Search Results (69)

This study proposes a hierarchical framework with task priority perception for mission planning, to enhance multi-UAV coordination in maritime emergency search and rescue. By establishing a hierarchical decoupling optimization mechanism, the complex multi-region coverage problem is decomposed into two stages: task allocation and path planning. First, a coverage voyage estimation model is constructed based on regional geometric features to provide basic data for subsequent task allocation. Second, an improved multi-objective, multi-population grey wolf optimizer (IM2GWO) is designed to solve the task allocation problem; this integrates adaptive genetic operations and the multi-population coevolutionary mechanism. Finally, a globally optimal coverage path is generated based on the improved dynamic programming (DP). Simulation results indicate that the proposed method effectively reduces total task duration while boosting overall coverage benefits through the aggregation of high-value regions. IM2GWO demonstrates statistically superior performance with respect to the Pareto front distribution index across all test scenarios. Meanwhile, the path planning module based on DP can effectively reduce the overall coverage path cost. Full article

Technological roadmaps are essential tools for managing and planning complex projects, especially in the rapidly evolving field of space exploration. Defined as dynamic schedules, they support strategic and long-term planning while coordinating current and future objectives with particular technology solutions. Currently, the available methodologies are mostly built on experts’ opinions and in just few cases, methodologies and tools have been developed to support the decision makers with a rational approach. In any case, all the available approaches are meant to draw “ideal” maturation plans. Therefore, it is deemed essential to develop an integrate new algorithms able to decision guidelines on “non-nominal” scenarios. In this context, Politecnico di Torino, in collaboration with the European Space Agency (ESA) and Thales Alenia Space–Italia, developed the Technology Roadmapping Strategy (TRIS), a multi-step process designed to create robust and data-driven roadmaps. However, one of the main concerns with its initial implementation was that TRIS did not account for time and budget estimates specific to the space exploration environment, nor was it capable of generating alternative development paths under constrained conditions. This paper discloses two main significant updates to TRIS methodology: (1) improved time and budget estimation to better reflect the specific challenges of space exploration scenarios and (2) the capability of generating alternative roadmaps, i.e., alternative technological maturation paths in resource-constrained scenarios, balancing financial and temporal limitations. The application of the developed routines to available case studies confirms the tool’s ability to provide consistent planning outputs across multiple scenarios without exceeding 20% deviation from expert-based judgements available as reference. The results demonstrate the potential of the enhanced methodology in supporting strategic decision making in early-phase mission planning, ensuring adaptability to changing conditions, optimized use of time and financial resources, as well as guaranteeing an improved flexibility of the tool. By integrating data-driven prioritization, uncertainty modeling, and resource-constrained planning, TRIS equips mission planners with reliable tools to navigate the complexities of space exploration projects. This methodology ensures that roadmaps remain adaptable to changing conditions and optimized for real-world challenges, supporting the sustainable advancement of space exploration initiatives. Full article

Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework’s superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems. Full article

With the rapid advancement of space technology, satellites are playing an increasingly vital role in fields such as Earth observation, communication and navigation, space exploration, and military applications. Efficiently deploying satellite missions under multi-objective, multi-constraint, and dynamic environments has become a critical challenge in the current aerospace domain. This paper integrates the concepts of game theory and proposes a distributed collaborative task model suitable for on-orbit satellite mission planning. A two-player impulsive maneuver game model is constructed using differential game theory. Based on the ideas of Nash equilibrium and distributed collaboration, multi-agent technology is applied to the distributed collaborative task planning, achieving collaborative allocation and countermeasure strategies for multi-objective and multi-satellite scenarios. Experimental results demonstrate that the method proposed in this paper exhibits good adaptability and robustness in multiple impulse scheduling, maneuver strategy iteration, and heterogeneous resource utilization, providing a feasible technical approach for mission planning and game confrontation in satellite clusters. Full article

