Search Results (1,822)

In three-dimensional (3D) logistics environments, finding optimal paths for unmanned aerial vehicles (UAVs) is challenging due to positioning inaccuracies that require ground-based corrections. These inaccuracies are exacerbated in harsh environments, leading to a significant risk of correction failure. This research proposes a multi-objective mixed integer programming model (MILP) that transforms dynamic uncertainties into binary constraints, utilizing a hierarchical sequencing strategy in the Gurobi optimizer to efficiently identify optimal paths. Our simulations indicate that achieving an 80% mission success probability necessitates an optimal path of 104,946 m with nine corrections. For a 100% success rate, the path length increases to 105,874 m, with corrections remaining constant. These results validate the model’s effectiveness in navigating environments with probabilistic constraints, highlighting its potential for addressing complex logistical challenges. Full article

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

This study proposes a hierarchical composite adaptive sliding-mode control strategy to address the strong nonlinear dynamics of a hexarotor Unmanned Aerial Vehicle (UAV) and the external disturbances encountered during flight. First, within the position-control loop, a Terminal Sliding Mode Control (TSMC) is designed to guarantee finite-time convergence of the system states, thereby significantly improving the UAV’s rapid response to complex trajectories. Concurrently, an online Adaptive rates mechanism is introduced to estimate and compensate unknown disturbances and modeling uncertainties in real time, further enhancing disturbance rejection. In the attitude-control loop, a Super-twisting Sliding Mode Control (STSMC) method is employed, where an Adaptive rate law dynamically adjusts the sliding gain to prevent overestimation and high-frequency chattering, while ensuring fast convergence and smooth response. To comprehensively validate the feasibility and superiority of the proposed scheme, a representative helical trajectory-tracking experiment was conducted and systematically compared, via simulation, against conventional control methods. Experimental results demonstrate that the proposed approach achieves stable control within 0.15 s, with maximum position and attitude tracking errors of 0.05 m and 0.15°, respectively. Moreover, it exhibits enhanced robustness and adaptability to external disturbances and parameter uncertainties, effectively improving the motion-control performance of hexacopter UAVs in complex missions. Full article

Shape memory polymer composite (SMPC) hinges have been researched as deployable structures in space missions due to their stable and controllable shape recovery behaviors. The elastic energy of the fabrics plays a dominant role in predicting the recovered shape of the hinges, as it strongly drives shape restoration. In this research, the shape recovery behaviors of SMPC hinges are numerically investigated by applying an equation that accounts for the hysteresis characteristics of the fabric reinforcement. The constitutive equation integrates the Mooney–Rivlin model, a viscoelastic, stored energy model, to characterize the hyperelastic properties varying with time, temperature, and shape recovery behaviors of the SMP matrix. Additionally, polynomial functions are introduced to represent the hysteresis effects and energy dissipation behavior of the fabrics. Since the elasticity of fabrics significantly affects the shape recovery of SMPCs, the developed constitutive equation enables accurate prediction of the recovered configuration. Finite element method analysis is performed based on this model and validated through comparison with experimental results. Finally, the constitutive equation is applied to investigate the shape memory response of SMPC hinges. The simulations present the significant design factors to increase the shape recovery ratio of the SMPC hinges. Full article

Collaborative unmanned aerial vehicle (UAV) swarm operations using the open-source PX4–ROS2 system have been extensively studied for reconnaissance and autonomous missions. PX4–ROS2 utilizes data distribution service (DDS) middleware to ensure network flexibility and support scalable operations. DDS enables decentralized information exchange through its discovery protocol. However, in dense swarm environments, the default initialization process of this protocol generates considerable communication overhead, which hinders reliable peer detection among UAVs. This study introduces an optimized DDS discovery scheme incorporating two key strategies: a preloading method that embeds known participant data before deployment, and a dynamic network awareness approach that regulates discovery behavior based on real-time connectivity. Integrated into PX4–ROS2, the proposed scheme was assessed through both simulations and real-world testing. Results demonstrate that the optimized discovery process reduced peak packet traffic by over 90% during the initial exchange phase, thereby facilitating more stable and scalable swarm operations in wireless environments. Full article

