Search Results (543)

Lane-changing and overtaking constitute a typical complex driving manoeuvre for intelligent vehicles operating on structured roads; this task demands that the vehicle not only plan a safe and smooth lane-change trajectory but also requires the control system to maintain high tracking accuracy and lateral stability. Addressing the challenges of real-time path planning and stable tracking control inherent in lane-changing and overtaking scenarios, this paper proposes a trajectory planning and control method that integrates an improved artificial potential field (APF) approach with a lateral–longitudinal cooperative controller. Regarding path planning, the proposed method constructs attractive and repulsive fields based on the APF framework, while introducing virtual target points, elliptical obstacle models, and velocity-dependent repulsive fields to mitigate the risk of local minima and enhance dynamic obstacle avoidance capabilities. To ensure trajectory continuity and trackability, a fifth-order polynomial is employed to smooth the planned path. Regarding control, the method utilises a Linear Quadratic Regulator (LQR)—optimised via a genetic algorithm—for lateral control; this is coupled with a dual-PID longitudinal controller that generates throttle and braking commands based on vehicle speed errors, thereby establishing a cooperative lateral–longitudinal tracking control strategy. The proposed method is validated using a CarSim–MATLAB/Simulink co-simulation platform. Simulation results demonstrate that the proposed method significantly improves trajectory-tracking accuracy and vehicle stability during lane-changing and overtaking manoeuvres. In a single lane-change scenario, the maximum lateral error is reduced from approximately 0.62 m to 0.22 m, and the heading angle error decreases from about 0.058 rad to 0.01 rad; in a continuous lane-changing scenario, the maximum lateral error drops from approximately 0.30 m to 0.04 m, while the heading angle error falls from about 0.016 rad to 0.005 rad. Furthermore, the yaw rate, sideslip angle, and lateral acceleration are reduced by 39.1%, 22.2%, and 28.9%, respectively. These results confirm that, under the specified simulation conditions, the proposed method exhibits superior tracking performance and stability. Future research could further explore more complex driving scenarios, such as curved roads, multi-vehicle interactions, sensor uncertainties, actuator delays, and real-vehicle field experiments. Full article

Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

Planning a low-detectability route for a fixed-wing UAV in mountainous environments with radar-based sensing constraints remains highly challenging. Conventional approaches struggle to simultaneously ensure both path quality and operational safety. To address this problem, this paper proposes a two-layer planning framework in which a Risk-A* algorithm provides a global reference route, while a model predictive control (MPC) scheme performs online receding-horizon trajectory optimization. The proposed method combines prior radar-platform information with time-varying detection-risk cues to generate terrain-masked and detection-feasible trajectories. In this study, the framework is instantiated and evaluated on a representative fixed-wing medium-altitude long-endurance (MALE) UAV, where “medium-altitude” denotes the platform class rather than the flight altitude maintained during the low-altitude flight segment. As a result, the UAV can complete the entire flight while reducing the detection-risk metric and overall planning cost. Simulation results on two DEM-based mountainous terrain zones, with one nominal start-goal pair specified in each terrain zone and 50 repeated executions conducted for each scenario, demonstrate that the Risk-A*-MPC framework may yield slightly longer paths and flight times; however, it consistently satisfies the no detection-threshold-exceedance requirement under the tested conditions. In the two main terrain-zone scenarios, the recorded maximum MPC solve time was 0.812 s, which remained below the 3 s control update period and supports the real-time executability of the online MPC layer on the tested computational platform. Full article

