Multi-satellite Mission Planning Algorithm Based on Preemptive Priority Model

Tabu Search (TS) algorithm is one of optimization search methods and has been applied for a large number of combinatorial optimization problems. In the paper, an improved TS algorithm is proposed for protein 3D folding structure prediction in off-lattice AB model. Experimental results show that the lowest energies computed by the improved TS algorithm are better than those obtained by the previous methods. Given a protein sequence, its lowest-energy conformation obtained by our improved TS method forms a single hydrophobic core, which suggests that AB model in three dimensions appears to reflect the real protein reasonably. Compared with the previous heuristic approaches, the improved TS algorithm has higher performance and can be effectively used to predict 3D structure prediction of proteins.

As a novel logistics transportation mode, the passenger and freight integration model has garnered significant attention in recent years. This study focuses on collaborative delivery route planning for rural public buses and express delivery vehicles, exploring its application within the context of passenger and freight integration. For the pickup and delivery routing problem with time windows under demand uncertainty, we first developed a traditional model aimed at minimizing total costs. Subsequently, considering the integration of passenger and freight co-transportation with collaborative delivery between buses and courier vehicles, we formulated a model for location and collaborative routing planning under the passenger-freight co-transportation mode. This model addresses the pickup and delivery problem with time windows while accounting for demand uncertainty. Meanwhile, for the first model, a hybrid heuristic algorithm based on the moth–flame optimization (MFO) and single-parent genetic algorithm (SGA) is designed and implemented. The validation of the effectiveness of this algorithm is completed using standard arithmetic examples. For the second model, the rural route optimization method and the improved tabu search algorithm are developed, considering the pickup and delivery sequence. Finally, through empirical analysis in three townships in Hanzhong City, the study verifies the feasibility of the proposed models and methods, along with the advantages of the passenger and freight integration model in rural areas.

The KOMPSAT-2 (KOrea Multi-Purpose SATellite-2) was launched in July 28, 2006 and has been normally operated. Mission planning system provides the capability to make conflict-free satellite mission schedule by predicting and analyzing satellite orbital events and users’ imaging requests. The amount of satellite mission planning has been increased due to the growing number of image requests. In order to meet the demands of lots of satellite mission planning activities, all mission plans have to be automatically verified without human intervention. Automation concept is also needed because these planning activities have to be done by the pre-defined timeline. To resolve these problems, MPVS(Mission Planning Verification System) has been developed by mission planning team. The MPVS automatically verifies mission plan with satellite contact schedules, resources and constraints. This paper presents the operational concept of the MPVS which verifies mission plan of the KOMPSAT-2. Also design architecture of the MPVS which is composed of the mission planning verification module, command planning verification module, contact schedule generation module, and database including resources and constraints is presented. This paper shows operation results of the MPVS and validity of the KOMPSAT-2 operation.

Agile Earth observation satellite (AEOS) mission planning (AEOSMP) problem aims to optimize observation efficiency by selecting and scheduling tasks from the Earth’s surface, subject to complex resource constraints. Increased flexibility of AEOS presents challenges for autonomous mission planning and scheduling. Deep reinforcement learning (DRL) and clustering tasks are two approaches to enhance the autonomy and observation efficiency of AEOSMP. This letter introduces two innovative algorithms to tackle the AEOSMP problem: the Sequential Clique Clustering and PPO Planning algorithm (SCC-PPO) and the Simultaneous Clustering and Planning PPO Algorithm (SCP-PPO). SCC-PPO initially partitions the mission tasks into cliques, followed by planning. In contrast, SCP-PPO combines clustering and planning into a single, concurrent process. Numerical simulations reveal that SCP-PPO enhances the observation reward by 1.01% to 11.43% compared to SCC-PPO.

Intermodal hub-and-spoke logistic network design with differentiated services: The case of China Railway Express

This paper proposes a novel hybrid excited generator, which is suitable for a variable-speed wind power generation system. Two sets of excitation sources are employed, which are permanent magnets (PMs) on the rotor and the field windings on the stator. The rotor is design with PM-iron structure, namely the PMs and iron poles are alternatively located in rotor, which can provide PM excitation and flux modulating simultaneously. Meanwhile, the adjacent stator teeth are designed with different height, which enables the stator with flux modulating effect, and, therefore, bidirectional flux modulating can be achieved. Some specific harmonics excited by the rotor PMs can be effectively adjusted by the field current, and the back electromotive force and electromagnetic torque can be regulated accordingly. Therefore, the proposed generator can achieve constant voltage control and maximum power point tracking control by controlling the field current. The design considerations and field regulating principle are analytically investigated. An improved Tabu search algorithm is used to optimize the proposed generator, and finite-element method is used to realize preliminary study of the electromagnetic performances. A prototype is manufactured and experimental tests are conducted to verify the theoretical analysis and control strategies.

