Search Results (82)

Reviews of artificial intelligence (AI) for mobile robots usually cover one competence—perception, SLAM, path planning, control, or reinforcement learning—and rarely show how these combine into a working system. We take the opposite view and treat autonomy as one pipeline: sensing, perception, localization and mapping, prediction, planning, visual servoing and control, high-level decision-making, and continual learning. We survey how AI has reshaped each stage for industrial and service robots across Industry 4.0, 5.0, and the emerging Industry 6.0. Using a structured, PRISMA-informed protocol with explicit search strings, inclusion criteria, and cross-embodiment transfer rules, we screen the literature, analyze a corpus drawn mainly from the last five years, and position it against prior surveys with a coverage matrix that exposes their single-block focus. Four findings stand out. Perception and localization approach engineering maturity through multimodal fusion and foundation vision models. Planning and control stay effective but computationally demanding. Decision-making, now driven by large language and vision–language–action models, is powerful yet unverifiable and fails under safety constraints. Lifelong learning is almost absent from deployed systems. The decisive weaknesses sit at the interfaces: at the perception–planning, planning–control, and control–decision handoffs the sim-to-real gap, limited on-robot compute, and scarce industrial data compound. We compare AI families by technology readiness, catalog datasets and benchmarks, examine the safety-certification barrier, and consolidate cross-cutting gaps. We close with a staged roadmap toward Industry 6.0 and a next-generation architecture coupling a foundation perception backbone, a world model and digital twin, a continual-learning memory, and a reasoning core wrapped by a safety monitor. The aim is to move from cataloging algorithms to engineering integrated autonomy. Full article

This paper presents an open-source, fully parametric three-component total knee arthroplasty (TKA) implant system and an automated surgical planning pipeline, addressing the absence of publicly available, modifiable TKA design frameworks in the literature. A cruciate-retaining femoral component, tibial baseplate, and polyethylene insert were designed in Autodesk Fusion with 160 parameters governing all anatomically significant geometry. The femoral articulation surface uses a tangency-constrained triple-radius J-curve. An automated Blender (v. 5.1) Python pipeline performs bone model alignment, size selection from a twelve-size chart, Boolean resection via parametric cutting blocks, and final component placement. Prototypes were 3D printed and validated on 1:1 anatomical bone models. The implant system achieved flush seating on all resection surfaces and impingement-free articulation through the full range of motion on all bone sets. The pipeline correctly aligned bone models, performed resections, and selected appropriately sized implants in all 11 cases, processing each in 1–1.5 min. The system is the first open-source TKA framework to simultaneously provide full parametric definition, documented design rationale, three-component coverage, an automated planning pipeline, and an additive manufacturing fabrication path. By releasing the complete parametric model and pipeline as open source, this work enables independent validation, population-specific adaptation, and iterative improvement by the global research community. Full article

Cable routing is a pivotal design component for electrical systems and safety-critical engineering fields, such as nuclear propulsion systems, nuclear power plants and aircraft. Scientific and optimized routing schemes are essential for efficient and safe power and signal transmission and for mitigating system failure risks. Previous studies have adopted heuristic search and swarm intelligence optimization algorithms for cable path planning; however, these methods tend to converge to local optima under complex constraints and cannot theoretically guarantee global optimality, failing to address multi-constraint, high-dimensional optimization challenges of confined-space cable routing. This paper proposes a mathematical programming-based systematic optimization model: it first discretizes continuous three-dimensional space into a grid coordinate system and constructs a composite cost field integrating geometric distance and thermal interference, then formulates a multi-objective optimization model considering path length, thermal impact and routing feasibility, which is converted into a single-objective problem via normalized weighting coefficients and solved by exact mathematical programming techniques, yielding a best feasible solution together with a provable lower bound and an optimality gap. When the solver converges within the time limit, global optimality for the discretized model can be certified. Simulation results show the proposed method reduces overall path cost by an average of 31.8% compared with classical algorithms like the A* algorithm, Dijkstra’s algorithm, Rapidly-exploring Random Tree (RRT), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Furthermore, it cuts decision variables by an average of 70% (up to 82% in complex scenarios) against the 0–1 Integer Linear Programming (ILP) model and the graph-theoretic Multi-Commodity Flow (MCF) model with multi-cost considerations. These results preliminarily validate the favorable solution quality, computational efficiency and engineering applicability of the proposed model for confined-space cable routing optimization. Full article