Modern robotic systems are evolving toward conducting missions based on semantic knowledge. Such systems require environmental modeling as essential for successful mission execution. However, there is an inefficiency in that manual modeling is required whenever a new environment is given, and adaptive modeling that can adapt to the environment is needed. In this paper, we propose an integrated framework that enables autonomous environmental modeling for service robots by fusing domain knowledge with open-vocabulary-based Vision-Language Models (VLMs). When a robot is deployed in a new environment, it builds occupancy maps through autonomous exploration and extracts semantic information about objects and places. Furthermore, we introduce human–robot collaborative modeling beyond robot-only environmental modeling. The collected semantic information is stored in a structured database and utilized on demand. To verify the applicability of the proposed framework to service robots, experiments are conducted in a simulated home environment and a real-world indoor corridor. Through the experiments, the proposed framework achieved over 80% accuracy in semantic information extraction in both environments. Semantic information about various types of objects and places was extracted and stored in the database, demonstrating the effectiveness of DK-SMF for service robots. Full article

This study addresses the selection and application of composite materials for aerospace systems operating in extreme environmental conditions, with a particular focus on high-altitude pseudo-satellites (HAPS). This research is centered on the development of a 400 kg autonomous aerial vehicle (AAV) capable of sustained operations at altitudes of up to 30 km. KMU-3’s microstructure, comprising high-modulus carbon fibers (5–7 µm diameter) in a 5-211B epoxy matrix, provides a high specific strength (1000–2500 MPa), low density (1.6–1.8 g/cm³), and thermal stability (−60 °C to +600 °C), ensuring structural integrity in stratospheric conditions. The mechanical, thermal, and aerodynamic properties of KMU-3-based truss structures were evaluated using finite element method (FEM) simulations, computational fluid dynamics (CFD) analysis, and experimental prototyping. The results indicate that ultra-thin KMU-3 with a wall thickness of 0.1 mm maintains structural integrity under dynamic loads while minimizing overall mass. A novel thermal bonding technique employing 5-211B epoxy resin was developed, resulting in joints with a shear strength of 40 MPa and fatigue life exceeding 10⁶ cycles at 50% load. The material properties remained stable across the operational temperature range of −60 °C to +80 °C. An optimized fiber orientation (0°/90° for longerons and ±45° for diagonals) enhanced the resistance to axial, shear, and torsional stresses, while the epoxy matrix ensures radiation resistance. Finite element method (FEM) and computational fluid dynamics (CFD) analyses, validated by prototyping, confirm the performance of ultra-thin (0.1 mm) truss structures, achieving a lightweight (45 kg) design. These findings provide a validated, lightweight framework for next-generation HAPS, supporting extended mission durations under harsh stratospheric conditions. Full article

The extensive deployment of quadrotors in complex environmental missions has revealed a critical challenge: degradation of trajectory tracking accuracy due to time-varying wind disturbances. Conventional model-based controllers struggle to adapt to nonlinear wind field dynamics, while data-driven approaches often suffer from catastrophic forgetting that compromises environmental adaptability. This paper proposes a reinforcement learning framework with continual adaptation capabilities to enhance robust tracking performance for quadrotors operating in dynamic wind fields. We develop a continual reinforcement learning framework integrating continual backpropagation algorithms with reinforcement learning. Initially, a foundation model is trained in wind-free conditions. When wind disturbance intensity undergoes gradual variations, a neuron utility assessment mechanism dynamically resets inefficient neurons to maintain network plasticity. Concurrently, a multi-objective reward function is designed to improve both training precision and efficiency. The Gazebo/PX4 simulation platform was utilized to validate the wind disturbance stepwise growth and stochastic variations. This approach demonstrated a reduction in the root mean square error of trajectory tracking when compared to the standard PPO algorithm. The proposed framework resolves the plasticity loss problem in deep reinforcement learning through structured neuron resetting, significantly enhancing the continual adaptation capabilities of quadrotors in dynamic wind fields. Full article