Advancements in Unmanned Aerial Vehicle (UAV) technologies have increased exponentially in recent years, with UAV swarm being a key area of interest. UAV swarm overcomes the energy reserve, payload, and single-objective limitations of single UAVs, enabling broader mission scopes. Despite these advantages, UAV swarm has yet to see widespread application within global industry. A leading factor hindering swarm application within industry is the divide that currently exists between the functional capacity of modern UAV swarm systems and the functionality required by legislation. This paper investigates this divide through an overview of global legislative practice, contextualized via a case study of Australia’s UAV regulatory environment. The overview highlighted legislative objectives that coincided with open challenges in the UAV swarm literature. These objectives were then formulated into analysis criteria that assessed whether systems presented sufficient functionality to address legislative concern. A systematic review methodology was used to apply analysis criteria to multi-objective UAV swarm mission planning systems. Analysis focused on multi-objective mission planning systems due to their role in defining the functional capacity of UAV swarms within complex real-world operational environments. This, alongside the popularity of these systems within the modern literature, makes them ideal candidates for defining new enabling technologies that could address the identified areas of weakness. The results of this review highlighted several legislative considerations that remain under-addressed by existing technologies. These findings guided the proposal of enabling technologies to bridge the divide between functional capacity and legislative concern. Full article

A cooperative game negotiation strategy considering multiple constraints is proposed for distributed impulsive multi-spacecraft approach missions in the presence of defending spacecraft. It is a dual-stage decision-making method that includes offline trajectory planning and online distributed negotiation. In the trajectory planning stage, a relative orbital dynamics model is first established based on the Clohessy–Wiltshire (CW) equations, and the state transition equations for impulsive maneuvers are derived. Subsequently, a multi-objective optimization model is formulated based on the NSGA-II algorithm, utilizing a constraint dominance principle (CDP) to address various constraints and generate Pareto front solutions for each spacecraft. In the distributed negotiation stage, the negotiation strategy among spacecraft is modeled as a cooperative game. A potential function is constructed to further analyze the existence and global convergence of Nash equilibrium. Additionally, a simulated annealing negotiation strategy is developed to iteratively select the optimal comprehensive approach strategy from the Pareto fronts. Simulation results demonstrate that the proposed method effectively optimizes approach trajectories for multi-spacecraft under complex constraints. By leveraging inter-satellite iterative negotiation, the method converges to a Nash equilibrium. Additionally, the simulated annealing negotiation strategy enhances global search performance, avoiding entrapment in local optima. Finally, the effectiveness and robustness of the dual-stage decision-making method were further demonstrated through Monte Carlo simulations. Full article

Developing spacecraft for efficient aerocapture missions demands managing extreme aerothermal environments, precise controls, and atmospheric uncertainties. Successful designs must integrate vehicle airframe considerations with trajectory planning, adhering to launcher dimension constraints and ensuring robustness against atmospheric and insertion uncertainties. To advance robust multi-objective optimization in this field, a new framework is presented, designed to rapidly analyze and optimize non-thrusting, fixed angle-of-attack aerocapture-capable spacecraft and their trajectories. The framework employs a three-degree-of-freedom atmospheric flight dynamics model incorporating planet-specific characteristics. Aerothermal effects are approximated using established Sutton–Graves, Tauber–Sutton, and Stefan–Boltzmann relations. The framework computes the resulting post-atmospheric pass orbit using an orbital element determination algorithm to estimate fuel requirements for orbital corrective maneuvers. A novel algorithm that consolidates multiple objective functions into a unified cost function is presented and demonstrated to achieve superior optima with computational efficiency compared to traditional multi-objective optimization approaches. Numerical examples demonstrate the methodology’s effectiveness and computational cost at optimizing terrestrial and Martian aerocapture maneuvers for minimum fuel, heat loads, peak heat transfers, and an overall optimal trajectory, including volumetric considerations. Full article

