude of approximately 18 km also poses a threat to the lightweight HAPS construction since layers of stronger winds and atmospheric disturbance have to be passed. To this end, simulated example missions over Bavaria are presented showcasing these effects, where mission success is ensured by means of the long term mission planner, the reactive guidance, and the inner-level protections implemented in the flight control system.
Global Navigation Satellite System (GNSS) signals, inertial measurements (angular rates, accelerations) and magnetometer measurements complement each other for position determination: GNSS provides a precise and drift-free position solution but is susceptible to signal outages. Inertial measurements are continuously available and of higher rate but suffer from integration drifts. Magnetic field measurements provide an instantaneous orientation in static conditions but are affected by both static and dynamic disturbances.In this paper, we provide a calibration method for magnetometers, which determines the biases and misalignment errors of the magnetometer as well as the magnetic flux including static disturbances. The method uses the iterative Gauss-Newton method and precise attitude information (heading, pitch) obtained from two low-cost GPS receivers. The attitude determination requires a tree search of the carrier phase integer ambiguities using a priori information on the distance between both GPS receivers.We also verified the proposed method with kinematic measurements from the CMPS10 sensor. We observe an accuracy of a few degrees for the unfiltered heading and a heading offset of less than 10 ∘ in 99.5% of all measurement epochs.
High-Altitude Pseudo-Satellites (HAPS) are long-endurance, fixedwing, lightweight Unmanned Aerial Vehicles (UAVs) that operate in the stratosphere and offer a flexible alternative for ground activity monitoring/imaging at specific time windows. As their missions must be planned ahead (to let them operate in controlled airspace), this paper presents a Genetic Algorithm (GA)-guided Hierarchical Task Network (HTN)-based planner for multiple HAPS. The HTN allows to compute plans that conform with airspace regulations and operation protocols. The GA copes with the exponentially growing complexity (with the number of monitoring locations and involved HAPS) of the combinatorial problem to search for an optimal task decomposition (that considers the time-dependent mission requirements and the time-varying environment). Besides, the GA offers a flexible way to handle the problem constraints and optimization criteria: the former encodes the airspace regulations, while the latter measures the client satisfaction, the operation efficiency and the normalized expected mission reward (that considers the wind effects in the uncertainty of the arrival-times at the monitoringlocations). Finally, by integrating the GA into the HTN planner, the new approach efficiently finds overall good task decompositions, leading to satisfactory task plans that can be executed reliably (even in tough environments), as the results in the paper show. CCS CONCEPTS• Theory of computation → Evolutionary algorithms; • Computing methodologies → Planning under uncertainty; Multiagent planning;
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.