Over the past three decades, geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) have become essential tools for spatial analysis in urban, environmental, and land-use research. This scoping review systematically maps and synthesizes the global literature on GWR and MGWR published between 1996 and 2026, aiming to identify the research hotspots, evolutionary paths, and cutting-edge trends. Bibliometrics and CiteSpace visualization tools are used to conduct a multi-dimensional visual analysis of thousands of selected articles, including countries, institutions, core authors, highly cited keywords, and key documents. The results show that the current research focuses on spatial heterogeneity, multiscale analysis, GWR model optimization, non-stationarity characterization, and simulation of urban land-use change. Potential future directions include the construction of spatiotemporal integrated models, the integration of high-performance computing, and the expansion of interdisciplinary applications. The results of this study can help scholars fully understand the current research status and future directions, and provide a scientific spatial analysis framework for practitioners in urban planning, land resource management, and environmental assessment. Furthermore, the conclusions can provide theoretical support and a decision-making basis for the government to formulate intelligent and refined urban development policies. Full article

To improve the machining ability of serial robots, an optimized SSI method is proposed to realize online modal identification. Firstly, the method employs the NExT technology, modal confidence factor, and modal assurance criterion to improve the parameters’ identification accuracy. Next, the machining vibration data at key points in the path of the machining robot are used to identify the modal frequency and damping ratio. Finally, the experimental results show that the optimized SSI method can achieve estimation errors within 7%, and the method of optimized SSI is more accurate than the traditional SSI method. Therefore, if only the modal frequency and the damping ratio are concerned, the method can realize the online recognition of modal parameters in the cutting path of the machining robot, which can provide key input parameters for the process planning and optimization. Full article

To reduce the conservatism of local trajectory planning in dynamic road scenarios caused by redundant projection of predicted trajectories, this paper proposes a hierarchical hybrid trajectory-planning framework with a time-to-arrival (TTA)-driven dynamic risk-filtering mechanism. In the Frenet coordinate system, road boundaries, ego states, and static and dynamic obstacles are represented uniformly to construct an S–L fused risk field and an S–T spatiotemporal interaction graph, enabling the filtering of temporally irrelevant conflict regions based on TTA relationships. At the path-planning layer, risk-guided adaptive sampling is integrated with dynamic programming and quadratic programming to improve search efficiency and trajectory quality. At the speed-planning layer, spatiotemporal coordination is achieved through non-uniform discretization, safe-corridor extraction, and speed-profile optimization. Simulation results show that the proposed method generates safe, smooth, continuous, and executable local trajectories in scenarios involving static-obstacle avoidance, adjacent-vehicle cut-ins, non-motorized road-user crossings, and mixed multi-obstacle interactions, while reducing unnecessary deceleration and detours. Ablation results further indicate that adaptive sampling reduces the number of DP search nodes by approximately 50% and the average planning time by about 30%, while maintaining a nearly unchanged minimum safety distance. These findings demonstrate that the proposed framework effectively suppresses redundant conflict regions and improves planning efficiency, solution feasibility, and motion continuity without compromising safety. Full article

In automated garment-cutting systems, idle-travel path planning becomes computationally expensive when the number of cutting pieces reaches medium-to-large scales (80–150 nodes), directly affecting production efficiency. To address the limitations of traditional heuristic methods in solution quality and runtime stability, this study proposes a cluster-based local search framework integrating K-means clustering with a Cluster-Aware Constrained Lin–Kernighan (CAC-LK) algorithm. K-means partitions entry points into compact spatial clusters to reduce the computational scale, and an adaptive depth-constrained CAC-LK procedure optimizes intra-cluster paths while maintaining a predictable runtime. Inter-cluster routes are connected using a nearest-neighbor strategy. Experiments on simulated datasets with 85 and 140 nodes show that the proposed method reduces the idle-travel distance by 4–10% compared with K-means + 3-opt while achieving a more stable runtime than unconstrained K-means + LK. The results demonstrate that the proposed framework provides an effective balance between path quality, scalability, and computational stability, showing strong applicability for real-time intelligent garment-cutting systems. Full article

Wing–diesel hybrid ships are a practical approach to sustainable maritime transport that harnesses wind energy to supplement diesel propulsion and reduce carbon emissions. The core optimization problem addressed in this study is the global energy efficiency optimization of path planning and propulsion system cooperative control for wing–diesel hybrid ships under two typical sail operation statuses (sail-deployed and sail-stowed) with dynamic changes in complex maritime meteorological and hydrological conditions. To address this issue, this paper proposes a global energy efficiency optimization framework based on an improved Deep Q-Network (DQN) and D*Lite algorithm. Firstly, the D*Lite algorithm is reconstructed with an incremental replanning mechanism and risk-aware cost function to generate real-time safe path constraints. Secondly, the DQN is improved by adopting a dueling network, noisy exploration and prioritized experience replay, and a differentiated reward function dynamically weighted by sail statuses is designed for it. Finally, a fuel consumption prediction model based on the gradient boosting algorithm is integrated into the reward function to realize an accurate energy efficiency assessment. Empirical results confirm that the framework achieves remarkable carbon reduction effects: the optimized routes reduce the total fuel consumption by 5.02%, cut carbon dioxide emissions by 140.66 tons, and improve the energy efficiency operational index by 7.50%. This framework provides an effective technical solution for the dynamic energy efficiency optimization of wing–diesel hybrid ships under different sail operation statuses. Full article

