Search Results (187)

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

With the rapid development of mobile robotics, wheeled bipedal robots, which combine the terrain adaptability of legged robots with the high mobility of wheeled systems, have attracted increasing research attention. To address the balance control problem during both standing and locomotion while reducing the influence of noise on control performance, this paper proposes a balance control framework based on a Linear Quadratic Regulator integrated with an Extended Kalman Filter (KLQR). Specifically, a baseline LQR controller is designed using the robot’s dynamic model, where the control input is generated in the form of wheel-hub motor torques. To mitigate measurement noise and suppress oscillatory behavior, an Extended Kalman Filter is applied to smooth the LQR torque output, which is then used as the final control command. Filtering experiments demonstrate that, compared with median filtering and other baseline methods, the proposed EKF-based approach significantly reduces high-frequency torque fluctuations. In particular, the peak-to-peak torque variation is reduced by more than 60%, and large-amplitude torque spikes observed in the baseline LQR controller are effectively eliminated, resulting in continuous and smooth torque output. Static balance experiments show that the proposed KLQR algorithm reduces the pitch-angle oscillation amplitude from approximately ±0.03 rad to ±0.01 rad, corresponding to an oscillation reduction of about threefold. The estimated RMS value of the pitch angle is reduced from approximately 0.010 rad to 0.003 rad, indicating improved convergence and steady-state stability. Furthermore, experiments involving constant-speed straight-line locomotion and turning indicate that the KLQR algorithm maintains stable motion with velocity fluctuations limited to within ±0.05 m/s. The lateral displacement deviation during locomotion remains below 0.02 m, and no abrupt acceleration or deceleration is observed throughout the experiments. Overall, the results demonstrate that applying Extended Kalman filtering to smooth the control torque effectively improves the smoothness and stability of LQR-based balance control for wheeled bipedal robots. Full article

This paper presents a finite element methodology for generating continuous digital elevation models (DEMs) from discrete terrain data using the Poisson equation under steady-state conditions. Unlike conventional DEM interpolation techniques, the proposed methodology formulates terrain reconstruction as a constrained harmonic problem, solved directly on scattered point sets using standard finite element procedures, without requiring structured grids or intermediate interpolation stages. The approach interprets the elevation field as a harmonic scalar function whose smoothness is enforced by the variational formulation of the Poisson problem. The governing equation is solved using standard finite element procedures with Dirichlet boundary conditions applied at the measurement points, ensuring that the reconstructed surface passes exactly through the known elevations. The isotropic conductivity coefficient is set to unity and the source term to zero, which simplifies the formulation and yields a harmonic interpolation independent of any physical parameters. The resulting surfaces exhibit continuous slopes and reduced sensitivity to irregular data distributions. Numerical tests comprising two analytical examples and a real terrain case show that, compared with thin-plate FEM and RBF–NURBS reconstructions, the proposed Poisson-based approach yields smoother and more stable surfaces, with global errors of the same order of magnitude and reduced computational cost. Full article

Forestry environments—such as logging sites, transport trails, and resource monitoring areas—are characterized by rugged terrain and irregularly distributed obstacles, which pose substantial challenges for AGV route planning. This poses challenges for route planning in automated guided vehicles (AGVs) and forestry machinery. To address these challenges, this study proposes a hybrid path optimization method that integrates an improved A* algorithm with the Dynamic Window Approach (DWA). At the global planning level, the improved A* incorporates a dynamically weighted heuristic function, a steering-penalty term, and Floyd-based path smoothing to enhance path feasibility and continuity. In terms of local planning, the improved DWA algorithm employs adaptive weight adjustment, risk-perception factors, a sub-goal guidance mechanism, and a non-uniform and adaptive sampling strategy, thereby strengthening obstacle avoidance in dynamic environments. Simulation experiments on two-dimensional grid maps demonstrate that this method reduces path lengths by an average of 6.82%, 8.13%, and 21.78% for 20 × 20, 30 × 30, and 100 × 100 maps, respectively; planning time was reduced by an average of 21.02%, 16.65%, and 9.33%; total steering angle was reduced by an average of 100°, 487.5°, and 587.5°. These results indicate that the proposed hybrid algorithm offers practical technical guidance for intelligent forestry operations in complex natural environments, including timber harvesting, biomass transportation, and precision stand management. Full article