Maritime unmanned aerial vehicles (UAVs) equipped with inertial navigation systems (INS) are prone to error accumulation, which can lead to excessive positioning errors and hinder their ability to perform long distance missions. To address this issue, this study first constructs a directed graph network for a flight area based on start and end points as well as error correction points. A multi-objective route planning model is then developed for a UAV, aiming to minimize both the flight distance and the number of positioning corrections. Considering the UAV’s turning radius, a trajectory length calculation model based on 3D Dubins curves is designed. Subsequently, a forward labeling-based multi-objective path planning algorithm is proposed to develop an optimization model. Experimental results demonstrate that the proposed method can effectively constrain the UAV’s horizontal and vertical positioning errors within 2.5 m, while optimally balancing flight distance and positioning accuracy to ensure the successful execution of long-range maritime UAV missions. The comparative results demonstrate that, while satisfying the positioning error requirements, our proposed method achieves a reduction of over 1.5% in total flight distance for maritime UAVs compared to the NSGA-II algorithm. Full article

The Chinese Chang’E-7 (CE-7) mission is planned to land in the lunar south polar region, and then deploy a mini-flying probe to fly into the cold trap to detect the water ice. The selection of a landing site is crucial for ensuring both a safe landing and the successful achievement of its scientific objectives. This study presents a method for landing site selection in the challenging environment of the lunar south pole, utilizing multi-source remote sensing data. First, the likelihood of water ice in all cold traps within 85°S is assessed and prioritized using neutron spectrometer and hyperspectral data, with the most promising cold traps selected for sampling by CE-7’s mini-flying probe. Slope and illumination data are then used to screen feasible landing sites in the south polar region. Feasible landing sites near cold traps are aggregated into larger landing regions. Finally, high-resolution illumination maps, along with optical and radar images, are employed to refine the selection and identify the optimal landing sites. Six potential landing sites around the de Gerlache crater, an unnamed cold trap at (167.10°E, 88.71°S), Faustini crater, and Shackleton crater are proposed. It would be beneficial for CE-7 to prioritize mapping these sites post-launch using its high-resolution optical camera and radar for further detailed landing site investigation and evaluation. Full article

This paper proposes a task planning method that integrates deep Q-learning network (DQN) with matrix sorting for Earth-oriented static multi-target cooperative observation tasks. The approach addresses emergent satellite failures in imaging constellations by eliminating the need for network model retraining during satellite malfunctions. It enables real-time generation of optimal task allocation schemes in contingency scenarios, ensuring efficient and adaptive task planning. Firstly, a mission scenario model is established by formulating task constraints and defining optimization objectives; secondly, a deep reinforcement learning framework is constructed to output the observation target sequence; then, the observation target sequence is transformed into a target sequence matrix, and a matrix-sorting planning method is proposed to carry out the optimal assignment of the task; lastly, a replanning method is designed for sudden satellite failure and insertion of urgent tasks. The experimental results show that the method has fast task planning speed, high task completion, and immediate task replanning capability. Full article

With the rapid increase in the number of Earth Observation Satellites (EOSs), research on autonomous mission scheduling has become increasingly critical for optimizing satellite sensor operations. While most existing studies focus on static environments or initial planning states, few address the challenge of dynamic request replanning for real-time sensor management. In this paper, we tackle the problem of multi-satellite rapid mission replanning under dynamic batch-arrival observation requests. The objective is to maximize overall observation revenue while minimizing disruptions to the original scheme. We propose a framework that integrates stochastic master-satellite mission allocation with single-satellite replanning, supported by reactive scheduling policies trained via deep reinforcement learning. Our approach leverages mission sequence modeling with attention mechanisms and time-attitude-aware rotary positional encoding to guide replanning. Additionally, scalable embeddings are employed to handle varying volumes of dynamic requests. The mission allocation phase efficiently generates assignment solutions using a pointer network, while the replanning phase introduces a hybrid action space for direct task insertion. Both phases are formulated as Markov Decision Processes (MDPs) and optimized using the PPO algorithm. Extensive simulations demonstrate that our method significantly outperforms state-of-the-art approaches, achieving a 15.27% higher request insertion revenue rate and a 3.05% improvement in overall mission revenue rate, while maintaining a 1.17% lower modification rate and achieving faster computational speeds. This demonstrates the effectiveness of our approach in real-world satellite sensor applications. Full article

