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Star Ark : a living, self-sustaining spaceship
\"As space ventures have become more numerous, leading scientists and theorists have offered ways of building a living habitat in a hostile environment, taking an 'ecosystems' view of space colonization. The contributors to this volume take a radical multi-disciplinary view of the challenge of human space colonization through the ongoing project Persephone. This book fundamentally challenges prevalent ideas about sustainability and proposes a new approach to resource austerity and conservation and providing truly sustainable approaches that are life-promoting\"-- Provided by publisher.
Book
Planning airborne photogrammetry and remote-sensing missions with modern platforms and sensors
The mission planning in airborne Photogrammetry and Remote Sensing applications, depending on the system of acquisition and by the adopted platform (such as rotary and fixed wing aircrafts, glider, airship, manned or unmanned), is the first and essential step to ensure the success of a survey mission. The purpose of this paper is to provide an overview on mission planning techniques using passive optical sensors. The basic concepts related to the usage of the most common sensor technologies are described, along with the several possible scenarios that may be afforded by using modern airborne sensors. Several examples of flight plans are illustrated and discussed to highlight correct methods, procedures and tools for data acquisition in the case of different types of manned and unmanned airborne missions. In particular, the flight planning with more recent technologies of digital passive optical airborne sensors will be dealt with, including frame cameras and multi-/hyperspectral push-broom sensors. Furthermore, in order to ensure the complete success of an airborne mission, some up-to-date solutions to know in advance the weather conditions (cloud cover, height of the sun, wind, etc.) and the GNSS satellite configuration are illustrated.
Journal Article
A Simulation Framework of Unmanned Aerial Vehicles Route Planning Design and Validation for Landslide Monitoring
Unmanned aerial vehicles (UAVs) have emerged as a highly efficient means of monitoring landslide-prone regions, given the growing concern for urban safety and the increasing occurrence of landslides. Designing optimal UAV flight routes is crucial for effective landslide monitoring. However, in real-world scenarios, the testing and validating of flight path planning algorithms incur high cost and safety concerns, making overall flight operations challenging. Therefore, this paper proposes the use of the Unreal Engine simulation framework to design UAV flight path planning specifically for landslide monitoring. It aims to validate the authenticity of the simulated flight paths and the correctness of the algorithms. Under the proposed simulation framework, we then test a novel flight path planning algorithm. The simulation results demonstrate that the model reconstruction obtained using the novel flight path algorithm exhibits more detailed textures, with a 3D model simulation accuracy ranging from 10 to 14 cm. Among them, the RMSE value of the novel flight route algorithm falls within the range of 10 to 11 cm, exhibiting a 2 to 3 cm improvement in accuracy compared to the traditional flight path algorithm. Additionally, it effectively reduces the flight duration by 9.3% under the same flight path compared to conventional methods. The results confirm that the simulation framework developed in this paper meets the requirements for landslide damage monitoring and validates the feasibility and correctness of the UAV flight path planning algorithm.
Journal Article
Coordinated Target Assignment and UAV Path Planning with Timing Constraints
The engagement of a group of autonomous air vehicles against several targets is a major challenge in mission planning. This paper addresses the problem of cooperative flight path planning where the air vehicles should arrive at the destinations simultaneously or sequentially with specified time delays, while minimizing the total mission time. This involves finding an optimal assignment of air vehicles to targets and generating trajectories in compliance with the kinematic constraints of the vehicles. The trajectories have to avoid nofly-areas, threats and other obstacles, and must prevent the air vehicles from colliding with each other. The presented algorithm for simultaneous arrival first calculates shortest flight paths between all pairs of air vehicles and targets using a network-based routing model. An optimal assignment and a critical path is found by solving a linear bottleneck assignment problem with costs corresponding to the lengths of the shortest paths. The other flight paths are prolongated to the length of the critical path by automatic insertion of waypoints. This is achieved by concatenating subpaths stored in different shortest-path-trees. Due to the special structure of the network, all concatenated flight paths are flyable and feasible. Sequential arrival at a target is realized by sorting the flight paths according to their lengths and prolongating them whenever necessary to accomplish the desired time delays. The capability of the approach is demonstrated by simulation results.
Journal Article
UAV flight path planning optimization
In modern warfare, the use of UAVs for reconnaissance, search and rescue missions is very common, and it is essential to plan the flight path of UAVs. However, in the face of complex battlefield environment, the existing flight path planning algorithms have the problems of long time consumption and unstable path. Therefore, this paper studies the UAV flight path planning optimization in complex battlefield environment. First, we construct the battlefield environment model. Then, by analyzing the UAV flight constraints existing in battlefield environment, the objective function is obtained. And the problem of UAV flight path planning optimization is transformed into a nonlinear combinatorial optimization problem. On this basis, an Adaptive Adjustment Flight Path Planning algorithm (AA-FPP) is proposed. The AA-FPP algorithm adaptively adjusts the absorption coefficient of fireflies by using chaotic strategy. It adjusts the control position updating formula by using time-varying inertia weight to enhance its global searching ability. Then, random factors based on Boltzmann selection strategy are introduced to perturb the iterative solutions in AA-FPP. It expands the search space of the path and enhances the convergence efficiency. Finally, simulation results show that the AA-FPP algorithm can successfully plan a flight path that reduces static/dynamic threat intensity. And it has greater advantages in path stability and planning time consumption.
