Search Results (270)

In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes an adaptive lookahead Pure Pursuit method incorporating angular velocity feedback. By dynamically adjusting the lookahead distance according to real-time attitude changes, the method enhances coordination between path curvature and robot stability. To enable systematic evaluation, three time-series-based metrics are introduced: mean absolute yaw error (MAYE), peak-to-peak fluctuation amplitude, and the standard deviation of angular velocity, with overshoot occurrences included as an additional indicator. Field experiments demonstrate that the proposed method outperforms baseline algorithms, achieving lower yaw errors (0.61–0.66°), reduced maximum deviation (≤3.7°), and smaller steady-state variance (<0.44°²), thereby suppressing high-frequency jitter and improving turning convergence. Under typical working conditions, the method achieved a mean yaw deviation of 0.6602°, a fluctuation of 5.59°, an angular velocity standard deviation of 10.79°/s, and 155 overshoot instances. The yaw angle remained concentrated around the target orientation, while angular velocity responses stayed stable without loss-of-control events, indicating a favorable balance between responsiveness and smoothness. Overall, the study validates the robustness and adaptability of the proposed strategy in complex orchard scenarios and establishes a reusable evaluation framework, offering theoretical insights and practical guidance for intelligent agricultural machinery optimization. Full article

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

This paper proposes a fast midcourse trajectory optimization method by using the Bézier curve as a transcription scheme to represent the interceptor trajectories. First, the trajectory optimization problem is established with the constraints during midcourse guidance and the performance index of the terminal velocity. Then, the interceptor position coordinates are represented using Bézier functions, which directly satisfy the boundary constraints. Other state and control variables are also expressed as Bézier functions. Finally, the original trajectory optimization problem is transformed into optimizing the Bézier parameters, which can be obtained by sequential quadratic programming. Numerical examples verify the rapidity of the proposed method when compared with various traditional numerical optimization methods. In addition, the result of the proposed method can be used as a fast solution satisfying all the boundary and path constraints, and it can also be used as an initial guess for further optimizations. Full article

To improve the adaptability of 4WID electric vehicles under various operating conditions, this study introduces a model predictive control approach utilizing a neural network for adaptive weight parameter prediction, which integrates four-wheel steering and longitudinal driving force control. To address the difficulty in adjusting the MPC weight parameters, the neural network undergoes offline training, and the Snake Optimization method is used to iteratively optimize the controller parameters under diverse driving conditions. To further enhance vehicle stability, the real-time stability state of the vehicle is assessed using the $\beta -\dot{\beta }$ phase plane method. The influence of vehicle speed and road adhesion on the instability boundary of the phase plane is comprehensively considered to design a stability controller based on different instability degree zones. This includes an integral sliding mode controller that accounts for both vehicle tracking capability and stability, as well as a PID controller, which calculates the additional yaw moment based on the degree of instability. Finally, an optimal distribution control algorithm coordinates the longitudinal driving torque and direct yaw moment while also considering the vehicle’s understeering characteristics in determining the torque distribution for each wheel. The simulation results show that under various operating conditions, the proposed control strategy achieves smaller tracking errors and more concentrated phase trajectories compared to traditional controllers, thereby improving path tracking precision, vehicle stability, and adaptability to varying conditions. Full article

The optimization of trajectories for multiple robotic arms in a shared workspace is critical for industrial automation but presents significant challenges, including data sharing, communication overhead, and adaptability in dynamic environments. Traditional centralized control methods require sharing raw sensor data, raising concerns and creating computational bottlenecks. This paper proposes a novel Federated Learning (FL) framework for distributed multi-robotic arm trajectory optimization. Our method enables collaborative learning where robots train a shared model locally and only exchange gradient updates, preserving data privacy. The framework integrates an adaptive Rapidly exploring Random Tree (RRT) algorithm enhanced with a dynamic pruning strategy to reduce computational overhead and ensure collision-free paths. Real-time synchronization is achieved via EtherCAT, ensuring precise coordination. Experimental results demonstrate that our approach achieves a 17% reduction in average path length, a 22% decrease in collision rate, and a 31% improvement in planning speed compared to a centralized RRT baseline, while reducing inter-robot communication overhead by 45%. This work provides a scalable and efficient solution for collaborative manipulation in applications ranging from assembly lines to warehouse automation. Full article