Three-dimensional (3D) dynamic trajectory planning for Autonomous Underwater Vehicles (AUVs) is associated with significant challenges such as balancing the trajectory quality, computational efficiency, and environmental adaptability within complex dynamic environments. To tackle these challenges, this paper proposes a novel trajectory planning framework by integrating Proximal Policy Optimization (PPO) and an Improved Interfered Fluid Dynamic System (IIFDS). The IIFDS serves as the planning layer, generating obstacle-adaptive trajectories for AUVs through the dynamic adjustment of flow field parameters. Meanwhile, PPO functions as the learning and decision-making layer, optimizing critical parameters in IIFDS, including repulsion response coefficients, tangential response coefficients, and directional coefficients, to enhance adaptability and real-time decision-making. To meet specific mission requirements, the IIFDS incorporates dynamics and kinematics constraints, while the PPO reward function is improved with a multi-objective dynamic structure. This reward design integrates objectives such as obstacle avoidance, target distance minimization, trajectory smoothness, dynamics constraints, and energy efficiency. These enhancements address sparse reward issues effectively and significantly improve the convergence and practical applicability of trajectory planning. Additionally, a diverse and dynamically complex obstacle environment is constructed for model training and performance evaluation. The experimental results demonstrate that the proposed framework efficiently generates smooth, energy-efficient, and collision-free trajectories in high-density dynamic obstacle scenarios. The framework exhibits strong robustness, excellent generalization capabilities, and offers a reliable solution for 3D dynamic trajectory planning for AUVs. Full article

As Unmanned Aerial Vehicles (UAVs) are becoming crucial in modern warfare, research on autonomous path planning is becoming increasingly important. The conflicting nature of the optimization objectives characterizes path planning as a multi-objective optimization problem. Current research has predominantly focused on developing new optimization algorithms. Although being able to find the mathematical optimum is important, one also needs to ensure this optimum aligns with the decision-maker’s (DM’s) most preferred solution (MPS). In particular, to align these, one needs to handle the DM’s preferences on the relative importance of each optimization objective. This paper provides a comprehensive overview of all preference handling techniques employed in the military UAV path planning literature over the last two decades. It shows that most of the literature handles preferences by the overly simplistic method of scalarization via weighted sum. Additionally, the current literature neglects to evaluate the performance (e.g., cognitive validity and modeling accuracy) of the chosen preference handling technique. To aid future researchers handle preferences, we discuss each employed preference handling technique, their implications, advantages, and disadvantages in detail. Finally, we identify several directions for future research, mainly related to aligning the mathematical optimum to the MPS. Full article

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment. Full article

Multi-satellite imaging mission planning (MSIMP) is an important focus in the field of satellite application. MSIMP involves a variety of coupled constraints and optimization objectives, which often require extensive simulation and evaluation when solving, leading to high computational costs and slow response times for traditional algorithms. Surrogate model expensive multi-objective evolutionary algorithms (SM-EMOEAs), which are computationally efficient and converge quickly, are effective methods for the solution of MSIMP. However, the recent advances in this field have not been comprehensively summarized; therefore, this work provides a comprehensive overview of this subject. Firstly, the basic classification of MSIMP and its different fields of application are introduced, and the constraints of MSIMP are comprehensively analyzed. Secondly, the MSIMP problem is described to clarify the application scenarios of traditional optimization algorithms in MSIMP and their properties. Thirdly, the process of MSIMP and the classical expensive multi-objective evolutionary algorithms are reviewed to explore the surrogate model and the expensive multi-objective evolutionary algorithms based on MSIMP. Fourthly, improved SM-EMOEAs for MSIMP are analyzed in depth in terms of improved surrogate models, adaptive strategies, and diversity maintenance and quality assessment of the solutions. Finally, SM-EMOEAs and SM-EMOEA-based MSIMP are analyzed in terms of the existing literature, and future trends and directions are summarized. Full article