The European Union aims to cut greenhouse gas emissions by 55% by 2030 and reach climate neutrality by 2050, targets that depend on expanding renewable generation in the European energy system. While photovoltaic (PV) capacity has grown quickly in several member states, others remain far behind. This paper frames that divergence as a systems planning problem: installed MW expands through diffusion-like dynamics, but the conversion of investment into energizable capacity is filtered by grid-integration constraints and institutional throughput. The study develops a macro-level framework for systems-level assessment and decision support to guide PV capacity planning and budget allocation using official 2012–2022 data for 22 EU countries. We combine (i) unsupervised clustering of standardized national deployment trajectories, (ii) bounded logistic fits interpreted as an operational diffusion-with-saturation representation that yield comparable growth parameters and maturity years (80–90% of the estimated ceiling), and (iii) a proportional reallocation scenario for countries below 5 GW in 2022. Three clusters emerge—steady growth, early plateau, and atypical paths—and an analytically tractable maturity indicator integrates capacity, rate, and timing in a single measure. In a 10 GW reallocation scenario, average progress toward the 5 GW benchmark rises from 9.8% to 23.1%, closing about 14.8% of the aggregate shortfall. The allocation experiment reveals a clear asymmetry: systems with an existing installed base convert additional MW into benchmark progress more efficiently than very low-baseline systems, where binding constraints are more likely to sit in permitting, interconnection queues, and hosting capacity rather than in finance alone. Turning these allocations into usable capacity depends on timely interconnection and power-electronics integration and on grid-enablement constraints such as interconnection readiness, inverter compliance, and local hosting capacity in high-penetration areas. The contribution is a transparent, updateable decision-support pipeline that links observed trajectory regimes to a maturity “clock” and an auditable allocation baseline, making the trade-off between closing capacity gaps and respecting feasibility filters explicit in an EU system with heterogeneous national subsystems. The proposed approach links macro-level maturity clusters to operational feasibility signals in the grid integration layer, showing that modeling-based allocation can improve system progress but cannot substitute grid-enablement measures, highlighting the importance of regional coordination in the EU energy system under heterogeneous national trajectories. Full article

Planned cutting is a core technique for intelligent coal mining, relying on high-precision geological models of fully mechanized mining faces to plan the cutting trajectory of mining equipment, with model accuracy as a prerequisite for intelligent mining. To address the limitations of traditional interpolation methods in dynamic model updating and the technical gap between geological information and equipment control parameters, this study proposes a coal mining machine cutting path control method based on dynamic geological models. An improved smooth discrete interpolation method is developed to realize dynamic updating of the geological model, effectively improving the accuracy of local geological models and ensuring safe mining operations. Meanwhile, a method for converting geological information into coal mining equipment control parameters is proposed, breaking the technical barrier between geological data and production control information and laying a foundation for unmanned and intelligent mining. Field tests conducted in a shaft coal mine in Shaanxi demonstrate that the method achieves precise control of the coal mining machine’s trajectory: during a 7-day trial, the working face advanced 56 m and mined 51,000 tons of coal with minimal human intervention. Comparative analysis shows that the error between the planned cutting based on the dynamic geological model and manual cutting is within 10 cm, and the drum height curve is smoother, reducing frequent adjustments and facilitating equipment protection. Dynamic model updating ensures high accuracy, with an average absolute error of 0.029 m at 5 m from the update point and 0.101 m at 10 m, meeting the requirements for automated cutting. The successful application of this method verifies its feasibility in actual mining processes, providing a new technical approach for achieving unmanned and intelligent coal mining. Full article

With the rapid development of Head-Up Display (HUD) technology for vehicles, optical freeform mirrors, as its core optical components, are crucial for achieving system compactness and high imaging quality. However, their complex surface shapes and large-aperture characteristics pose significant challenges to ultra-precision manufacturing. This study presents a systematic optimization framework for the ultra-precision machining of HUD optical freeform mold cores, integrating surface design, tool path planning, vibration analysis, and process parameter optimization. Firstly, based on the XY polynomial freeform surface model, an off-axis three-mirror HUD system was designed, and the surface parameters and machining dimensions of the mold core were determined. For the Single-Point Diamond Turning (SPDT) Slow Tool Servo (STS) process, a hybrid trajectory planning method combining equidistant projection and cubic spline interpolation was proposed to ensure the smoothness and accuracy of the tool path. Through theoretical analysis and experimental verification, the selection criteria for tool parameters such as tool nose radius and effective cutting angle were clarified, and the mechanistic impact of Z-axis vibration on surface roughness and waviness was quantitatively revealed. Finally, through ultra-precision turning experiments and on-machine measurement, a high-precision freeform surface mold core was successfully fabricated. This validates the effectiveness and feasibility of the proposed process solution and provides technical support for the high-quality manufacturing of HUD optical elements. Full article