To address the challenges of low automatic leveling efficiency and insufficient control precision for small tracked operation chassis navigating uneven terrain in hilly and mountainous areas, this study proposes a leveling control system that integrates a dual-layer threshold strategy with a BP neural network algorithm. The system is developed based on a four-point lifting leveling mechanism. Building upon this foundation, the conventional single-threshold angle error compensation control strategy was optimized to meet the specific leveling demands of chassis operating in such complex environments. A co-simulation platform was established using Matlab/Simulink-AMEsim for subsequent simulation and comparative analysis. Simulation results demonstrate that the proposed method achieves a 15.6% improvement in leveling response speed and a 21.3% enhancement in leveling accuracy compared to the classical single-threshold PID control algorithm. Static test results reveal a smooth leveling process devoid of significant overshoot or hysteresis, with the leveling error consistently maintained within 0.5°. Field tests further indicate that at a travel speed of 3 km/h under a 50 kg load, the platform stabilization time is reduced by an average of 1.3 s, while the leveling angle error remains within 0.5°. The proposed system not only improves leveling response speed and precision but also effectively enhances the overall leveling efficiency of the tracked chassis system. Full article

Whether an unmanned aerial vehicle (UAV) can complete its mission successfully is determined by trajectory planning. Reasonable and efficient UAV trajectory planning in 3D environments is a complex global optimization problem, in which numerous constraints need to be considered carefully, including mountainous terrain, obstacles, no-fly zones, safety altitude, smoothness, flight distance, and so on. Generally speaking, symmetry characteristics from the starting point to the endpoint can be concluded from the potential spatial multiple trajectories. Aiming at the deficiencies of the Sparrow Search Algorithm (SSA) in 3D symmetric trajectory planning such as population diversity and local optimization, the sine–cosine function and the Lévy flight strategy are combined, and the Improved Sparrow Search Algorithm (ISSA) is proposed, which can find a better solution in a shorter time by dynamically adjusting the search step size and increasing the occasional large step jumps so as to increase the symmetry balance of the global search and the local development. In order to verify the effectiveness of the improved algorithm, ISSA is simulated and compared with the Sparrow Search Algorithm (SSA), Particle Swarm Algorithm (PSO), Gray Wolf Algorithm (GWO) and Whale Optimization Algorithm (WOA) in the same environment. The results show that the ISSA algorithm outperforms the comparison algorithms in key indexes such as convergence speed, path cost, obstacle avoidance safety, and path smoothness, and can meet the requirement of obtaining a higher-quality flight path in a shorter number of iterations. Full article

Mine rescue robots operate in extremely adverse subterranean environments, where the acquired video data are frequently affected by severe jitter and motion distortion. Such instability leads to the loss of critical visual information, thereby reducing the reliability of rescue decision-making. To address this issue, a dual-channel visual stabilization framework based on the SuperPoint network is proposed, extending the traditional ORB descriptor framework. Here, dual-channel refers to two configurable and mutually exclusive feature extraction paths—an ORB-based path and a SuperPoint-based path—that can be flexibly switched according to scene conditions and computational requirements, rather than operating simultaneously on the same frame. The subsequent stabilization pipeline remains unified and consistent across both modes. The method employs an optimized detector head that integrates deep feature extraction, non-maximum suppression, and boundary filtering to enable precise estimation of inter-frame motion. When combined with smoothing filters, the approach effectively attenuates vibrations induced by irregular terrain and dynamic operational conditions. Experimental evaluations conducted across diverse scenarios demonstrate that the proposed algorithm achieves an average improvement of 27.91% in Peak Signal-to-Noise Ratio (PSNR), a 55.04% reduction in Mean Squared Error (MSE), and more than a twofold increase in the Structural Similarity Index (SSIM) relative to pre-stabilized sequences. Moreover, runtime analysis indicates that the algorithm can operate in near-real-time, supporting its practical deployment on embedded mine rescue robot platforms.These results verify the algorithm’s robustness and applicability in environments requiring high visual stability and image fidelity, providing a reliable foundation for enhanced visual perception and autonomous decision-making in complex disaster scenarios. Full article

