Search Results (1,073)

This paper proposes the Quantum-behaved Loser Reverse-learning Differential Evolution (QLRDE) algorithm to address the inherent limitations of the standard Differential Evolution (DE) algorithm, including slow convergence speed and the premature stagnation in local optima. QLRDE incorporates three innovations: quantum-behaved mutation strategies suppress premature convergence by leveraging quantum mechanics, the Loser Reverse-Learning Mechanism enhances diversity by reconstructing inferior individuals through opposition-based learning, and an adaptive parameter adjustment mechanism balances exploration and exploitation to improve robustness and convergence efficiency. Experimental evaluations on twelve benchmark functions confirm that QLRDE demonstrates better performance than existing algorithms in terms of search capability and convergence speed. Furthermore, QLRDE is employed for the 3D UAV path planning problem. QLRDE can generate B-Spline-based smooth flight paths and incorporate real-world constraints into the cost function. Simulation results confirm that QLRDE outperforms several competing algorithms with respect to path quality, computational efficiency, and robustness. Full article

To reduce manual operation and enhance the intelligence of the high-altitude maintenance wall-climbing robot during its operation, path planning and autonomous navigation need to be implemented. Due to non-uniform magnetic adhesion between the wall-climbing robot and the steel plate, often caused by variations in steel thickness or surface pitting, the wall-climbing robot may experience motion deviations and deviate from its planned trajectory. In order to obtain the actual deviation from the expected trajectory, it is necessary to accurately locate the wall-climbing robot. This allows for the generation of precise control signals, enabling trajectory correction and ensuring high-precision autonomous navigation. Therefore, this paper proposes an external visual localization system based on a pan–tilt laser tracker unit. The system utilizes a zoom camera to track an AprilTag marker and drives the pan–tilt platform, while a laser rangefinder provides high-accuracy distance measurement. The robot's three-dimensional (3D) pose is ultimately calculated by fusing the visual and ranging data. However, due to the limited tracking speed of the pan–tilt mechanism relative to the robot’s movement, we introduce an Extended Kalman Filter (EKF) to robustly predict the robot's true spatial coordinates. The robot's three-dimensional coordinates are periodically compared with the predefined Full article

GNN approaches to property valuation typically rely on spatial proximity, assuming that nearby properties exhibit similar price patterns. In practice, this assumption often fails as neighborhood and administrative boundaries create sharp price discontinuities, a form of spatial heterophily. This study proposes a Boundary-Aware Dual-Path Graph Neural Network (BD-GNN), a heterophily-oriented GNN specifically designed for continuous regression tasks. The model uses a dual and adaptive message passing design, separating inter- and intra-boundary pathways and combining them through a learnable gating parameter $\alpha$. This allows it to capture boundary effects while preserving spatial continuity. Experiments conducted on three structurally contrasting housing datasets, namely Bangkok, King County (USA), and Singapore, demonstrate consistent performance improvements over strong baselines. The proposed BD-GNN reduces MAPE by 7.9%, 4.4%, and 4.5% and increases $R^2$ by 3.2%, 0.7%, and 5.0% for the respective datasets. Beyond predictive performance, $\alpha$ provides a clear picture of how spatial and administrative factors interact across urban scales. GNN Explainer provides local interpretability by showing which neighbors and features shape each prediction. BD-GNN bridges predictive accuracy and structural insight, offering a practical, interpretable framework for applications such as property valuation, taxation, mortgage risk assessment, and urban planning. Full article

