Search Results (198)

Grain-scale heterogeneity strongly influences hydraulic fracture initiation and trajectory in crystalline rocks, yet its contributions to non-planar growth and the interaction of multiple nearby cracks remain insufficiently quantified. To address this gap, we perform numerical experiments on a model containing two parallel pre-existing cracks using a hydro-mechanical phase-field framework, systematically quantifying how mineral distribution and axial compression govern non-planar hydraulic fracture growth and inter-fracture competition. The results demonstrate that mineral distribution is the primary driver of fracture complexity. Even within the same Voronoi tessellation, redistributing minerals alone yields markedly different trajectories, deflections, branching patterns, and final morphologies. Furthermore, non-planar growth follows a stepwise, energy-threshold-driven mechanism. When cracks penetrate strong grains or undergo large-angle deflections, propagation is impeded, and injection pressure builds up. Once a critical energy threshold is reached, accumulated energy is rapidly released along the path of minimum incremental energy, manifested as abrupt pressure drops and rapid crack advance. Additionally, the two nearby fractures exhibit strong mechanical competition. Despite negligible hydraulic interference in low-permeability granite, early growth of one fracture redistributes stresses and suppresses the driving force of the other, resulting in asymmetric development. Finally, axial compression primarily governs the overall propagation orientation and influences local failure modes but has a limited effect on peak pressure relative to mineral distribution. Full article

This article addresses the problem of finding the shortest route for Automated Guided Vehicles (AGVs) in a production environment with constrained battery state-of-charge (SoC) and time-dependent operating conditions. The route map is divided into a uniform grid containing stationary obstacles and two types of dynamic obstacles: human, for which AGV transportation is prohibited, and inanimate (moving objects), which impose a penalty function. A key contribution of the proposed methodology is the introduction of a battery residual charge matrix, which embeds cell-level energy feasibility directly into the grid-based environment representation by determining minimum admissible SoC constraints and accounting for transition-dependent energy costs. This matrix restricts the set of traversable cells under low-energy conditions, enabling energy-aware route feasibility evaluation during both initial planning and adaptive replanning. The proposed approach is based on the A* and D* Lite algorithms, providing shortest-path construction that explicitly integrates battery SoC into the spatio-temporal cost function. To avoid collisions in a multi-agent environment during routing, a simplified hybrid scheme with M* elements performs local coordination and adaptive trajectory replanning. The effectiveness of the proposed methodology was assessed using travel time, temporal complexity, and spatial complexity metrics. Simulation results on a $10\times 10$ grid showed that agents with sufficient battery completed routes of 8 and 11 cells with travel times of 7.2 to 10.7 conventional units. A critically low-energy agent was initially unable to move, but after adjusting the minimum SoC constraint, all agents completed their routes with travel times up to 11.4 conventional units, demonstrating the direct impact of energy constraints on system performance. Additional experiments with varying agent counts and SoC thresholds confirmed reliable balancing of route feasibility and energy constraints across configurations. Full article

This paper addresses global path planning for a wheeled mobile robot with two different kinematic structures, considering both shortest path and minimum energy consumption criteria. The main research question concerns how the robot’s kinematic structure and the selected planning algorithm influence the resulting path with respect to these criteria. Our review of the state of the art discusses selected path planning methods, including model-based approaches. To determine the energy optimal path, a simplified model of the PIAP GRANITE robot was developed. The robot can be configured as either differentially driven or skid-steered. In the differentially driven configuration, the robot has two driven wheels and two caster wheels, whereas in the skid-steered configuration all wheels are independently driven. The robot’s models are based on previous theoretical and experimental studies and include kinematics, dynamics, drive units, and wheel slip phenomena. For path planning, it was assumed that the robot can move straight or turn. A flat terrain representative of typical urban environments was modeled as a grid of square cells, each characterized by friction and rolling resistance coefficients. Path planning was performed using A*, Theta*, and RRT* algorithms. In order to quantitatively evaluate the results, quality indexes were defined, including path length, energy consumption, computation time, and the number of analyzed nodes. Simulation results are presented for selected terrain maps, both robot configurations, all algorithms, and both optimization criteria. The results show that the differentially driven configuration is consistently more energy-efficient. For the skid-steered robot, minimizing the number of turns is crucial due to high turning energy costs. The A* algorithm consistently finds optimal paths, whereas RRT* is faster but produces non-optimal and non-repeatable results. Theta* does not always achieve optimality due to limitations imposed by the line-of-sight function. Full article