Achieving precise prediction and intelligent control remains a pivotal challenge in cutting processes. This need is addressed through a comprehensive survey of three critical enabling technologies: cutting force/temperature modeling, tool path planning, and vibration suppression. First, the evolution of cutting force and temperature modeling is analyzed, tracing its progression from traditional analytical methods and finite-element numerical simulations to data-driven models such as machine learning (ML) and physics-informed neural networks. This analysis highlights multiphysics coupling and model–data fusion as key to enhancing prediction accuracy. Subsequently, the evolution of tool path planning is examined, showing its development from a geometric interpolation problem into a multi-objective optimization challenge incorporating dynamic constraints, involving computational geometry, graph theory, and meta-heuristic algorithms. Finally, stability analysis based on time-delay differential equations, state identification via signal processing and ML, and active control strategies for vibration suppression are discussed. In conclusion, mathematical methods are shown to be fundamentally integrated throughout the ‘perception–prediction–decision–control’ closed-loop of the cutting process. This integration provides a solid theoretical foundation and technical support for building high-performance manufacturing systems dedicated to complex curved critical components. Full article

Crawler-type combine harvesters feature labor-intensive operation, tough steering and complex environments in paddy fields, necessitating reliable automatic operation to ensure efficient and complete harvesting. An error threshold-based autonomous navigation system for crawler-type combine harvesters was developed by using right-angle turning according to unilateral brake steering. Based on the chassis structure and working principles, a moving control system was designed to achieve automatic control of steering, speed and throttle. A global path planning method was proposed to generate a spiral path by giving reference points and operation directions. A path tracking method based on the error threshold was developed to calculate both lateral and heading errors in real-time, and we executed the adjustment strategy to ensure rapid alignment and high-precision tracking. A right-angle turning method was implemented to prevent missed cutting and crop damage by giving an adjustment distance. Field tests showed that the maximum lateral and heading errors for straight-line path tracking were 10.25 cm and 1.94°, respectively. The maximum lateral and heading errors for right-angle turning were 17.64 cm and −14.46°, respectively. It was concluded that the newly developed autonomous navigation system showed adequate path tracking accuracy and stability, meeting working requirements in crop harvesting. Full article

A system-level scheme that couples a multi-dimensional attention-fused vision model and an improved Dijkstra planner is proposed for basketball robots in complex scenes. Fast-moving object detection, cluttered background recognition, and real-time path decision are targeted. For vision, the proposed YOLO11 with Multi-dimensional Attention Fusion (YOLO11-MAF) is equipped with four modules: Coordinate Attention (CoordAttention), Efficient Channel Attention (ECA), Multi-Scale Channel Attention (MSCA), and Large-Separable Kernel Attention (LSKA). Detection accuracy and robustness for high-speed basketballs are raised. For planning, an improved Dijkstra algorithm is proposed. Binary heap optimization and heuristic fusion cut time complexity from $O\left(V^2\right)$ to $O\left(\right(V+E\left)\log V\right)$. Redundant expansions are removed and planning speed is increased. A complete robot platform integrating mechanical, electronic, and software components is constructed. End-to-end experiments show the improved vision model raises mAP@0.5 by 0.7% while keeping real-time frames per second (FPS). The improved path planning algorithm cuts average compute time by 16% and achieves over 95% obstacle avoidance success. The work offers a new approach for real-time perception and autonomous navigation of intelligent sport robots. It lays a basis for future multi-sensor fusion and adaptive path planning research. Full article

Thin-walled cellular structures of continuous fiber-reinforced thermoplastic composites (CFRTPCs) have received much attention from both academics and industry due to their superior properties. Additive manufacturing provides an efficient solution for fabricating these thin-walled cellular structures of CFRTPCs. However, the process often requires cutting fiber filaments at jumping points during printing. Furthermore, the filament may twist, fold, and break due to sharp turns in the printing path. These issues adversely affect the mechanical properties of the additive manufactured part. In this paper, a Euler graph-based path planning method for additive manufacturing of CFRTPCs is proposed to avoid jumping and sharp turns. Euler graphs are constructed from non-Eulerian graphs using the method of doubled edges. An optimized Hierholzer’s algorithm with pseudo-intersections is proposed to generate printing paths that satisfy the continuity, non-crossing, and avoid most of the sharp turns. The average turning angle was reduced by up to $20.88\%$ and the number of turning angles less than or equal to $120°$ increased by up to $26.67\%$ using optimized Hierholzer’s algorithm. In addition, the generated paths were verified by house-made robot-assisted additive manufacturing equipment. Full article