In human daily-life scenarios, humanoid robots need not only to stand up smoothly but also to autonomously sit down for rest, energy management, and interaction. This capability is crucial for enhancing their autonomy and practicality. However, both sitting and standing involve complex dynamics constraints, diverse initial postures, and unstructured terrains, which make traditional hand-crafted controllers insufficient for multi-scenario demands. Reinforcement Learning (RL), with its generalization ability across high-dimensional state spaces and complex tasks, offers a promising solution for automatically generating motion control policies. Nevertheless, policies trained directly with RL often produce abrupt motions, making it difficult to balance smoothness and stability. To address these challenges, we propose a two-stage reinforcement learning framework: In the first stage, we focus on exploration and train initial policies for both sitting and standing, with relatively weak constraints on smoothness and joint safety, and without introducing noise. In the second stage, we refine the policies by tracking the motion trajectories obtained in the first stage, aiming for smoother transitions. We model the tracking problem as a bi-level optimization, where the tracking precision is dynamically adjusted based on the current tracking error, forming an adaptive curriculum mechanism. We apply this framework to a 1.7 m adult-scale humanoid robot, achieving stable execution in two representative real-world scenarios: sitting down onto a chair, stand up from a chair. Our approach provides a new perspective for the practical deployment of humanoid robots in real-world scenarios. Full article

This study focuses on the development and optimization of a Clutchless Automated-Manual Transmission (CLAMT) system for tractors, aiming to enhance performance and efficiency across diverse operating conditions. It explores the use of a dog clutch mechanism as a simpler, robust alternative to traditional synchronizers. The main objective is to replace complex transmission setups—often requiring up to 32 gear ratios—with a system that operates efficiently using only two gears, without sacrificing versatility. Smooth gear engagement, even under varying loads and terrains, is a key challenge addressed. To ensure this, a Vehicle Management Unit (VMU) manages gear shifts and actively synchronizes speeds. The system leverages steady torque delivery through control algorithms and modern hybrid/electric powertrain capabilities. Two algorithmic approaches are implemented, and their performance is evaluated through empirical testing. Results show improvements in system simplicity, transmission reliability, and overall operational efficiency. The proposed approach offers valuable insights for future agricultural drivetrains, highlighting the potential of dog clutch-based architectures in reducing mechanical complexity while maintaining functional performance. Full article

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments. Full article

Fire refugia play an important role in post-fire ecosystem recovery because they preserve areas that represent a persistent legacy in the landscape and serve as propagule sources for forest regeneration. Our objective was to identify the pre-fire topographic and land cover conditions that determine the presence and quality of megafire refugia in the mountains of central Argentina. In 208 1-ha field-based plots, we assessed pre-fire topographic and land cover variables along with post-fire vegetation responses two years after the megafires. Based on these assessments, we developed a fire refugia quality index ranging from 0 (no refugia) to 5 (high-quality refugia). Using ordinal logistic regression and a model selection approach, we found that high-quality fire refugia were associated with the more humid east mountain flank and east- and north-facing slopes, as well as with smooth terrain, high topographic positions, greater rock cover, steep slopes, and higher tree-to-grass cover proportions. Our findings highlight the importance of topographic and land cover variables in shaping fire refugia and provide insights into post-fire management and the conservation of biodiversity in mountain ecosystems. Full article