To address the key limitations of existing laser SLAM (Simultaneous Localization and Mapping) navigation systems in orchards—insufficient safety margins, unsmooth trajectories, poor dynamic obstacle adaptability, and high energy consumption—this study proposes an A* (A-Star)-DWA (Dynamic Window Approach) collaborative optimization algorithm integrated into an orchard-specific laser SLAM framework. Three core enhancements were added to the global A* planner: (1) obstacle–vertex adjacency checks (maintaining ~1 m minimum safety distance, meeting 0.8–1.2 m orchard machinery requirements); (2) redundant node elimination (reducing unnecessary turns and energy use); (3) obstacle density metric integrated into the heuristic function (optimizing node expansion efficiency). For the local DWA planner, key parameters (azimuth weight, obstacle distance weight, prediction horizon, etc.) were calibrated to orchard scenarios and tracked robot kinematics, with a lightweight “deviate → avoid → rejoin global path” mechanism for real-time obstacle avoidance. A three-stage path smoothing process (Bresenham verification + cubic spline interpolation + curvature constraint optimization) further improved trajectory quality. The A*-DWA framework synergizes A*’s global optimality (overcoming DWA’s local minima) and DWA’s real-time obstacle avoidance (compensating for A*’s static limitation). Validations via Matlab/Gazebo/Rviz simulations and field tests in the “Xinli No. 7” pear orchard confirmed superior performance: 100% obstacle avoidance success rate (vs. 85.0–92.0% for comparative algorithms), 0.36–0.45 s response time (57.7–71.1% shorter), 1.05–1.15 m safety distance (far exceeding 0.60–0.82 m of existing methods); field tests show 10% lower energy consumption than traditional A*, 0.011 m mean lateral deviation (straight segments), and 65% reduced peak turning deviation (0.14 m). This work resolves multidimensional orchard navigation challenges, enhances agricultural robot efficiency, safety, and adaptability, and provides a practical basis for smart agriculture advancement. Full article

Autonomous mobile robots require efficient path planning algorithms for navigation in grid-based environments. While the A* algorithm guarantees optimally short paths using admissible heuristics, it exhibits path degeneracy: multiple geometrically distinct paths often share identical length. Classical A* arbitrarily selects among these equal-cost candidates, frequently producing trajectories with excessive directional changes. Each turn induces deceleration–acceleration cycles that degrade energy efficiency and accelerate mechanical wear. To address this, we propose Turn-Minimizing A* (TM-A*), a lexicographic optimization approach that maintains distance optimality while minimizing cumulative heading changes. Unlike weighted-cost methods that require parameter calibration, TM-A* applies a dual-objective framework: distance takes strict priority, with turn count serving as a tie-breaker among equal-length paths. A key contribution of this work is the explicit guarantee that the generated path has the minimum number of turns among all shortest paths. By formulating path planning as a lexicographic optimization problem, TM-A* strictly prioritizes path length optimality and deterministically selects, among all equal-length candidates, the one with the fewest directional changes. Unlike classical A*, which arbitrarily resolves path degeneracy, TM-A* provably eliminates this ambiguity. As a result, the method ensures globally shortest paths with minimal turning, directly improving trajectory smoothness and operational efficiency. We prove that TM-A* preserves the O(|E|log|V|) time complexity of classical A*. Validation across 30 independent Monte Carlo trials at resolutions from 200 × 200 to 1000 × 1000 demonstrates that TM-A* reduces turn count by 39–43% relative to baseline A* (p < 0.001). Although the inclusion of orientation expands the search space four-fold, the computation time increases by only a factor of approximately 3 (≈200%), indicating efficient scalability relative to problem complexity. With absolute latency remaining below 3300 ms for 1000 × 1000 grids, the approach is highly suitable for static global planning. Consequently, TM-A* provides a deterministic and scalable solution for generating smooth trajectories in industrial mobile robot applications. Full article

A deep understanding of hydrogen diffusion in metals under stress is crucial for revealing the mechanism of hydrogen embrittlement. While the effects of isotropic and uniaxial stress have been studied, the atomic-scale mechanism under a pure biaxial stress state remains unclear. This work employs molecular dynamics simulations to investigate hydrogen diffusion in α-iron under controlled biaxial stress. The results show that biaxial stress influences diffusion indirectly by altering the lattice geometry and thus the migration energy barrier. It is found that the diffusion path is governed by the direction of the minimum principal strain, while the diffusion rate is controlled by the maximum tensile principal strain, with which it exhibits an approximately exponential relationship. These insights clarify the distinct roles of different strain components, providing a refined framework for understanding hydrogen behavior under complex stress states and guiding the design of hydrogen-resistant materials. Full article