Unmanned ground vehicles (UGVs) in agriculture face challenges in navigating complex environments due to the presence of dynamic obstacles. This causes several practical problems including mission delays, higher energy consumption, and potential safety risks. This study addresses the challenge by shifting path planning from reactive local avoidance to proactive global optimization. To that end, it integrates aerial imagery from an unmanned aerial vehicle (UAV) to identify dynamic obstacles using a low-latency YOLOv8 detection pipeline. These are translated into georeferenced exclusion zones for the UGV. The UGV follows the optimized path while relying on a LiDAR-based reactive protocol to autonomously detect and respond to any missed obstacles. A farm management information system is used as the central coordinator. The system was tested in 30 real-field trials in a walnut orchard for two distinct scenarios with varying worker and vehicle loads. The system achieved high mission success, with the UGV completing all tasks safely, with four partial successes caused by worker detection failures under afternoon shadows. UAV energy consumption remained stable, while UGV energy and mission time increased during reactive maneuvers. Communication latency was low and consistent. This enabled timely execution of both proactive and reactive navigation protocols. In conclusion, the present UAV–UGV system ensured efficient and safe navigation, demonstrating practical applicability in real orchard conditions. Full article

Balancing convergence accuracy and population diversity remains a fundamental challenge in multi-objective optimization, particularly for complex and constrained engineering problems. To address this issue, this paper proposes a novel Multi-Objective Crested Porcupine Optimizer (MOCPO), inspired by the hierarchical defensive behaviors of crested porcupines. The proposed algorithm integrates four biologically motivated defense strategies—vision, hearing, scent diffusion, and physical attack—into a unified optimization framework, where global exploration and local exploitation are dynamically coordinated. To effectively extend the original optimizer to multi-objective scenarios, MOCPO incorporates a reference-point guided external archiving mechanism to preserve a well-distributed set of non-dominated solutions, along with an environmental selection strategy that adaptively partitions the objective space and enhances solution quality. Furthermore, a multi-level leadership mechanism based on Euclidean distance is introduced to provide region-specific guidance, enabling precise and uniform coverage of the Pareto front. The performance of MOCPO is comprehensively evaluated on 18 benchmark problems from the WFG and CF test suites. Experimental results demonstrate that MOCPO consistently outperforms several state-of-the-art multi-objective algorithms, including MOPSO and NSGA-III, in terms of IGD, GD, HV, and Spread metrics, achieving the best overall ranking in Friedman statistical tests. Notably, the proposed algorithm exhibits strong robustness on discontinuous, multimodal, and constrained Pareto fronts. In addition, MOCPO is applied to UAV path planning in four complex terrain scenarios constructed from real digital elevation data. The results show that MOCPO generates shorter, smoother, and more stable flight paths while effectively balancing route length, threat avoidance, flight altitude, and trajectory smoothness. These findings confirm the effectiveness, robustness, and practical applicability of MOCPO for solving complex real-world multi-objective optimization problems. Full article

As a high-value fruit crop grown worldwide, apples require efficient harvesting solutions to maintain a stable supply. Intelligent harvesting robots represent a promising approach to address labour shortages. This study introduced a Cartesian robot integrated with a continuous-picking end-effector, providing a cost-effective and mechanically simpler alternative to complex articulated arms. The system employed a hand–eye calibration model to enhance positioning accuracy. To overcome the inefficiencies resulting from disordered harvesting sequences and excessive motion trajectories, the harvesting process was treated as a travelling salesman problem (TSP). The conventional fixed-plane return trajectory of Cartesian robots was enhanced using a three-dimensional continuous picking path strategy based on a fixed retraction distance (H). The value of H was determined through mechanical characterization of the apple stem’s brittle fracture, which eliminated redundant horizontal displacements and improved operational efficiency. Furthermore, an improved grey wolf optimizer (IGWO) was proposed for multi-fruit path planning. Simulations demonstrated that the IGWO achieved shorter path lengths compared to conventional algorithms. Laboratory experiments validated that the system successfully achieved vision-based localization and fruit harvesting through optimal path planning, with a fruit picking success rate of 89%. The proposed methodology provides a practical framework for automated continuous harvesting systems. Full article

