Search Results (30)

This article presents an approach to extend the capabilities of vat photopolymerization (VPP) 3D printing using a robotic arm, with a focus on robust path planning. The robotic cell consists of a Mecademic Meca500 six-axis robot mounted on a Zaber X-LRQ300AP linear guide. The kinematic chain is inverted to reflect the logic of VPP: the world reference frame is fixed to the robot’s tool (the build plate), while the tool frame is attached to the polymerization zone. A virtual degree of freedom for screen image rotation is introduced, bringing the system to eight degrees of freedom. Inverse kinematics are solved under constraints (pose tolerance, joint limits, collision avoidance, and continuity) and evaluated using multi-criteria metrics: manipulability, normalized joint-limit margin, and positional/angular sensitivity. The algorithm follows a deterministic coarse-to-fine search procedure: discrete sweeping of global part orientations, initial sampling with Halton sequences, abd feasibility filtering on a sparsified trajectory, followed by refinement and multi-criteria ranking. The pipeline successfully discarded infeasible orientations and identified feasible printing trajectories for six of the seven benchmark parts, while the remaining case highlights a limitation that may be addressed in future improvements. Full article

Sampling-based planners such as RRT* are attractive for robot navigation in complex spaces, but they often suffer from high randomness, low efficiency, slow convergence, and suboptimal path quality in cluttered environments. To address these limitations, this paper proposes PFMS-RRT*, a progress-aware fused-sampling RRT* with a multi-level strategy extension. The method builds on a bidirectional RRT* framework and introduces three main components: (i) a progress-aware fused sampling scheme that adapts an oriented elliptical sampling region based on inter-tree progress and stagnation, mixes locally guided elliptical samples with globally explorative Halton-sequence samples, and dynamically balances exploration and exploitation; (ii) a three-level goal-guided extension mechanism that escalates from direct steering to local probing and then multi-direction detours to maintain forward progress when obstacles block expansion; and (iii) a smooth tangential artificial potential field (APF) extension used as a fallback, with a failure-driven probabilistic switching rule that increases APF usage after repeated extension failures. Simulations in four representative 2D environments (sparse, corridor-like dense, random dense, and narrow passage) show that PFMS-RRT* consistently yields shorter paths, lower and more stable runtime, and fewer nodes than several RRT* variants while maintaining competitive or improved obstacle clearance. Full article

In complex ground environments, conventional RRT* often suffers from low planning efficiency and poor path quality for robot path planning. This paper proposes BGSE-RRT* (Bi-tree Cooperative, Goal-guided, low-discrepancy Sampling, multi-sector Expansion). First, BGSE-RRT* constructs a nonlinear switching probability via bi-tree cooperative adaptive switching, together with KD-Tree nearest-neighbor acceleration and multi-condition triggering, to adaptively balance global exploration and local convergence. Meanwhile, a goal-guided expansion with dynamic target binding and adaptive step size, under a multi-constraint feasibility check, accelerates the convergence of the two trees. When the goal-guided expansion becomes blocked, BGSE-RRT* generates candidate points in local multi-sector regions using a 2D Halton low-discrepancy sequence and selects the best candidate for expansion; if the multi-sector expansion still fails, a sampling-point-guided expansion is activated to continue advancing and search for a feasible path. Second, B-spline smoothing is applied to improve trajectory continuity. Finally, in five simulation environments and ROS/real-robot joint validation, compared with GB-RRT*, BI-RRT*, BI-APF-RRT*, and BAI-RRT*, BGSE-RRT* reduces planning time by up to 84.71%, shortens path length by 2.94–6.88%, and improves safety distance by 20.68–48.33%. In ROS/real-robot validation, the trajectory-tracking success rate reaches 100%. Full article

Achieving safe and stable navigation for autonomous ground vehicles (AGVs) in complex environments remains a key challenge in intelligent robotics. Conventional Model Predictive Path Integral (MPPI) control relies on pseudo-random Gaussian sampling, which often results in non-uniform sample distributions and jitter-prone control sequences, thereby limiting both convergence efficiency and control stability. This paper proposes a trajectory optimization method: Halton-MPPI, which improves MPPI by employing low-discrepancy sampling and modeling temporally correlated perturbations. Specifically, it utilizes the Halton sequence as the sampling basis for control disturbances to enhance spatial coverage, while the Ornstein–Uhlenbeck (OU) process is introduced to impose temporal correlation on control perturbations. This time-consistent noise propagation allows perturbation effects to accumulate over time, thereby expanding trajectory coverage. Large-scale simulations on the BARN dataset demonstrate that the method significantly enhances both trajectory smoothness (MSCX) and control smoothness (MSCU) while maintaining high success rates. Moreover, field tests in outdoor environments validate the effectiveness and robustness of Halton-MPPI, underscoring its practical value for autonomous navigation in complex environments. Full article