In the field of energy transformation, the application of batteries is widening. To address the challenge of health state estimation of energy storage batteries with multiple operating conditions, this study analyzes the aging cycle operation data of lithium-ion batteries and develops a scheme to extract a number of raw features and their corresponding health status labels. Multidimensional candidate feature sets that capture aging information under different conditions are constructed. Subsequently, a three-stage feature selection strategy, including Pearson and Spearman correlation analysis, hierarchical redundancy elimination, and minimum redundancy maximum relevance, was applied to screen the candidate feature set of each condition, resulting in customized feature sets with condition adaptability. By analyzing the occurrence frequency and mean absolute correlation coefficient of each feature within the custom feature set, a comprehensive feature set with multi-condition adaptability was screened and determined. On this basis, by integrating temporal sequence information and operating condition information, a dual-path fusion estimation model with attention mechanism and condition modulation was established. The validation results of the lithium-ion battery multi-condition cycling aging dataset demonstrate that the model achieves accurate health state estimation, with mean absolute error and root mean square error of 0.8281% and 0.9835%, respectively. Finally, comparisons with other methods were conducted in terms of feature selection strategies and model estimation performance. The results demonstrate that the proposed approach achieves superior estimation accuracy and enhanced interpretability. Full article

Wireless Sensor Networks (WSNs) are pivotal for data acquisition, yet reliability is severely constrained by routing voids induced by sparsity, uneven energy, and high dynamicity. To address these challenges, the Hybrid Acoustic-Optical Adaptive Void-handling Protocol (HAO-AVP) is proposed to satisfy the requirements for highly reliable communication in complex underwater environments. First, targeting uneven energy, a reinforcement learning mechanism utilizing Gini coefficient and entropy is adopted. By optimizing energy distribution, voids are proactively avoided. Second, to address routing interruptions caused by the high dynamicity of topology, a collaborative mechanism for active prediction and real-time identification is constructed. Specifically, this mechanism integrates a Markov chain energy prediction model with on-demand hop discovery technology. Through this integration, precise anticipation and rapid localization of potential void risks are achieved. Finally, to recover damaged links at the minimum cost, a four-level progressive recovery strategy, comprising intra-medium adjustment, cross-medium hopping, path backtracking, and Autonomous Underwater Vehicle (AUV)-assisted recovery, is designed. This strategy is capable of adaptively selecting recovery measures based on the severity of the void. Simulation results demonstrate that, compared with existing mainstream protocols, the void identification rate of the proposed protocol is improved by approximately 7.6%, 8.4%, 13.8%, 19.5%, and 25.3%, respectively, and the void recovery rate is increased by approximately 4.3%, 9.6%, 12.0%, 18.4%, and 24.2%, respectively. In particular, enhanced robustness and a prolonged network life cycle are exhibited in sparse and dynamic networks. Full article

This paper presents a low-cost, modular ankle–foot prosthesis that integrates an S-shaped compliant foot with a parallel spring–short-stroke actuator branch to balance energy return, impact attenuation, and rapid personalization. The design follows an FDM-oriented CAD/CAE workflow using PETG and interchangeable modules (foot, ankle unit, pylon adapter). Finite-element analyses of heel-strike, mid-stance, and toe-off load cases, supported by bench checks, show strain localization in intended flexural regions, a minimum safety factor of 15 for the housing, and peak-stress reduction after geometric refinements (increased transition radii and local ribs). The modular layout simplifies servicing and allows quick tuning of stiffness and damping without redesigning the load-bearing structure. The results indicate an engineeringly realistic path toward accessible prosthetics and provide a basis for subsequent upgrades toward semi-active control and sensor-assisted damping. Full article

Upper limb deficiencies can limit the range of tasks children can perform. Current prosthetics provide overall good performance to increase the activities that users can complete, but challenges remain. Body- or electrically powered prostheses struggle to restore the full range of motion needed for specific tasks. Currently, these systems do not allow for controlled hand closure or opening across all possible postures. A breathing-powered prototype named Airbender, which extracts energy from a breathing input by means of a Tesla turbine, provides the possibility of operation in any position. This paper introduces a novel design for a multi-finger actuated breathing-powered upper limb prosthetic concept and analyses its performance through a series of lab-based experiments. Results show that such a design could provide a fully controllable system. The final assembled design is capable of achieving full actuation under a flow rate of 340 Ls/min. The results obtained demonstrate that a functional multi-finger actuated breathing-powered upper limb prosthesis could be feasible and opens a path for future research in the field, with the ultimate goal of reducing the minimum flow rate required and actuation time to further improve its functionality. Full article

This paper introduces a novel control archetype designed to mitigate high-altitude electromagnetic pulse (HEMP) $E_3$ disturbances on the power grid, as well as information on performance and specifications of different control laws for the controller archetype. This method of protection has been overlooked in the literature until now. A controlled voltage supply is placed on the load-side of a transformer, diverting unwanted power from the transformer core to prevent saturation. The controlled voltage source is modeled using four control laws: an integral controller (capacitor), Linear Quadratic Regulator (LQR), an energy storage minimized feedforward control law, and a Hamiltonian feedback law. Results show that the Hamiltonian feedback law and the energy storage minimization feedforward control law both flat-line magnetic flux with similar actuator requirements. The LQR approach requires less energy storage than the other two laws, depending on control tuning, as it allows greater exogenous current flow through the neutral path to ground. This leads to further optimization opportunities based on acceptable exogenous current levels. A sweep of different LQR gains revealed a reduction of approximately $32\%$ in minimum control effort, $47\%$ in minimum power to maintain saturation bounds, $20\%$ in energy storage requirements, and $59\%$ in required controller bandwidth. Voltage and bandwidth requirements of the load-side controller are comparable to neutral blocking requirements with energy and power requirements being higher for the load-side controller. This, however, comes with the benefit of being able to use pre-existing assets—neutral blocking devices have not been deployed. Additionally, the load-side blocking capacitor degrades transformer performance compared to the unmitigated system. Full article