The construction cost of offshore wind farms (OWFs) is heavily influenced by vessel scheduling and meteorological uncertainties. To address these challenges, this paper proposes a constraint-driven hierarchical optimization framework for the coordinated scheduling of installation vessels (IVs) and transport vessels (TVs). First, a Mixed-Integer Linear Programming (MILP) model is established to describe the operational constraints, which is then decomposed into two interrelated sub-problems: vessel path planning and scheduling optimization. For path planning, the problem is modeled as a Multiple Traveling Salesman Problem (MTSP) to ensure balanced fleet workloads. This stage is solved via a tailored three-stage heuristic combining balanced sweep clustering and penalized local search. For scheduling optimization, a hybrid Earliest Deadline First (EDF)-Simulated Annealing (SA) strategy is employed, where EDF generates a strictly feasible baseline to warm-start the SA optimization. Furthermore, a stochastic optimization approach integrates historical meteorological data to ensure schedule robustness against weather uncertainty. The validity of the framework is supported by two real-world OWF cases, which demonstrate total cost reductions of 15.44% and 13.20%, respectively, under stochastic weather conditions. These results demonstrate its effectiveness in solving high-constraint offshore engineering problems. Full article

In robot motion planning in a space with obstacles, the goal is to find a collision-free path for robots from the start to the target position. Numerous fundamentally different approaches, and their many variants, address this problem depending on the types of obstacles, the dimensionality of the space and the restrictions on robot movements. We present a hierarchical motion planning framework for snake-like robots navigating cluttered environments. At the global level, a bounded Generalized Voronoi Diagram (GVD) generates a maximal-clearance path through complex terrain. To overcome the limitations of pure avoidance strategies, we incorporate a local trajectory optimization layer that enables Obstacle-Aided Locomotion (OAL). This is realized through a simulation-in-the-loop system in CoppeliaSim, where gait parameters are optimized using Particle Swarm Optimization (PSO) based on contact forces and energy efficiency. By coupling high-level deliberative planning with low-level contact-aware control, our approach enhances both adaptability and locomotion efficiency. Experimental results demonstrate improved motion performance compared to conventional planners that neglect environmental contact. Full article

When forest fires occur, timely detection and initial attack are critical for fire prevention. This study focuses on optimizing the cruise path of Unmanned Aerial Vehicles (UAVs) from the perspective of initial attack. It aims to maximize coverage of regions where initial attack success rates are low, shorten the time taken to detect fires, and, in turn, boost detection effectiveness and the initial attack success. In this paper, a path planning strategy, Improved Multi-Objective Crested Porcupine Optimizer (IMOCPO), is proposed. This strategy employs a weighted sum approach to formulate a composite objective function that balances global search and local optimization capabilities, considering practical requirements such as UAV endurance and uneven distribution of risk areas, thus enhancing adaptability in complex forest environments. The weight selection is justified through systematic grid search and validated by sensitivity analysis. The proposed strategy was compared and evaluated with a related strategy using four metrics: high-risk coverage rate, grid coverage rate, Average Distance Risk (ADR), and Average Grid Risk (AGR). Results show that the proposed path planning strategy performs better in these metrics. This study provides an effective solution for optimizing UAV cruise strategies in forest fire monitoring and has practical significance for improving the intelligence of forest fire prevention. Full article

The integration of large models and multimodal foundation models into the low-altitude economy is driving a transformative shift, enabling intelligent, autonomous, and efficient operations for low-altitude vehicles (LAVs). This article provides a comprehensive analysis of the role these large models play within the smart integrated lower airspace system (SILAS), focusing on their applications across the four fundamental networks: facility, information, air route, and service. Our analysis yields several key findings, which pave the way for enhancing the application of large models in the low-altitude economy. By leveraging advanced capabilities in perception, reasoning, and interaction, large models are demonstrated to enhance critical functions such as high-precision remote sensing interpretation, robust meteorological forecasting, reliable visual localization, intelligent path planning, and collaborative multi-agent decision-making. Furthermore, we find that the integration of these models with key enabling technologies, including edge computing, sixth-generation (6G) communication networks, and integrated sensing and communication (ISAC), effectively addresses challenges related to real-time processing, resource constraints, and dynamic operational environments. Significant challenges, including sustainable operation under severe resource limitations, data security, network resilience, and system interoperability, are examined alongside potential solutions. Based on our survey, we discuss future research directions, such as the development of specialized low-altitude models, high-efficiency deployment paradigms, advanced multimodal fusion, and the establishment of trustworthy distributed intelligence frameworks. This survey offers a forward-looking perspective on this rapidly evolving field and underscores the pivotal role of large models in unlocking the full potential of the next-generation low-altitude economy. Full article