Facility agricultural robots operating in greenhouse environments often encounter narrow passages, dense obstacle distributions, and frequent path-direction changes, which increase the difficulty of achieving efficient and smooth autonomous navigation. Conventional A* algorithms usually suffer from redundant node expansion, dense turning-point distributions, and insufficient path continuity under such constrained conditions. To address these issues, this study proposes an improved A*-based path-planning framework that integrates adaptive heuristic weighting, dynamic corner correction, and Bézier-curve-based path smoothing. Rather than introducing an entirely new planning paradigm, the proposed method coordinates several existing optimization strategies within a unified framework to improve search efficiency, path regularity, and path continuity for facility agricultural scenarios. The adaptive heuristic weighting strategy dynamically adjusts the contribution of the heuristic term according to the relative distance between the current node and the target node, thereby improving global search guidance while reducing unnecessary exploration. Dynamic corner correction is introduced to suppress zigzag path structures and reduce redundant turning nodes in obstacle-dense regions, while Bézier-curve-based smoothing is employed to improve path continuity and compatibility with the kinematic characteristics of agricultural mobile robots. Simulation experiments were conducted on grid maps and greenhouse-like environments with different obstacle distributions, and comparative evaluations were performed against Dijkstra, RRT, and conventional A* algorithms. Under representative simulation scenarios, the proposed framework reduced the number of turning points by up to 53.7% and decreased computation time by approximately 19.4% compared with the conventional A* algorithm, based on the average results of repeated trials under identical conditions. In addition, physical platform experiments on a ROS2-based agricultural robot demonstrated that the planned trajectories maintained relatively stable navigation performance and smoother directional transitions in constrained greenhouse-like environments. The results indicate that the proposed framework achieves a more balanced trade-off between computational efficiency, path compactness, and path smoothness than the benchmark methods considered in this study. Nevertheless, the current validation remains limited to structured or semi-structured greenhouse environments under static obstacle conditions. Future work will focus on improving adaptability to dynamic agricultural scenarios and integrating the framework with real-time perception and motion-control systems for practical greenhouse deployment. Full article

Environmental monitoring in hazardous indoor zones conventionally relies on fixed-sensor networks or manual inspections, both of which suffer from spatial blind spots and increased human exposure risks. This paper addresses the problem of transforming sparse, mobile sensor measurements into spatially resolved risk assessments in GPS-denied environments. We propose a Hazard Index (HI) framework that normalizes environmental parameters against established safety thresholds into a unified, graduated risk metric with $O\left(N\right)$ computational complexity, where N is the number of monitored parameters. The framework is designed for multi-parameter hazard assessment; the present work validates the computational pipeline, spatial mapping methodology, and classification logic through single-parameter CO₂ detection ($N=1$) deployed on a LiDAR-guided robotic platform integrating an MQ-135 gas sensor interfaced via a NodeMCU ESP8266 microcontroller. Experimental validation across a 144 sq ft indoor area achieved a trajectory-following RMSE of 0.54 ft relative to planned waypoints using Hector SLAM without odometry, detected CO₂ concentrations ranging from 0.02% to 0.25%, and identified a hazardous region encompassing eight measurement points ($\mathrm{HI}\ge 1.0$) using a three-tier classification scheme (Safe, Elevated, Hazardous) within 225 s of active mapping. The framework provides a lightweight computational footprint suitable for real-time evaluation on an NVIDIA Jetson Nano. The proposed approach establishes a cost-effective, reproducible methodology for autonomous indoor environmental monitoring, with the modular architecture designed for future expansion to multi-parameter sensing. Full article

Urban Unmanned Aerial Vehicle (UAV) logistics operations are frequently constrained by the intersection of limited battery endurance and dynamic ground traffic. When mobile depots are delayed by congestion, onboard drone fleets experience extended idling periods, leading to constrained sortie generation and reduced asset utilization. To address this bottleneck, this paper introduces a traffic-predictive multi-UAV dispatch framework for deterministic day-ahead planning under modeled urban operating conditions. By coupling a count-derived macroscopic speed surrogate learned using XGBoost with a Particle Swarm Optimization (PSO)–Mixed-Integer Linear Programming (MILP) optimization architecture, the framework synchronizes mobile depot trajectories with forecasted low-congestion windows and pre-allocates endurance-feasible parallel aerial sorties. Controlled computational experiments across 30 synthetic routing instances demonstrate the potential value of this approach within the stated modeling assumptions. Compared to baseline clustered deployments, the traffic-aware framework raises mean fleet utilization from 0.43 to 0.63—a 46.2% relative improvement driven by temporal compression of the mission window rather than an absolute increase in flight hours. Furthermore, the proposed framework reduces total mission completion time by 69.87% relative to the conventional truck-only baseline, while achieving a 29.58% incremental gain over static speed drone deployments. These findings suggest that incorporating predictive ground traffic information into day-ahead UAV scheduling can improve modeled fleet efficiency; however, field validation with measured route-level speeds, real delivery demand, and operational constraints remains necessary before deployment-level claims can be made. Full article