Path planning for unmanned aerial vehicles (UAVs) in mountainous environments requires satisfying terrain clearance and obstacle avoidance constraints while optimizing path length, flight time, and energy consumption. To address these challenges, this paper proposes EC-MOPSO (Epsilon-dominance and Crowding-distance-based Multi-Objective Particle Swarm Optimization). Inspired by the principle of symmetry, the algorithm integrates an adaptive parameter adjustment mechanism with a $\epsilon -$ dominance–crowding archiving strategy to balance global exploration and local exploitation through spatially symmetric archive management. A safety-repairable B-spline trajectory model ensures smooth and feasible flight paths under complex terrain conditions. Simulation results show that EC-MOPSO reduces path length by 10–40%, improves normalized hypervolume by over 25%, and decreases performance variance by 20–25%, confirming faster convergence and higher robustness compared with representative multi-objective optimization approaches. Ablation studies further verify that both the adaptive parameter mechanism and the $\epsilon -$ dominance–crowding strategy significantly enhance convergence stability and overall optimization performance. Overall, EC-MOPSO provides an adaptive and reliable optimization framework for generating efficient, safe, and energy-aware UAV trajectories in real-world mountainous rescue missions. Full article

Legged robots have great potential in complex environments, but achieving robust and natural locomotion remains difficult due to challenges in generating smooth gaits and resisting disturbances. This article presents a novel reinforcement learning framework that integrates a skeleton-aware graph neural network (GNN), a single-stage teacher–student architecture, a system-response model, and a Wasserstein Adversarial Motion Priors (wAMP) module. The skeleton-aware GNN enriches observations by encoding key node information and link properties, providing structured body information and better spatial awareness on irregular terrains. Unlike conventional two-stage approaches, this method jointly trains teacher and student policies to accelerate learning and improve sim-to-real transfer using hybrid advantage estimation (HAE). The system-response model further enhances robustness by predicting future observations from historical states via contrastive learning, enabling the policy to anticipate terrain variations and external disturbances. Finally, wAMP provides a more stable adversarial imitation method for fitting expert datasets of both flat ground and stair locomotion. Experiments on quadruped robots demonstrate that the proposed approach achieves more natural gaits and stronger robustness than existing baselines. Full article

In this study, a nonlinear dynamic model is developed to examine the stability and vibration behavior of a self-balancing electric Segway operating over irregular stochastic terrains. The Segway is treated as a three-degrees-of-freedom cart–inverted pendulum system, incorporating elastic and damping effects at the wheel–ground interface. Road irregularities are generated in accordance with international standard using high-order filtered noise, allowing for representation of surface classes from smooth to highly degraded. The governing equations, formulated via Lagrange’s method, are transformed into a Lorenz-like state-space form for nonlinear analysis. Numerical simulations employ the fourth-order Runge–Kutta scheme to compute translational and angular responses under varying speeds and terrain conditions. Frequency-domain analysis using Fast Fourier Transform (FFT) identifies resonant excitation bands linked to road spectral content, while Kernel Density Estimation (KDE) maps the probability distribution of displacement states to distinguish stable from variable regimes. The Lyapunov stability assessment and bifurcation analysis reveal critical velocity thresholds and parameter regions marking transitions from stable operation to chaotic motion. The study quantifies the influence of the gravity–damping ratio, mass–damping coupling, control torque ratio, and vertical excitation on dynamic stability. The results provide a methodology for designing stability control systems that ensure safe and comfortable Segway operation across diverse terrains. Full article

Autonomous planetary rovers require robust path planning over rough 3D terrains, where traditional metrics such as path length, number of nodes, and planning time do not adequately capture path quality. Rapidly Exploring Random Trees (RRT) and its asymptotically optimal variant, RRT*, are widely used sampling-based algorithms for non-holonomic mobile robots but are limited when traversing uneven 3D terrain. This study proposes 3D-RRT*, a simplified, terrain-aware extension of Traversability-Based RRT*, designed to maintain high path quality while reducing planning time. The performance of 3D-RRT* is evaluated using metrics that are both practical and meaningful in the context of planetary rover path planning: path smoothness, path flatness, path length, and planning time. Exploration of a simulated Martian surface demonstrates that 3D-RRT* significantly improves path quality compared to standard RRT and RRT*, achieving smoother, safer, and more efficient routes for planetary rover missions. Full article