To address the limitations of the original Preschool Education Optimization Algorithm (PEOA) in population diversity preservation and late-stage convergence accuracy, this paper proposes a Multi-strategy Enhanced Preschool Education Optimization Algorithm (MEPEOA). The proposed algorithm integrates an improved population initialization strategy, a multi-strategy collaborative search mechanism, adaptive regulation, and boundary control to achieve a more effective balance between global exploration and local exploitation. The performance of MEPEOA is comprehensively evaluated on IEEE CEC2017 and CEC2022 benchmark suites and compared with several state-of-the-art metaheuristic algorithms, including EWOA, MPSO, L_SHADE, BKA, ALA, BPBO, and the original PEOA. Experimental results demonstrate that MEPEOA achieves superior optimization accuracy and stability on the majority of benchmark functions. For example, on CEC2017 with 30 dimensions, MEPEOA reduces the average fitness value of multimodal function F9 by approximately 73.6% compared with PEOA and by more than 47% compared with EWOA. In terms of stability, the standard deviation of MEPEOA on function F6 is only 4.13 × 10⁻³, which is several orders of magnitude lower than those of EWOA, MPSO, and BKA, indicating highly consistent convergence behavior. Furthermore, MEPEOA exhibits clear advantages in convergence speed and robustness, achieving the best Friedman mean rank across all tested benchmark suites. In addition, MEPEOA is applied to a two-dimensional grid-based path planning problem, where it consistently generates shorter and more stable collision-free paths than competing algorithms. Overall, the proposed MEPEOA demonstrates strong robustness, fast convergence, and superior stability, making it an effective and extensible solution for complex numerical optimization and practical engineering problems. Full article

To address the insufficient exploration ability, susceptibility to local optima, and limited convergence accuracy of the standard Student Psychology-Based Optimization (SPBO) algorithm in three-dimensional UAV trajectory planning, we propose an enhanced variant, Enhanced SPBO (ESPBO). ESPBO augments SPBO with three complementary strategies: (i) Time-Adaptive Scheduling, which uses normalized time ($\tau =t/T$) to schedule global step-size shrinking, Gaussian fine-tuning, and Lévy flight intensity, enabling strong early exploration and fine late-stage exploitation; (ii) Mentor Pool Guidance, which selects a top-K mentor set and applies time-varying guidance weights to reduce misleading attraction and improve directional stability; and (iii) Directional Jump Exploration, which couples a differential vector with Lévy flights to strengthen basin-crossing while keeping the differential step bounded for robustness. Numerical experiments on CEC2017, CEC2020 and CEC2022 benchmark functions compare ESPBO with Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Improved multi-strategy adaptive Grey Wolf Optimization (IAGWO), Dung Beetle Optimization (DBO), Snake Optimization (SO), Rime Optimization (RIME), and the original SPBO. We evaluate best path length, mean trajectory length, standard deviation, and convergence curves and assess statistical stability via Wilcoxon rank-sum tests (p = 0.05) and the Friedman test. ESPBO significantly outperforms the comparison algorithms in path-planning accuracy and convergence stability, ranking first on both test suites. Applied to 3D UAV trajectory planning in mountainous terrain with no-fly zones, ESPBO achieves an optimal path length of 199.8874 m, an average path length of 205.8179 m, and a standard deviation of 5.3440, surpassing all baselines; notably, ESPBO’s average path length is even lower than the optimal path length of other algorithms. These results demonstrate that ESPBO provides an efficient and robust solution for UAV trajectory optimization in intricate environments and extends the application of swarm intelligence algorithms in autonomous navigation. Full article