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety. Full article

The safety of autonomous vehicles depends not only on perception and planning, but also on the correctness of low-level electronic signals that connect controllers and actuators. Errors at this interface, caused by hardware degradation, timing violations, software faults, or unexpected interactions, can lead to unsafe behavior even when high-level autonomy functions operate correctly. Existing safety mechanisms primarily focus on system behavior, trajectories, or controller design, leaving actuator-bound command streams largely unmonitored. This paper proposes a low-level, AI-enabled anomaly-detection layer for autonomous vehicle architectures. The core idea is to embed a lightweight observer within a virtualized master controller to monitor control-signal streams in real time without interfering with the primary control logic. The proposed framework combines a stacked LSTM sequence classifier with rule-based safety constraints and context-aware monitoring to detect physically implausible or temporally inconsistent command behavior before actuation. A proof-of-concept simulation study was conducted to evaluate the practicality of the approach using overtaking scenarios in a co-simulated high-level and low-level environment. The results show that the proposed concept can identify severe abnormal low-level behavior and provide preliminary warning/error indications, supporting its potential as a complementary safety layer at the control-to-actuation interface. Full article

Wire arc additive manufacturing (WAAM) has emerged as a promising technology for producing large-scale metal components due to its high deposition efficiency, low material cost, and design flexibility. However, the widespread industrial adoption of WAAM is hindered by challenges in geometric accuracy, process stability, and defect control, which are closely related to two critical aspects: trajectory planning and real-time weld seam tracking. This review provides a comprehensive and critical analysis of recent advances in both fields, with an emphasis on their interconnection rather than treating them as separate research streams. Unlike existing reviews that primarily summarize path planning algorithms or image processing techniques in isolation, this paper explicitly examines the integration challenges and synergistic potential between offline trajectory optimization and online vision-based monitoring. Key topics include adaptive path strategies for sharp corners and intersections, interlayer filling methods to mitigate heat accumulation and residual stress, as well as passive and active visual sensing technologies for molten pool characterization and defect detection. The review further identifies a persistent gap in closed-loop systems that combine real-time image feedback with dynamic path replanning. Based on the analysis of representative studies, current limitations are discussed and future research directions are proposed, including the development of digital twins, multi-modal data fusion, and reinforcement learning-based adaptive control. This review offers a distinct perspective aimed at advancing intelligent, high-precision WAAM systems for complex metal components. Full article

Recent progress in prosthetic manipulation highlights the need for perception-driven control strategies that can adapt to diverse objects and user intent. This work presents a modular vision-guided grasping pipeline that integrates VLM-based object identification, orientation-aligned geometric modeling, and per-finger grasp planning for dexterous prosthetic hands. A Vision–Language Model (VLM) identifies the target object and activates the grasping pipeline only when recognition is confident, supporting intent-aware operation. From the segmented point cloud, an object-aligned bounding box (OBB) is constructed to provide a compact, orientation-aware representation of the object’s global extents, enabling more accurate distance and collision queries than axis-aligned boxes. Using this representation, the system evaluates candidate fingertip trajectories and selects contact poses for each finger independently, followed by Damped Least Squares inverse kinematics for joint-level execution. Preliminary experiments on a limited set of representative objects using the Linker Hand O7 demonstrate that the proposed pipeline achieves consistent grasp execution and exhibits promising real-time performance within controlled scenarios. In simulation, the proposed pipeline achieved a maximum segmentation accuracy of 93.4%, while hardware experiments on the Linker Hand O7 achieved 93.2% segmentation accuracy, confirming stable grasp execution across representative objects. While the evaluation is not yet comprehensive, the results indicate that combining semantic object identification with lightweight geometric reasoning can support efficient and adaptable grasp generation suitable for future prosthetic applications. Full article

While Unmanned Aerial Systems (UAS) are increasingly used for infrastructure inspection, a critical gap exists between optimized path planning and reliable real-world execution. Fully autonomous flights face regulatory constraints and environmental risks, whereas manual piloting introduces inconsistencies that compromise data quality. To address this gap, this study proposes a human-in-the-loop assistive navigation platform that enables pilots to follow preplanned inspection trajectories while maintaining manual control. The proposed system integrates an Augmented Reality (AR)-based guidance module that provides real-time viewpoint localization with a mesh-coupled quality monitoring module that continuously evaluates view redundancy and triangulation uncertainty. A proof-of-concept field demonstration through an on-site façade inspection example indicates that the proposed platform has the potential to improve the consistency of viewpoint distribution, achieving closer adherence to planned spacing and stand-off distance. This results in more uniform spatial sampling, enhanced view redundancy, and reduced variability in theoretical uncertainty, leading to improved geometric conditions for Structure-from-Motion (SfM) reconstruction. Overall, the field demonstration highlights the potential of combining computational guidance with human decision-making to support reliable and high-quality UAS-based infrastructure inspection. Full article