The integration of diverse robotic platforms with varying payload capacities is a critical challenge in swarm robotics and autonomous systems. This paper presents a robust, modular framework designed to manage and coordinate heterogeneous swarms of autonomous vehicles, including terrestrial, aerial, and aquatic platforms. Built on the Robot Operating System (ROS) and integrated with C++ and ArduPilot, the framework enables real-time communication, autonomous decision-making, and mission execution across multi-domain environments. Its modular design supports seamless scalability and interoperability, making it adaptable to a wide range of applications. The proposed framework was evaluated through simulations and real-world experiments, demonstrating its capabilities in collision avoidance, dynamic mission planning, and autonomous target reallocation. Experimental results highlight the framework’s robustness in managing UAV swarms, achieving 100% collision avoidance success and significant operator workload reduction, in the tested scenarios. These findings underscore the framework’s potential for practical deployment in applications such as disaster response, reconnaissance, and search-and-rescue operations. This research advances the field of swarm robotics by offering a scalable and adaptable solution for managing heterogeneous autonomous systems in complex environments. Full article

Static Priority-based Multiple Access (SPMA) is an emerging and promising wireless MAC protocol which is widely used in Unmanned Aerial Vehicle (UAV) networks. UAV (Unmanned Aerial Vehicle) networks, also known as drone networks, refer to a system of interconnected UAVs that communicate and collaborate to perform tasks autonomously or semi-autonomously. These networks leverage wireless communication technologies to share data, coordinate movements, and optimize mission execution. In SPMA, traffic arriving at the UAV network node can be divided into multiple priorities according to the information timeliness, and the packets of each priority are stored in the corresponding queues with different thresholds to transmit packet, thus guaranteeing the high success rate and low latency for the highest-priority traffic. Unfortunately, the multi-priority queue scheduling of SPMA deprives the packet transmitting opportunity of low-priority traffic, which results in unfair conditions among different-priority traffic. To address this problem, in this paper we propose the method of Adaptive Credit-Based Shaper with Reinforcement Learning (abbreviated as ACBS-RL) to balance the performance of all-priority traffic. In ACBS-RL, the Credit-Based Shaper (CBS) is introduced to SPMA to provide relatively fair packet transmission opportunity among multiple traffic queues by limiting the transmission rate. Due to the dynamic situations of the wireless environment, the Q-learning-based reinforcement learning method is leveraged to adaptively adjust the parameters of CBS (i.e., idleslope and sendslope) to achieve better performance among all priority queues. The extensive simulation results show that compared with traditional SPMA protocol, the proposed ACBS-RL can increase UAV network throughput while guaranteeing Quality of Service (QoS) requirements of all priority traffic. Full article

Effective energy management in Low Earth Orbit satellites is critical, as inefficient energy management can significantly affect mission objectives. The dynamic and harsh space environment further complicates the development of effective energy management strategies. To address these challenges, we propose a Deep Reinforcement Learning approach using Deep-Q Network to develop an adaptive energy management framework for Low Earth Orbit satellites. Compared to traditional techniques, the proposed solution autonomously learns from environmental interaction, offering robustness to uncertainty and online adaptability. It adjusts to changing conditions without manual retraining, making it well-suited for handling modeling uncertainties and non-stationary dynamics typical of space operations. Training is conducted using a realistic satellite electric power system model with accurate component parameters and single-orbit power profiles derived from real space missions. Numerical simulations validate the controller performance across diverse scenarios, including multi-orbit settings, demonstrating superior adaptability and efficiency compared to conventional Maximum Power Point Tracking methods. Full article

The utilization of Unmanned Aerial Vehicles (UAVs) in missions such as reconnaissance and surveillance has grown rapidly, underscoring the need for efficient path planning algorithms that ensure both optimality and collision avoidance. The A-star algorithm is widely used for global path planning due to its ability to generate optimal routes; however, its high computational cost makes it unsuitable for real-time applications, particularly in unknown or dynamic environments. For local path planning, the Artificial Potential Field (APF) algorithm enables real-time navigation by attracting the UAV toward the target while repelling it from obstacles. Despite its efficiency, APF suffers from local minima and limited performance in dynamic settings. To address these challenges, this paper proposes the A-star-Guided Potential Field (AGPF) algorithm, which integrates the strengths of A-star and APF to achieve robust performance in both global and local path planning. The AGPF algorithm was validated through simulations conducted in the Robot Operating System (ROS) environment. Simulation results demonstrate that AGPF produces smoother and more optimal paths than A-star, while avoiding the local minima issues inherent in APF. Furthermore, AGPF effectively handles moving and previously unknown obstacles by generating real-time avoidance trajectories, demonstrating strong adaptability in dynamic and uncertain environments. Full article

