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"Mission planning"
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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
Digital Twin of Space Environment: Development, Challenges, Applications, and Future Outlook
This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications.
Journal Article
Survey on Mission Planning of Multiple Unmanned Aerial Vehicles
The task assignment issue and the path planning problem of Multiple Unmanned Aerial Vehicles (Multi-UAV) are collectively referred to as the Mission Planning Problem (MPP). This review article provides an update on the progress of the MPP on Multi-UAV. Focusing on the burning issue of task assignment, this paper focuses on the comparison of the characteristics of the mathematical programming method, heuristic algorithm, negotiation algorithm and neural networks. According to different constraints, trajectory planning can be divided into 2 dimension coverage, 3 dimension cooperation, and 4 dimension space-time cooperation. Combined with typical research, common collaborative guidance methods are introduced, and the key development direction of this field is prospected. The article shows that, although the MPP has been extensively studied, ongoing research is required. In particular, it is necessary to pay attention to the timeliness of the task assignment, the information coupling exists in MPP, and the problems caused by multiple constraints of Multi-UAV and environmental uncertainty.
Journal Article
A Multi-UAV cooperative mission planning method based on SA-WOA algorithm for three-dimensional space atmospheric environment detection
In the application of rotorcraft atmospheric environment detection, to reflect the distribution of atmospheric pollutants more realistically and completely, the sampling points must be spread throughout the entire three-dimensional space, and the cooperation of multiple unmanned aerial vehicles (multi-UAVs) can ensure real-time performance and increase operational efficiency. In view of the problem of coordinated detection by multi-UAVs, the region division and global coverage path planning of the stereo space to be detected are studied. A whale optimization algorithm based on the simulated annealing-whale optimization algorithm (SA-WOA) is proposed, which introduces adaptive weights with the Levy flight mechanism, improves the metropolis criterion, and introduces an adaptive tempering mechanism in the SA stage. Path smoothing is subsequently performed with the help of nonuniform rational B-spline (NURBS) curves. The comparison of algorithms using the eil76 dataset shows that the path length planned by the SA-WOA algorithm in this paper is 10.15% shorter than that of the WOA algorithm, 13.25% shorter than the SA planning result, and only 0.95% difference from the optimal path length in the dataset. From the perspective of planning time, its speed is similar to WOA, with a relative speed increase of 27.15% compared to SA, proving that the algorithm proposed in this paper has good planning performance. A hardware system platform is designed and built, and environmental gas measurement experiments were conducted. The experimental results indicate that the multi-UAV collaborative environment detection task planning method proposed in this paper has certain practical value in the field of atmospheric environment detection.
Journal Article
Summary of Research on Satellite Mission Planning Based on Multi-Agent-System
With the continuous development of space science, the number and types of space missions are increasing day by day. Reasonable mission planning plays an important role in fulfilling space missions to the maximum extent with limited satellite resources. Satellite mission planning modelling based on multi-agent system (MAS) is a bottom-up method. This article introduces three specific solution methods and compares and analyses them. Finally, the future development direction of the MAS mission planning problem is analysed.
Journal Article
Hierarchical Cooperative Multitarget Allocation Strategy of Multiple Spacecrafts
In the future space missions, multispacecraft collaboration has become increasingly significant due to the inherent limitations of a single spacecraft in executing complex tasks. To address the coordination challenges associated with multispacecraft systems, a novel hierarchical mission planning framework is proposed in this paper. Within this framework, a complete planning process is divided into two layers, namely, the evaluation layer and the decision‐making layer. At the evaluation layer, the mission effectiveness is obtained based on the capabilities and current states of each spacecraft. At the decision‐making layer, the mission effectiveness data is then utilized for task allocation to generate a comprehensive and optimal mission plan. To validate the proposed method, extensive mission planning and orbital dynamics simulations were conducted using virtual scenarios of multispacecraft collaboration. The results demonstrate that this approach effectively resolves the challenges of multispacecraft task allocation and single‐spacecraft task planning, thus providing a robust solution for the coordination of future space missions.