Export supply chains (ESCs) for perishable fruits, such as mangoes and avocados, are shaped by complex supply–demand dynamics and macroeconomic conditions. However, limited forecasting of these dynamics constrains strategic planning and investment in Australia’s horticultural sector. This study assesses the longitudinal growth and future potential of mango and avocado exports. To achieve this, the study identifies influential supply–demand dynamics and applies time-series forecasting to understand the export trends. Historical export–import data were analysed for mango and avocado from 1992 to 2024, including volume, value, per capita GDP (Australia and key importing nations), real exchange rate, and real interest rate. Holt’s exponential smoothing was used to forecast export trends, supported by unit root testing in RStudio 4.2.3 and model execution in SPSS version 30. ARIMA and ARIMAX models were applied to stationary variables to improve mango export forecasts. The results show that avocado exports follow a strong upward trajectory, while mango exports remain volatile due to logistical inefficiencies and informal trade disruptions. ARIMAX modelling confirmed that production and consumption volumes significantly enhance forecast accuracy. Macroeconomic trends, rising GDP, declining real interest rates, and stable real exchange rates further reinforce Australia’s competitive position in the destination markets. The long-run trends in export volume and value suggest that both the mango and avocado sectors hold potential for further export growth, although the higher volatility observed in the avocado series indicates that expansion should be approached cautiously. To sustain this growth, maintaining a balanced relationship between production capacity and export demand, particularly for commodities exhibiting higher volatility, will be essential for ensuring stable and efficient export performance over time. Full article

Autonomous navigation on unknown uneven terrain remains a critical challenge for unmanned ground vehicle (UGV) deployed in unstructured environments such as disaster relief, wilderness exploration, and off-road logistics. Existing motion planning methods for such environments suffer from three key limitations: under-utilization of the solution space due to discretized terrain assessment, difficulty in transforming complex terrain safety constraints into optimization-compatible forms, and the inherent trade-off between environmental modeling accuracy and real-time performance. This paper presents a hierarchical motion planning framework that enables safe and fast navigation of UGV on unknown uneven terrain. We first construct a traversability map based on terrain slope, roughness, and sparsity extracted from ground point cloud clusters. Non-traversable points are then transformed via spherical inversion and inverse mapping to generate terrain safety corridors composed of a series of convex polygons. The geometric containment relationship between the vehicle’s convex hull and the corridor is reformulated as continuously differentiable Control Barrier Function (CBF) constraints to ensure driving safety. The front-end employs a kinodynamic Hybrid A* algorithm with a traversability-aware node pruning strategy, while the back-end trajectory optimization embeds the CBF constraints as hard constraints within the optimization loop to guarantee forward invariance of the safety set under the linearized dynamics. The proposed framework achieves full-shape collision avoidance without sacrificing the solution space, while maintaining real-time performance for autonomous navigation on complex terrain. Full article

The A* algorithm is widely adopted across agriculture, robotics, and GPS navigation for efficient route planning, yet it faces challenges in balancing search efficiency with path quality. To address these limitations, we introduce Octile–Annealed, a novel heuristic that augments the classic Octile distance with a distance-dependent annealing weight. Specifically, Octile–Annealed scales the Octile metric by a smooth function of the current node’s Euclidean distance to the final location, yielding a heuristic that is gentle near the target and more directive when far away. This design retains the geometric fidelity of Octile, accelerates search convergence in open regions, and preserves guidance in constrained corridors. Beyond discrete planning, we incorporate adaptive Bézier smoothing to post-process the grid path into a collision-free, curvature-friendly trajectory. This is particularly relevant in agricultural environments (e.g., orchard rows and cross-aisles), where machines must follow efficient routes without abrupt turns that could slow operations or risk crop damage. We benchmark Octile–Annealed against three established baselines—Euclidean and Octile—on orchard-like grids of varying sizes and obstacle patterns. The results show that Octile–Annealed consistently reduces computation time while maintaining competitive raw path lengths and producing short, smooth Bézier trajectories. Overall, the proposed heuristic enhances A*’s operational efficiency and route quality, making it well-suited for complex, structured agricultural layouts and for general navigation tasks that benefit from smooth post-processing. However, it must be acknowledged that these comparative performance metrics are strictly limited to simulated grid cases; consequently, comprehensive validation using actual field data remains necessary to fully confirm their practical applicability under real-world agricultural conditions. Full article