This follow-up scientific case study builds on prior research to explore the computational challenges of applying quantum algorithms to financial asset management, focusing specifically on solving the graph-cut problem for investment recommendation. Unlike our prior study, which focused on idealized QAOA performance, this work introduces a graph compression pipeline that enables QAOA deployment under real quantum hardware constraints. This study investigates quantum-accelerated spectral graph compression for financial asset recommendations, addressing scalability and regulatory constraints in portfolio management. We propose a hybrid framework combining the Quantum Approximate Optimization Algorithm (QAOA) with spectral graph theory to solve the Max-Cut problem for investor clustering. Our methodology leverages quantum simulators (cuQuantum and Cirq-GPU) to evaluate performance against classical brute-force enumeration, with graph compression techniques enabling deployment on resource-constrained quantum hardware. The results underscore that efficient graph compression is crucial for successful implementation. The framework bridges theoretical quantum advantage with practical financial use cases, though hardware limitations (qubit counts, coherence times) necessitate hybrid quantum-classical implementations. These findings advance the deployment of quantum algorithms in mission-critical financial systems, particularly for high-dimensional investor profiling under regulatory constraints. Full article

Due to the limited endurance of UAVs, especially in scenarios involving large areas and dense target nodes, it is challenging for multiple UAVs to complete diverse tasks while ensuring timely execution. Toward this, we propose a cross-platform system consisting of an aircraft carrier (AC) and multiple UAVs, which makes unified task planning for included heterogeneous platforms to maximize the efficiency of the entire combat system. The carrier-based UAV swarm mission planning problem is formulated to minimize completion time and resource utilization, taking into account large-scale targets, multi-type tasks, and multi-obstacle environments. Since the problem is complex, we design a decoupled framework to simplify the solution by decomposing it into two levels: upper-level AC path planning and bottom-level multi-UAV cooperative mission planning. At the upper level, a drop point determination method and a discrete genetic algorithm incorporating improved A* (DGAIIA) are proposed to plan the AC’s path in the presence of no-fly zones and radar threats. At the bottom level, an improved differential evolution algorithm with a market mechanism (IDEMM) is proposed to minimize task completion time and maximize UAV utilization. Specifically, a dual-switching search strategy and a neighborhood-first buying-and-selling mechanism are developed to improve the search efficiency of the IDEMM. Simulation results validate the effectiveness of both the DGAIIA and IDEMM. An animation of the simulation results is available at simulation section. Full article

With the rapid advancement of maritime sports, the integration of auxiliary unmanned surface vehicles (USVs) has emerged as a promising solution to enhance the efficiency and safety of maritime education, particularly in tasks such as buoy deployment and escort operations. This paper presents a novel high-precision trajectory tracking control algorithm designed to ensure stable navigation of the USVs along predefined competition boundaries, thereby facilitating the reliable execution of buoy placement and escort missions. First, the paper proposes an improved adaptive fractional-order nonsingular fast terminal sliding mode control (AFONFTSMC) algorithm to achieve precise trajectory tracking of the reference path. To address the challenges posed by unknown environmental disturbances and unmodeled dynamics in marine environments, a nonlinear lumped disturbance observer (NLDO) with exponential convergence properties is proposed, ensuring robust and continuous navigation performance. Additionally, an artificial potential field (APF) method is integrated to dynamically mitigate collision risks from both static and dynamic obstacles during trajectory tracking. The efficacy and practical applicability of the proposed control framework are rigorously validated through comprehensive numerical simulations. Experimental results demonstrate that the developed algorithm achieves superior trajectory tracking accuracy under complex sea conditions, thereby offering a reliable and efficient solution for maritime sports education and related applications. Full article