Journal Article
Coordinated flight path planning for a fleet of missiles in high-risk areas
This paper addresses the flight path planning problem for multiple missiles engaging stationary targets in high-risk areas. Targets protected by air defence are preferably engaged by a fleet or swarm of missiles, not individual missiles. The concept of a swarm attack is that a large number of approaching missiles overwhelm air defence. The deployment of missiles is often part of a broader mission including further participants. Flight path planning is then an integral element of mission planning, requiring strict timing coordination of all members involved. The flight times of the missiles are dictated by the master planning. We present algorithms for offline planning and online re-planning of flight paths for a fleet of missiles with flight time constraints. The algorithms are based on an advanced bidirectional RRT* algorithm that generates risk-minimizing flight paths with predefined flight times. Online planning generates the flight paths of the fleet sequentially, maintaining a safety distance between the missiles to prevent mutual collision. Offline planning uses a global optimization approach to determine an optimal selection of flight paths from a large set of potential paths. The selection is performed by a branch and bound algorithm that determines optimal cliques in the path compatibility graph. The optimization is embedded in an iterative algorithm that allows to successively improve the mission success.
Journal Article
ORBIT: Optimized Routing for Bridge Inspection Toolkit. An open-source UAS flight path planning tool for comprehensive bridge inspections under realistic constraints
Manual bridge inspections are labour-intensive, hazardous, and costly. While unmanned aerial system (UAS) are promising to facilitate the process, current flight planning tools do not address the unique challenges of complex bridge geometries or GNSS-denied underdeck environments. We present ORBIT, an open-source toolkit for generating optimized waypoint routes specifically designed bridge inspection missions using only minimal prior data. ORBIT generates coordinated waypoint routes for overview and underdeck inspections, maintaining spatial overlap between datasets to facilitate accurate image alignment. This approach also allows the UAS to closely follow bridge side faces at constant offsets, optimizing data acquisition for damage detection tasks. The planning workflow supports integration of commonly available cross-sectional plans or satellite imagery, incorporates flexible safety zones, and exports missions in standard KML and KMZ formats for direct use even with off-the-shelf commercial drones. Field deployments on multiple concrete canal bridges demonstrate that the generated routes provide complete inspection coverage. Underdeck missions were successfully executed using a DJI Mavic 3 Enterprise, relying solely on its onboard IMU when GNSS was unavailable and achieving reliable operation for bridge spans up to 20 meters. By making ORBIT openly available, this work aims to enable safer, more precise, and scalable UAS-based bridge inspection, and to support future research in the field.https://github.com/ErToBar2/ORBIT
Journal Article
Online flight path planning with flight time constraints for fixed-wing UAVs in dynamic environments
PurposeA major challenge for mission planning of aircraft is to generate flight paths in highly dynamic environments. This paper presents a new approach for online flight path planning with flight time constraints for fixed-wing UAVs. The flight paths must take into account the kinematic restrictions of the vehicle and be collision-free with terrain, obstacles and no-fly areas. Moreover, the flight paths are subject to time constraints such as predetermined time of arrival at the target or arrival within a specified time interval.Design/methodology/approachThe proposed flight path planning algorithm is an evolution of the well-known RRT* algorithm. It uses three-dimensional Dubins paths to reflect the flight capabilities of the air vehicle. Requirements for the flight time are realized by skillfully concatenating two rapidly exploring random trees rooted in the start and target point, respectively.FindingsThe approach allows to consider static obstacles, obstacles which might pop up unexpectedly, as well as moving obstacles. Targets might be static or moving with constantly changing course. Even a change of the target during flight, a change of the target approach direction or a change of the requested time of arrival is included.Originality/valueThe capability of the flight path algorithm is demonstrated by simulation results. Response times of fractions of a second qualify the algorithm for real-time applications in highly dynamic scenarios.
Journal Article
Development of pre-flight planning algorithms for the functional-program prototype of a distributed intellectual control system of unmanned flying vehicle groups
In article presents algorithmic for a reconnaissance and combat unmanned flying vehicle (UFV) group pre-flight planning. The algorithms, which we elaborated, includes: algorithm, involved for mission target decision, membership selection. Discussing algorithm is based on analytical probabilistic models, providing evaluation of UAF group efficiency operation, considering UAF reliability as well as on board navigation and weapon facility performances; algorithm of UAF routes at the stages of group motion to meeting point and further movement to target area. Developed algorithm provides definition both direction and velocity of UAF, considering actual environment condition and dangerous of UAF recognition by radar, acoustic or infrared facilities of enemy; algorithms of UAF group operation scheduling at the mission target area.
Journal Article
An FPTAS for Dynamic Multiobjective Shortest Path Problems
The Dynamic Multiobjective Shortest Path problem features multidimensional costs that can depend on several variables and not only on time; this setting is motivated by flight planning applications and the routing of electric vehicles. We give an exact algorithm for the FIFO case and derive from it an FPTAS for both, the static Multiobjective Shortest Path (MOSP) problems and, under mild assumptions, for the dynamic problem variant. The resulting FPTAS is computationally efficient and beats the known complexity bounds of other FPTAS for MOSP problems.
Journal Article