Passive transit signal priority (TSP) strategies are widely recognized as effective tools for mitigating bus delays along urban arterials. However, existing TSP models primarily focus on through movements of transit vehicles, leading to potential delays for buses making turning movements. Moreover, these models do not adequately address signal coordination in multi-modal traffic systems involving both buses and private vehicles, resulting in increased delays and frequent stops for private vehicles. To address these limitations, this study proposes a binary mixed-integer linear programming (BMILP)-based signal progression band optimization model designed for multi-modal, path-level signal coordination. The model creates multiple progression bands for both straight and turning buses to minimize potential transit delays and enhance public transport service levels. By incorporating the mutual interactions between buses and private vehicles, progression bands for private vehicles are simultaneously optimized, enabling coordinated signal control that considers all users. The objective function maximizes passenger-equivalent service demand satisfied by the progression bands, explicitly accounting for mixed traffic flows and passenger loads. Numerical experiments on an urban arterial corridor demonstrate that, compared with the benchmark BUSBAND method, the proposed model achieves a 26% reduction in average bus delays, a 37% reduction in passenger car delays, and a 22% decrease in total stops, while also improving overall travel time reliability. Full article

The increasing penetration of electrified vehicles is accelerating the evolution of on-board and off-board charging systems, which must deliver higher efficiency, power density, safety, and bidirectionality under increasingly demanding constraints. This article presents a system-level review of state-of-the-art charging architectures, with a focus on galvanically isolated power conversion stages, wide-bandgap-based switching devices, battery pack design, and real-world implementation trends. The analysis spans the full energy path—from grid interface to battery terminals—highlighting key aspects such as AC/DC front-end topologies (Boost, Totem-Pole, Vienna, T-Type), high-frequency isolated DC/DC converters (LLC, PSFB, DAB), transformer modeling and optimization, and the functional integration of the Battery Management System (BMS). Attention is also given to electrochemical cell characteristics, pack architecture, and their impact on OBC design constraints, including voltage range, ripple sensitivity, and control bandwidth. Commercial solutions are examined across Tier 1–3 suppliers, illustrating how technical enablers such as SiC/GaN semiconductors, planar magnetics, and high-resolution BMS coordination are shaping production-grade OBCs. A system perspective is maintained throughout, emphasizing co-design approaches across hardware, firmware, and vehicle-level integration. The review concludes with a discussion of emerging trends in multi-functional power stages, V2G-enabled interfaces, predictive control, and platform-level convergence, positioning the on-board charger as a key node in the energy and information architecture of future electric vehicles. Full article

To address issues such as inadequate robustness in dynamic obstacle avoidance, instability in formation morphology, severe resource conflicts in multi-task scenarios, and challenges in global path planning optimization for unmanned aerial vehicles (UAVs) operating in complex airspace environments, this paper examines the advantages and limitations of conventional UAV formation cooperative control theories. A multi-UAV cooperative control strategy is proposed, integrating adaptive game theory and deep reinforcement learning within a unified framework. By employing a three-layer information fusion architecture—comprising the physical layer, intent layer, and game-theoretic layer—the approach establishes models for multi-modal perception fusion, game-theoretic threat assessment, and dynamic aggregation-reconstruction. This optimizes obstacle avoidance algorithms, facilitates interaction and task coupling among formation members, and significantly improves the intelligence, resilience, and coordination of formation-wide cooperative control. The proposed solution effectively addresses the challenges associated with cooperative control of UAV formations in complex traffic environments. Full article