Journal Article
Resilient multi-objective mission planning for UAV formation: A unified framework integrating task pre- and re-assignment
Combat effectiveness of unmanned aerial vehicle (UAV) formations can be severely affected by the mission execution reliability. During the practical execution phase, there are inevitable risks where UAVs being destroyed or targets failed to be executed. To improve the mission reliability, a resilient mission planning framework integrates task pre- and re-assignment modules is developed in this paper. In the task pre-assignment phase, to guarantee the mission reliability, probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization model. And an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient solution. As in the task-reassignment phase, possible trigger events are first analyzed. A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency scenario. And the dual objective used in the former phase is adapted into a single objective to keep a consistent combat intention. Three cases of different scales demonstrate that the two modules cooperate well with each other. On the one hand, the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is introduced. On the other hand, the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a millisecond. The corresponding animation is accessible at bilibili.com/video/BV12t421w7EE for better illustration.
•A unified framework integrating task pre- and re-assignment for CTAP is proposed.•Probabilistic constraints are introduced to ensure mission reliability.•A multi-population multi-objective evolutionary algorithm is developed.•A real-time CNP-based task re-assignment algorithm is given.•The simulations verify that the resilient framework ensures high mission reliability.
Journal Article
Multi-Objective Multi-Satellite Imaging Mission Planning Algorithm for Regional Mapping Based on Deep Reinforcement Learning
Satellite imaging mission planning is used to optimize satellites to obtain target images efficiently. Many evolutionary algorithms (EAs) have been proposed for satellite mission planning. EAs typically require evolutionary parameters, such as the crossover and mutation rates. The performance of EAs is considerably affected by parameter setting. However, most parameter configuration methods of the current EAs are artificially set and lack the overall consideration of multiple parameters. Thus, parameter configuration becomes suboptimal and EAs cannot be effectively utilized. To obtain satisfactory optimization results, the EA comp ensates by extending the evolutionary generation or improving the evolutionary strategy, but it significantly increases the computational consumption. In this study, a multi-objective learning evolutionary algorithm (MOLEA) was proposed to solve the optimal configuration problem of multiple evolutionary parameters and used to solve effective imaging satellite task planning for region mapping. In the MOLEA, population state encoding provided comprehensive population information on the configuration of evolutionary parameters. The evolutionary parameters of each generation were configured autonomously through deep reinforcement learning (DRL), enabling each generation of parameters to gain the best evolutionary benefits for future evolution. Furthermore, the HV of the multi-objective evolutionary algorithm (MOEA) was used to guide reinforcement learning. The superiority of the proposed MOLEA was verified by comparing the optimization performance, stability, and running time of the MOLEA with existing multi-objective optimization algorithms by using four satellites to image two regions of Hubei and Congo (K). The experimental results showed that the optimization performance of the MOLEA was significantly improved, and better imaging satellite task planning solutions were obtained.
Journal Article
Deep Reinforcement Learning for Intelligent Dual-UAV Reconnaissance Mission Planning
The reconnaissance of high-value targets is prerequisite for effective operations. The recent appreciation of deep reinforcement learning (DRL) arises from its success in navigation problems, but due to the competitiveness and complexity of the military field, the applications of DRL in the military field are still unsatisfactory. In this paper, an end-to-end DRL-based intelligent reconnaissance mission planning is proposed for dual unmanned aerial vehicle (dual UAV) cooperative reconnaissance missions under high-threat and dense situations. Comprehensive consideration is given to specific mission properties and parameter requirements through the whole modelling. Firstly, the reconnaissance mission is described as a Markov decision process (MDP), and the mission planning model based on DRL is established. Secondly, the environment and UAV motion parameters are standardized to input the neural network, aiming to deduce the difficulty of algorithm convergence. According to the concrete requirements of non-reconnaissance by radars, dual-UAV cooperation and wandering reconnaissance in the mission, four reward functions with weights are designed to enhance agent understanding to the mission. To avoid sparse reward, the clip function is used to control the reward value range. Finally, considering the continuous action space of reconnaissance mission planning, the widely applicable proximal policy optimization (PPO) algorithm is used in this paper. The simulation is carried out by combining offline training and online planning. By changing the location and number of ground detection areas, from 1 to 4, the model with PPO can maintain 20% of reconnaissance proportion and a 90% mission complete rate and help the reconnaissance UAV to complete efficient path planning. It can adapt to unknown continuous high-dimensional environmental changes, is generalizable, and reflects strong intelligent planning performance.
Journal Article