The study focuses on the design of an intelligent information-control system (ICS) for metallurgical production, aimed at robust forecasting of technological parameters and automatic self-adaptation under noise, anomalies, and data drift. The proposed architecture integrates a hybrid LSTM–DNN model with low-discrepancy hypercube sampling using Sobol and Halton sequences to ensure uniform coverage of operating conditions and the hyperparameter space. The processing pipeline includes preprocessing and temporal synchronization of measurements, a parameter identification module, anomaly detection and correction using an ε-threshold scheme, and a decision-making and control loop. In simulation scenarios modeling the dynamics of temperature, pressure, level, and flow (1 min sampling interval, injected anomalies, and measurement noise), the hybrid model outperformed GRU and CNN architectures: a determination coefficient of R² > 0.92 was achieved for key indicators, MAE and RMSE improved by 7–15%, and the proportion of unreliable measurements after correction decreased to <2% (compared with 8–12% without correction). The experiments also demonstrated accelerated adaptation during regime changes. The scientific novelty lies in combining recurrent memory and deep nonlinear approximation with deterministic experimental design in the hypercube of states and hyperparameters, enabling reproducible self-adaptation of the ICS and increased noise robustness without upgrading the measurement hardware. Modern metallurgical information-control systems operate under non-stationary regimes and limited measurement reliability, which reduces the robustness of conventional forecasting and decision-support approaches. To address this issue, a hybrid LSTM–DNN architecture combined with low-discrepancy hypercube probing and anomaly-aware data correction is proposed. The proposed approach is distinguished by the integration of hybrid neural forecasting, deterministic hypercube-based adaptation, and anomaly-aware data correction within a unified information-control loop for non-stationary industrial processes. Full article

When the Colebrook equation is used in its original implicit form, the unknown pipe flow friction factor can only be obtained through time-consuming and computationally demanding iterative calculations. The empirical Colebrook equation relates the unknown Darcy friction factor to a known Reynolds number and a known relative roughness of a pipe’s inner surface. It is widely used in engineering. To simplify computations, a variety of explicit approximations have been developed, the accuracy of which must be carefully evaluated. For this purpose, this Data Descriptor gives a sufficient number of pipe flow friction factor values that are computed using a highly accurate iterative algorithm to solve the implicit Colebrook equation. These values serve as reference data, spanning the range relevant to engineering applications, and provide benchmarks for evaluating the accuracy of the approximations. The sampling points within the datasets are distributed in a way that minimizes gaps in the data. In this study, a Python Version v1 script was used to generate quasi-random samples, including Halton, Hammersley, Sobol, and deterministic lattice-based Korobov samples, which produce smaller gaps than purely random samples generated for comparison purposes. Using these sequences, a total of 2²⁰ = 1,048,576 data points were generated, and the corresponding datasets are provided in in the zenodo repositoryWhen a smaller subset of points is needed, the required number of initial points from these sequences can be used directly. Full article

To address real-world engineering design optimization problems, this study proposes a reinforcement learning enhanced multi-objective social network search algorithm (QMOSNS), which represents a novel approach for solving multi-objective optimization problems. QMOSNS utilizes Halton sequences for population initialization to enhance the diversity of the initial population. A multi-objective archive mechanism is implemented to store Pareto-optimal solutions and select parental individuals through a reassigned fitness evaluation strategy. Furthermore, Q-learning is incorporated to adaptively select mutation operators, thereby dynamically balancing the algorithm’s exploration and exploitation capabilities. QMOSNS was rigorously evaluated through 50 prominent case studies, including 22 unconstrained multi-objective benchmark problems, 18 constrained multi-objective benchmark problems, and 10 multi-objective engineering design problems, to comprehensively validate its computational capabilities and effectiveness. Moreover, statistical results obtained using consistent performance metrics were compared with those of other highly regarded algorithms to ensure a fair and objective performance assessment. The comparative results show that QMOSNS is robust and superior in handling a wide variety of multi-objective problems. This study underscores the efficacy of integrating reinforcement learning with social intelligence for tackling complex multi-objective optimization in engineering and computational domains. Full article