Against the backdrop of the ongoing implementation of the “dual-carbon” strategy and green building policies, this study concentrates on the production stage of precast concrete (PC) components. A composite analytical framework that integrates the structural equation model (SEM) with the system dynamics model (SD) is developed, through which a systematic and dynamically responsive model for the joint optimization of carbon emissions and costs is proposed. The results demonstrate that (1) when investment in green policies is maintained within the range of 10–20%, a 4.2% reduction in carbon emissions can be achieved by 2030, while costs remain optimized; (2) under the scenario of moderate green policy investment (10–20%) combined with a carbon tax of CNY 100/ton, carbon emissions can be reduced by 7.52%, with costs also reaching an optimal level; and (3) among the multi-path emission reduction strategies, the technology optimization pathway and energy structure optimization pathway achieve reductions of 9.68% and 8.97%, respectively. These findings provide theoretical support for the coordinated control of carbon emissions and costs during the production stage of PC components, while also offering empirical evidence and practical guidance for governments in formulating green building policies and for enterprises in advancing low-carbon transitions. Full article

Network monitoring is becoming increasingly challenging as networks grow in scale, speed, and complexity. The evolution of monitoring approaches reflects a shift from device-centric, localized techniques toward network-wide observability enabled by modern networking paradigms. Early methods like SNMP polling and NetFlow provided basic insights but struggled with real-time visibility in large, dynamic environments. The emergence of Software-Defined Networking (SDN) introduced centralized control and a global view of network state, opening the door to more coordinated and programmable measurement strategies. More recently, programmable data planes (e.g., P4-based switches) and in-band telemetry frameworks have allowed fine grained, line rate data collection directly from traffic, reducing overhead and latency compared to traditional polling. These developments mark a move away from single point or per flow analysis toward holistic monitoring woven throughout the network fabric. In this survey, we systematically review the state of the art in network-wide monitoring. We define key concepts (topologies, flows, telemetry, observability) and trace the progression of monitoring architectures from traditional networks to SDN to fully programmable networks. We introduce a taxonomy spanning local device measures, path level techniques, global network-wide methods, and hybrid approaches. Finally, we summarize open research challenges and future directions, highlighting that modern networks demand monitoring frameworks that are not only scalable and real-time but also tightly integrated with network control and automation. Full article

Contemporary multi-UAV formations face dual challenges of obstacle avoidance and rapid formation recovery. To enable UAV swarms to efficiently restore their predefined configurations post-obstacle navigation, a formation recovery strategy grounded in Nash equilibrium game theory is proposed in this paper. By integrating the IVY optimization algorithm, a collaborative control model that systematically balances individual UAV interests with swarm-level objectives through carefully designed optimization criteria is established. Comparative experimental results demonstrate that, compared to traditional formation obstacle-avoidance algorithms, Improved Particle Swarm Optimization (IPSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA), our method exhibits superior performance across multiple key metrics, including average path length, formation accuracy rate, recovery time, and total time consumption. Real-flight tests on a multi-UAV platform confirm IVYA-Nash surpasses improved APF in formation accuracy and aerodynamic disturbance resistance, proving robustness in dynamic multi-agent scenarios. The work provides an efficient and reliable solution for coordinated control of UAV formations in complex environments. Full article

Uncertainty remains a central challenge in robotic navigation, exploration, and coordination. This paper examines how Partially Observable Markov Decision Processes (POMDPs) and their decentralized variants (Dec-POMDPs) provide a rigorous foundation for decision-making under partial observability across tasks such as Active Simultaneous Localization and Mapping (A-SLAM), adaptive informative path planning, and multi-robot coordination. We review recent advances that integrate deep reinforcement learning (DRL) with POMDP formulations, highlighting improvements in scalability and adaptability as well as unresolved challenges of robustness, interpretability, and sim-to-real transfer. To complement learning-driven methods, we discuss emerging strategies that embed probabilistic reasoning directly into navigation, including belief-space planning, distributionally robust control formulations, and probabilistic graph models such as enhanced probabilistic roadmaps (PRMs) and Canadian Traveler Problem-based roadmaps. These approaches collectively demonstrate that uncertainty can be managed more effectively by coupling structured inference with data-driven adaptation. The survey concludes by outlining future research directions, emphasizing hybrid learning–planning architectures, neuro-symbolic reasoning, and socially aware navigation frameworks as critical steps toward resilient, transparent, and human-centered autonomy. Full article