The reuse of abandoned mines is not a pure ecological project but a complex social public project. While it is unsustainable to reuse abandoned mines without ecological restoration, such restoration without comprehensive resource utilization will cause a serious waste of resources. Therefore, to reduce the contradiction between ecological restoration and resource utilization in the process of reusing abandoned mines, there is an urgent need to research the classification, grading development, and utilization evaluation index system of abandoned mine resources. Based on the concept of “energy, resource and functionalization” three-dimensional coordinated development and utilization, this paper analyzes the value connotation of abandoned mine reuse and constructs an evaluation index system for the reuse value of abandoned mine resources, including resource conditions, ecological conditions, and development conditions. Secondly, according to the priority needs of abandoned mine reuse, the minimum factor method is used to design a development sequence that can take into account the reuse of abandoned mines and the coordinated development of the ecological environment and the region. On this basis, the value of abandoned mine reuse is divided into four grades and three development stages. Taking the Jingxi Mining Area as an example, corresponding development and utilization suggestions are proposed, and the guiding value of the evaluation index system in assessing resource potential and optimizing of development paths is verified. The research results can provide scientific decision support for planning the development and utilization of abandoned mine resources. They also have practical significance for constructing green development technology standards and promoting ecological restoration and industrial transformation and upgrading in mining areas. Full article

Aerial inspection missions over long distances are guided by planning paths with minimum energy consumption, which generally leads to routes with shorter distances. However, these conditions cannot always be met due to the environment’s geometric constraints, especially in the most unexpected (unsafe, dangerous, and risky) scenarios. The aim of this work is to improve the efficiency, stability, and security of unmanned aerial vehicles (UAVs) during inspection missions in unstructured and unpredictable environments. Traditional path-planning techniques that prioritize minimum energy consumption often lead to routes with shorter distances, but these can be hindered by environmental geometric constraints and risky scenarios. To address this challenge, a new path-planning algorithm is proposed that ensures safe passage through challenging moving locations while respecting the physical limitations of the robot and the shape of narrow passages. The algorithm is combined with a controller design for obstacle avoidance. Experimental results with different motion restrictions are presented, demonstrating the effectiveness of the proposed approach. Full article

Last-mile logistics is a critical operational and environmental challenge in urban areas. This paper introduces an intelligent path planning system using the Resistive Grid Path Planning Methodology (RGPPM) to optimize distribution based on energy and environmental metrics. The foundational innovation is the integration of electrical-circuit analogies, modeling the distribution network as a resistive grid where optimal routes emerge naturally as current flows, offering a paradigm shift from conventional algorithms. Using a multi-connected grid with georeferenced resistances, RGPPM estimates minimum and maximum paths for various starting points and multi-agent scenarios. We introduce five key performance indicators (KPIs)—Percentage of Distance Savings (PDS), Coefficient of Savings (CS), Coefficient of Global Savings (CGS), Percentage of Load Imbalance (PLI), and Percentage of Deviation with Multi-Agent (PDM)—to evaluate system performance. Simulations for textbook delivery to 129 schools in the Veracruz–Boca del Río area show that RGPPM significantly reduces travel distances. This leads to substantial savings in energy consumption, CO₂ emissions, and operating costs, particularly with electric vehicles. Finally, the results validate RGPPM as a flexible and scalable strategy for sustainable urban logistics. Full article

The challenge of optimally controlling energy storage systems under uncertainty conditions, whether due to uncertain storage device dynamics or load signal variability, is well established. Recent research works tackle this problem using two primary approaches: optimal control methods, such as stochastic dynamic programming, and data-driven techniques. This work’s objective is to quantify the inherent trade-offs between these methodologies and identify their respective strengths and weaknesses across different scenarios. We evaluate the degradation of performance, measured by increased operational costs, when a reinforcement learning policy is adopted instead of an optimal control policy, such as dynamic programming, Pontryagin’s minimum principle, or the Shortest-Path method. Our study examines three increasingly intricate use cases: ideal storage units, storage units with losses, and lossy storage units integrated with transmission line losses. For each scenario, we compare the performance of a representative optimal control technique against a reinforcement learning approach, seeking to establish broader comparative insights. Full article