To address the challenges in unstructured orchard environments, including high geometric similarity between fruit trees (with the measured average Euclidean distance difference between point cloud descriptors of adjacent trees being less than 0.5 m), significant dynamic interference (e.g., interference from pedestrians or moving equipment can occur every 5 min), and uneven terrain, this paper proposes an improved mapping algorithm named OSC-LIO (Orchard Scan Context Lidar Inertial Odometry via Smoothing and Mapping). The algorithm designs a dynamic point filtering strategy based on Euclidean clustering and spatiotemporal consistency within a 5-frame sliding window to reduce the interference of dynamic objects in point cloud registration. By integrating local semantic features such as fruit tree trunk diameter and canopy height difference, a two-tier verification mechanism combining “global and local information” is constructed to enhance the distinctiveness and robustness of loop closure detection. Motion compensation is achieved by fusing data from an Inertial Measurement Unit (IMU) and a wheel odometer to correct point cloud distortion. A three-level hierarchical indexing structure—”path partitioning, time window, KD-Tree (K-Dimension Tree)”—is built to reduce the time required for loop closure retrieval and improve the system’s real-time performance. Experimental results show that the improved OSC-LIO system reduces the Absolute Trajectory Error (ATE) by approximately 23.5% compared to the original LIO-SAM (Tightly coupled Lidar Inertial Odometry via Smoothing and Mapping) in a simulated orchard environment, while enabling stable and reliable path planning and autonomous navigation. This study provides a high-precision, lightweight technical solution for autonomous navigation in orchard scenarios. Full article

To address the current reliance on manual labor in traditional logistics sorting operations, which leads to low sorting efficiency and high operational costs, this study presents the design of an unmanned logistics vehicle based on the Robot Operating System (ROS). To overcome bounding-box loss issues commonly encountered by mainstream video-stream image segmentation algorithms under complex conditions, the novel SMITE video image segmentation algorithm is employed to accurately extract key regions of mail items while eliminating interference. Extracted logistics information is mapped to corresponding grid points within a map constructed using Simultaneous Localization and Mapping (SLAM). The system performs global path planning with the A* heuristic graph search algorithm to determine the optimal route, autonomously navigates to the target location, and completes the sorting task via a robotic arm, while local path planning is managed using the Dijkstra algorithm. Experimental results demonstrate that the SMITE video image segmentation algorithm maintains stable and accurate segmentation under complex conditions, including object appearance variations, illumination changes, and viewpoint shifts. The PaddleOCR text recognition algorithm achieves an average recognition accuracy exceeding 98.5%, significantly outperforming traditional methods. Through the analysis of existing technologies and the design of a novel parcel-grasping control system, the feasibility of the proposed system is validated in real-world environments. Full article

In view of the complex flight scenarios existing in UAV path planning, it is necessary to model the UAV flight trajectory. When constructing the model, cost factors such as the minimum flight path of the UAV, obstacle avoidance, flight altitude, and trajectory smoothness are fully taken into account. To reduce the overall flight cost, a novel secretary bird optimization algorithm (NSBOA) is proposed in this paper, which effectively addresses the limitations of traditional algorithms in handling UAV path planning tasks. First of all, the Singer chaotic map is adopted to initialize the population instead of the conventional random initialization method. This improvement increases population diversity, enables the initial population to be more evenly distributed in the search space, and further accelerates the algorithm’s convergence speed in the subsequent optimization process. Second, an adaptive adjustment mechanism is integrated with the Levy flight mechanism to optimize the core logic of the algorithm, with a specific focus on improving the exploitation stage. By introducing appropriate perturbations near the current optimal solution, the algorithm is guided to jump out of local optimal traps, thereby enhancing its global optimization capability and avoiding premature convergence caused by insufficient population diversity. By comparing and analyzing NSBOA with SBOA, WOA, PSO, POA, NGO, and HHO algorithms in 12 common evaluation functions and CEC 2017 test functions, and applying NSBOA to the UAV path optimization problem, the simulation results show the effectiveness and superiority of the proposed scheme. Full article