The increasing frequency of climate- and cyber-induced blackouts in modern distribution networks calls for restoration strategies that are both resilient and control-aware. Traditional black-start schemes, based on predefined energization sequences from synchronous machines, are inadequate for inverter-dominated grids characterized by high penetration of distributed energy resources and limited system inertia. This paper proposes a novel multi-layered black-start planning framework that explicitly incorporates the dynamic capabilities and operational constraints of grid-forming energy storage systems (GFESs). The approach formulates a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), jointly minimizing total restoration time, voltage–frequency deviations, and maximizing early-stage load recovery. A graph-theoretic partitioning module identifies restoration subgrids based on topological cohesion, critical load density, and GFES proximity, enabling localized energization and autonomous island formation. Restoration path planning is embedded as a mixed-integer constraint layer, enforcing synchronization stability, surge current thresholds, voltage drop limits, and dispatch-dependent GFES constraints such as SoC evolution and droop-based frequency support. The model is evaluated on a modified IEEE 123-bus system with five distributed GFES units under multiple blackout scenarios. Simulation results show that the proposed method achieves up to 31% faster restoration and 46% higher voltage compliance compared to MILP and heuristic baselines, while maintaining strict adherence to dynamic safety constraints. The framework yields a diverse Pareto frontier of feasible restoration strategies and provides actionable insights into the coordination of distributed grid-forming resources for decentralized black-start planning. These results demonstrate that control-aware, partition-driven optimization is essential for scalable, safe, and fast restoration in the next generation of resilient power systems. Full article

Robotic milling offers significant advantages for machining large aerospace components due to its low cost and high flexibility. However, compared to computerized numerical control (CNC) machine tools, robot systems exhibit lower stiffness, leading to force-induced deformation during milling process that significantly compromises path accuracy. This study proposed a dynamic robot posture programming method to enhance the stiffness for aluminum alloy milling task. Firstly, a milling force prediction model is established and validated under multiple postures and various milling parameters, confirming its stability and reliability. Secondly, a robot stiffness model is developed by combining system stiffness and milling forces within the milling coordinate system to formulate an optimization index representing stiffness performance in the actual load direction. Finally, considering the constraints of joint limit, singular position and joint motion smoothness and so on, the robot posture in the milling trajectory is dynamically programmed, and the joint angle sequence with the optimal average stiffness from any cutter location (CL) point to the end of the trajectory is obtained. Under the assumption that positioning errors were effectively compensated, the experimental results demonstrated that the proposed method can control both axial and radial machining errors within 0.1 mm at discrete points. For the specific milling trajectory, compared to the single-step optimization algorithm starting from the initial optimal posture, the proposed method reduced the axial error by 12.23% and the radial error by 8.61%. Full article

Formation control plays a vital role in coordinating multi-agent systems and swarm robotics, enabling collaboration in applications such as autonomous vehicles, robotic swarms, and distributed sensing. This paper introduces the formation-control problem, highlights its challenges, and compares centralized and decentralized schemes. We review recent advances and analyze popular algorithms, then propose a hybrid framework that combines leader–follower tracking with an artificial potential field (APF) safety layer. In three-UAV tests, the followers cross paths and one encounters a static obstacle. We run multiple simulations across scenarios with obstacles and varying formations. Results show the hybrid controller maintains the required formation while avoiding inter-agent collisions. Using quantitative metrics, we find the leader–follower baseline achieves the lowest formation error but has the most safety violations, whereas APF greatly improves safety at the cost of higher error. The hybrid combines these strengths—delivering APF-level safety with lower error and negligible runtime overhead—providing a practical balance between precise formation keeping and robust collision avoidance. Full article