The scale of Earth observation satellite system continues to expand and its application is becoming more and more extensive, which puts forward higher requirements for satellite mission planning system. This paper summarizes and analyzes some key issues in the mission planning of earth observation satellites. Firstly, the development status and the trend of earth observation satellites are introduced. Secondly, we summarize and analyze complex requirements of observation decomposition method, the priority of requirements determination method, the model construction and solving methods of single-satellite scheduling, multi-satellite integrated scheduling and multi-satellite dynamic scheduling in the process of requirements Integrated Preprocessing and mission planning. Finally, we discuss the problems that still need to be solved and the direction of research.

The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). It consists on a set of locations that should visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. In this paper, a temporal constraint model is implemented and tested by performing Backtracking search in several missions where its complexity has been incrementally modified. The experimental phase consists on two different phases. On the one hand, several mission simulations containing (n) UAVs using different sensors and characteristics located in different waypoints, and (m) requested tasks varying mission priorities have been carried out. On the other hand, the second experimental phase uses a backtracking algorithm to look through the whole solutions space to measure the scalability of the problem. This scalability has been measured as a relation between the number of tasks to be performed in the mission and the number of UAVs needed to perform it.

Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles

The CSDP Mission Planning Process of the European Union: Innovations and Shortfalls

In this paper, we proposed a new method for solving the problem of mission planning for large area target scanning by multiple Earth Observation Satellites. Firstly, based on a variety of physical constraints and specific mission constraints, the mission planning model is established. Then a simple and efficient area decomposition method is designed, by using this method and combining the physical constraints, the area target visibility extraction can be completed. Based on this method, the mission planning model can be simplified to an integer programming model. Then the adaptive genetic algorithm is designed to solve this model. Finally, the effectiveness and practicability of the proposed method are verified by simulation experiments.

The rapid development of modern defense technology decreased the USVs' attacking effect greatly, autonomous formation cluster attack techniques of USVs has became one of the key technologies of future naval warfare, mission planning among USVs is the key for them to complete tasks smoothly and efficiently. Regarding the cluster attack mission planning problem as multi-constrained task allocation process, building mission planning model, and improve particle initialization and optimization process combined with distributed auction mechanism(DAM) and Biogeography-Based Optimization algorithm(BBO). Simulation result indicates that the program achieved with distributed auction mechanism particle swarm optimization could fully meet the requirements of USVs' cluster attack missions, and shows better convergence compared with traditional particle swarm optimization(PSO) and other swarm intelligence algorithms.

Satellite mission planning is the basis and top-level work of space missions and the beginning of each space mission. Therefore, the scientific research of satellite mission planning is very important. By analyzing the existing research results, we can know that the research on task planning mainly focuses on three aspects: research objects, established model, and solution algorithm. Starting from these three aspects vertically and then horizontally, this paper comprehensively discusses the theoretical basis, application, and advantages and disadvantages of related technologies in the research literature in recent years. Finally, based on the research on satellite mission planning, this paper puts forward its own views on the future development direction and research focus.

Autonomous mission planning of Earth observation satellite based on onboard cloud detection

Autonomous UAV systems are increasingly touted as the dominant future paradigm for inspecting civil infrastructure. Within this study, we have designed and developed a cloud system for high-level path planning, monitoring, and testing of autonomous UAV missions for inspecting infrastructures such as power lines, power towers, bridges, and railways. The software architecture is based on identified system’s functional and non-functional requirements. The cloud system is intended for UAV inspection operators and therefore should support multiple concurrent users. The microservice architecture has assured independence between functionalities, allowing independent scaling resulting in the fast processing time of near-optimal route calculation for UAVs reaching inspection targets. Furthermore, the independence between the services facilitates feature addition and future development. The system robustness is assured by containerizing services and continuously deploying to the Kubernetes cluster distributed across multiple worker nodes. Kubernetes scaling properties have enabled multiple concurrent users regardless of heavy computations for inspection paths. Application load testing has resulted in low processing time when individual services are scaled. Calculated inspection paths are validated for real-world inspection by employing the UAV Gazebo simulation based on a 3D dynamic model with an onboard flight controller, leading the UAV through the waypoints provided by the cloud system. By containerizing the simulation and deploying it within the cluster, we have enabled developers and users to test paths before sending the real-world UAVs to the inspection.