The reliable and safe operation of traction elevators depends on traction capacity, which is degraded by traction sheave groove wear. The resulting slippage reduces transmission efficiency and may cause a catastrophic failure due to the sudden loss of friction. After analyzing slippage mechanisms, we propose a prediction model that combines the Improved Pelican Optimization Algorithm (IPOA) with an Extreme Learning Machine (ELM). A mechanism analysis identifies key inputs—the wear amount, payload, and wire rope tension—providing a basis for model construction. The approach uses Halton sequence initialization, adaptive nonlinear weighting, and Gaussian perturbation, which improve the handling of nonlinearities. IPOA is then employed to optimize the ELM parameters, yielding the IPOA-ELM model. Experiments across multiple wear conditions show that IPOA-ELM predicts slippage more accurately than a traditional ELM. The study clarifies how traction sheave groove wear induces rope slippage and demonstrates the effectiveness of the proposed model under varying wear and load conditions, offering a practical reference for failure mechanism analysis and preventive strategies in elevator traction systems. Full article

Currently, fastener installation within the narrow, confined space of a wing box must be performed manually, as existing robotic systems are unable to adequately meet the internal assembly requirements. To address this problem, a new robot with one prismatic and five revolute joints (1P5R) has been developed for entering and operating inside the wing box. Firstly, the mechanical structure and control system of the robot were designed and implemented. Then, an improved Probabilistic Roadmap (PRM) method was developed to enable rapid and smooth path planning, mainly depending on optimization of sampling strategy based on Halton sequence, an elliptical-region-based redundant point optimization strategy using control points, improving roadmap construction, and path smoothing based on B-spline curves. Finally, obstacle–avoidance path planning based on the improved PRM was simulated using the MoveIt platform, corresponding robotic motion experiments were conducted, and the improved PRM was validated. Full article

To address the issue of uncertainty in renewable energy and its impact on the safe and stable operation of power systems, this paper proposes a transient stability constrained optimal power flow (TSCOPF) calculation method that takes into account the uncertainty of wind power and load. First, a non-parametric kernel density estimation method is used to construct the probability density function of wind power, while the load uncertainty model is based on a normal distribution. Second, a TSCOPF model incorporating the critical clearing time (CCT) evaluation metric is constructed, and corresponding probabilistic constraints are established using opportunity constraint theory, thereby establishing a TSCOPF model that accounts for wind power and load uncertainties; then, a semi-invariant probabilistic flow calculation method based on de-randomized Halton sequences is used to convert opportunity constraints into deterministic constraints, and the improved sooty tern optimization algorithm (ISTOA) is employed for solution. Finally, the superiority and effectiveness of the proposed method are validated through simulation analysis of case studies. Full article

To address the shortcomings of poor convergence and the ease of falling into local optima when using the traditional gold rush optimization (GRO) algorithm to solve the complex scheduling problem of a combined cooling, heating, and power (CCHP) microgrid system, an optimal scheduling model for a microgrid based on the improved gold rush optimization (IGRO) algorithm is proposed. First, the Halton sequence is introduced to initialize the population, ensuring a uniform and diverse distribution of prospectors, which enhances the algorithm’s global exploration capability. Then, a dynamically adaptive weighting factor is applied during the gold mining phase, enabling the algorithm to adjust its strategy across different search stages by balancing global exploration and local exploitation, thereby improving the convergence efficiency of the algorithm. In addition, a weighted global optimal solution update strategy is employed during the cooperation phase, enhancing the algorithm’s global search capability while reducing the risk of falling into local optima by adjusting the balance of influence between the global best solution and local agents. Finally, a t-distribution mutation strategy is introduced to improve the algorithm’s local search capability and convergence speed. The IGRO algorithm is then applied to solve the microgrid scheduling problem, with the objective function incorporating power purchase and sale cost, fuel cost, maintenance cost, and environmental cost. The example results show that, compared with the GRO algorithm, the IGRO algorithm reduces the average total operating cost of the microgrid by 3.29%, and it achieves varying degrees of cost reduction compared to four other algorithms, thereby enhancing the system’s economic benefits. Full article

This paper introduces a multi-indicator heuristic evaluation-based rapidly exploring random tree (MIHE-RRT) algorithm to address the key challenges of robot path planning in complex environments. The core innovation lies in a novel dual optimization framework that combines Hammersley sequence sampling with a comprehensive multi-indicator heuristic evaluation mechanism. The Hammersley sequence ensures uniform coverage of the configuration space, while the multi-indicator heuristic evaluation mechanism intelligently guides tree expansion through a three-dimensional evaluation system incorporating diversity, distance, and angle values. After generating the initial path, a pruning algorithm removes redundant points to produce an efficient and practical final path. Extensive experimental validation in four different environmental scenarios (semi-enclosed, maze, chaotic, and crowded) demonstrates that MIHE-RRT outperforms RRT (rapidly exploring random tree), IBi-RRT (improved bidirectional rapidly exploring random tree), and HB-RRT (halton biased rapidly exploring random tree) algorithms. Results show significant improvements in planning efficiency (54–88% reduction in execution time), path quality (15–24% shorter paths), and computational resource utilization (77–94% reduction in nodes). These excellent performance metrics not only prove MIHE-RRT’s advantages in complex environments but also make it particularly suitable for practical robot navigation applications requiring reliable and efficient path planning. Full article

A multi-strategy improved honey badger algorithm (MIHBA) is proposed to address the problem that the honey badger algorithm may fall into local optimum and premature convergence when dealing with complex optimization problems. By introducing Halton sequences to initialize the population, the diversity of the population is enhanced, and premature convergence is effectively avoided. The dynamic density factor of water waves is added to improve the search efficiency of the algorithm in the solution space. Lens opposition learning based on the principle of lens imaging is also introduced to enhance the ability of the algorithm to get rid of local optimums. MIHBA achieves the best ranking in 23 test functions and 4 engineering design problems. The improvement of this paper improves the convergence speed and accuracy of the algorithm, enhances the adaptability and solving ability of the algorithm to complex functions, and provides new ideas for solving complex engineering design problems. Full article

Infill drilling is one of the most effective methods of improving the performance of polymer flooding. The difficulties related to infill drilling are determining the optimal numbers and placements of infill wells. In this study, an improved Archimedes optimization algorithm with a Halton sequence (HS-AOA) was proposed to overcome the aforementioned difficulties. First, to optimize infill well placement for polymer flooding, an objective function that considers the economic influence of infill drilling was developed. The novel optimization algorithm (HS-AOA) for infill well placement was subsequently developed by combining the AOA with the Halton sequence. The codes were developed in MATLAB 2023a and connected to a commercial reservoir simulator, Computer Modeling Group (CMG) STARS, Calgary, AB, Canada to carry out infill well placement optimization. Finally, the HS-AOA was compared to the basic AOA to confirm its reliability and then used to optimize the infill well placements for polymer flooding in a typical offshore oil reservoir. The results showed that the introduction of the Halton sequence into the AOA effectively increased the diversity of the initial objects in the AOA and prevented the HS-AOA from becoming trapped in the local optimal solutions. The HS-AOA outperformed the AOA. This approach was effective for optimizing the infill well placement for polymer flooding processes. In addition, infill drilling could effectively and economically improve the polymer flooding performance in offshore oil reservoirs. The net present value (NPV) of the polymer flooding case with infill wells determined by HS-AOA reached USD 3.5 × 10⁸, which was an increase of 7% over that of the polymer flooding case. This study presents an effective method for optimizing infill well placement for polymer flooding processes. It can also serve as a valuable reference for other optimization problems in the petroleum industry, such as joint optimization of well control and placement. Full article

This paper measures the impact of the number of Halton draws in excess of $\lceil \sqrt{n}\rceil$ on technical efficiency in the generalized true random effects (four-component) stochastic frontier model estimated by simulated maximum likelihood. A substantial set of Monte Carlo simulations demonstrates that increasing the number of Halton draws to $\lceil n^{3/4}\rceil$ ($\lceil n^{2/3}\rceil$) decreases the mean squared error of the total technical efficiency estimates by $6.1$ ($4.9$) percent. Furthermore, increasing the number of Halton draws either improves or has no detrimental impact on correlation, mean squared error, relative bias, and upward bias for persistent, transient, and total technical efficiency. An energy sector application is included, to demonstrate how these issues can arise in practice, and how increasing Halton draws can improve parameter and efficiency estimates in empirical work. Full article