Search Results (414)

This paper addresses the critical challenge of energy management for autonomous robots in the context of large-scale photovoltaic parks. The dynamic and vast nature of these environments, characterized by dense, structured rows of solar panels, introduces unique complexities, including uneven terrain, varied operational demands, and the need for equitable resource allocation among diverse robot fleets. The presented framework adapts and significantly extends the Affinity Propagation algorithm for strategic charging station placement within photovoltaic parks. The key contributions include: (1) a multi-attribute grid-based environment model that quantifies terrain difficulty and panel-specific obstacles; (2) an extended multi-factor scoring function that incorporates penalties for terrain inaccessibility and proximity to sensitive photovoltaic infrastructure; (3) a sophisticated, energy-aware consumption model that accounts for terrain friction, slope, and rolling resistance; and (4) a novel multi-agent fairness constraint that ensures equitable access to charging resources across heterogeneous robot sub-fleets. Through extensive simulations on synthesized photovoltaic park environments, it is demonstrated that the enhanced algorithm not only significantly reduces travel distance and energy consumption but also promotes a fairer, more efficient operational ecosystem, paving the way for scalable and sustainable robotic maintenance and inspection. Full article

This study presents the design, modeling, and multi-platform simulation of NEPTUNE-R, a solar-powered autonomous hydro-robot developed for sustainable water purification and oxygenation. Mechanical design was performed in Fusion 360, trajectory optimization in MATLAB R2024a, and dynamic motion analysis in Roblox Studio, creating a reproducible digital twin environment. The proposed path-planning strategies—Boustrophedon and Archimedean spiral—achieved full surface coverage across various lake geometries, with an average efficiency of 97.4% ± 1.2% and a 12% reduction in energy consumption compared to conventional linear patterns. The integrated Euler-based force model ensured stability and maneuverability under ideal hydrodynamic conditions. The modular architecture of NEPTUNE-R enables scalable implementation of photovoltaic panels and microbubble-based oxygenation systems. The results confirm the feasibility of an accessible, zero-emission platform for aquatic ecosystem restoration and contribute directly to Sustainable Development Goals (SDGs) 6, 7, and 14 by promoting clean water, renewable energy, and life below water. Future work will involve prototype testing and experimental calibration to validate the numerical findings under real environmental conditions. Full article

In this paper, a hybrid locomotion approach is proposed and experimentally validated for a quadrupedal robot to enhance energy efficiency on mixed terrains. A mechanical solution was implemented by adding passive wheels on the robot’s abdomen, to allow for gliding on flat portions of the faced terrains. This strategy aims to reduce the use of the legs, decreasing the overall energy consumption. To allow an efficient use of simulations, a data-driven approach was developed to estimate motor power consumption from joint dynamics on the real robot and subsequently applied within the simulation environment. The neural network achieved a coefficient of determination of R² = 0.97, ensuring accurate estimation of energy consumption under both simulated and real conditions. Experimental and simulated results show that the proposed sliding gait reduces the average Cost of Transport from approximately 4.5–6.0 during trotting to 0.8–1.1 during sliding, corresponding to a four–five-fold improvement in energy efficiency. Overall, the results demonstrate that a simple mechanical upgrade of the robot’s body structure can significantly enhance locomotion efficiency and versatility on flat or slightly descending terrains. Full article

Exoskeleton robots have been widely applied in military, industrial, and rehabilitation fields, with their practical effectiveness substantially reliant on a comprehensive performance evaluation framework. This paper reviews the prevalent testing methods for exoskeleton robots, including electromyography (EMG), motion capture, human–machine interaction forces, energy consumption monitoring, and both subjective and objective assessments. Through the systematic integration and comparison of these methodologies, this study establishes a methodological foundation for the comprehensive evaluation of performance and provides a theoretical basis for the development of standardized evaluation frameworks in the future. Furthermore, by systematically comparing and integrating these methodologies, this study aims to establish a methodological foundation for the future development of a standardized, multi-dimensional evaluation framework, which is essential for translating exoskeleton technology from laboratory research to practical applications. Full article

In reinforcement learning (RL), reward function design is critical to the learning efficiency and final performance of agents. However, in complex tasks such as humanoid motion tracking, traditional static weighted reward functions struggle to adapt to shifting learning priorities across training stages, and designing a suitable shaping reward is problematic. To address these challenges, this paper proposes a two-layered reward reinforcement learning framework. The framework decomposes the reward into two layers: an upper-level goal reward that measures task completion, and a lower-level optimizing reward that includes auxiliary objectives such as stability, energy consumption, and motion smoothness. The key innovation lies in the online optimization of the lower-level reward weights via an online meta-heuristic optimization algorithm. This online adaptivity enables goal-conditioned reward shaping, allowing the reward structure to evolve autonomously without requiring expert demonstrations, thereby improving learning robustness and interpretability. The framework is tested on a gymnastic motion tracking problem for the Unitree G1 humanoid robot in the Isaac Gym simulation environment. The experimental results show that, compared to a static reward baseline, the proposed framework achieves 7.58% and 10.30% improvements in upper-body and lower-body link tracking accuracy, respectively. The resulting motions also exhibit better synchronization and reduced latency. The simulation results demonstrate the effectiveness of the framework in promoting efficient exploration, accelerating convergence, and enhancing motion imitation quality. Full article

Autonomous navigation in GPS-denied, unstructured environments such as murky waters or complex seabeds remains a formidable challenge for robotic systems, primarily due to sensory degradation and the computational inefficiency of conventional algorithms. Drawing inspiration from the robust navigation strategies of marine species such as the sea turtle’s quantum-assisted magnetoreception, the octopus’s tactile-chemotactic integration, and the jellyfish’s energy-efficient flow sensing this study introduces a novel neuromorphic framework for resilient robotic navigation, fundamentally based on the co-design of marine-inspired sensors and event-based neuromorphic processors. Current systems lack the dynamic, context-aware multisensory fusion observed in these animals, leading to heightened susceptibility to sensor failures and environmental perturbations, as well as high power consumption. This work directly bridges this gap. Our primary contribution is a hybrid sensor fusion model that co-designs advanced sensing replicating the distributed neural processing of cephalopods and the quantum coherence mechanisms of migratory marine fauna with a neuromorphic processing backbone. Enabling real-time, energy-efficient path integration and cognitive mapping without reliance on traditional methods. This proposed framework has the potential to significantly enhance navigational robustness by overcoming the limitations of state-of-the-art solutions. The findings suggest the potential of marine bio-inspired design for advancing autonomous systems in critical applications such as deep-sea exploration, environmental monitoring, and underwater infrastructure inspection. Full article

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based simulated environments demonstrated that when benchmarked against the classical MARL baseline, QI-MARL achieved an 8% improvement in path efficiency, a 12% increase in task success rate, and a 15% reduction in energy consumption. When compared with the rule-based approach, improvements reached 15%, 20%, and 26%, respectively. Ablation studies further confirmed the substantial contributions of the quantum-inspired exploration and energy-sharing mechanisms, while sensitivity and scalability analyses validated the system’s robustness across varying swarm sizes and environmental complexities. The proposed framework effectively integrates quantum-inspired AI, intelligent microgrid management, and autonomous robotics, offering a novel approach to energy coordination in cyber-physical systems. Potential applications include smart buildings, industrial campuses, and distributed renewable energy networks, where the system enables flexible, resilient, and energy-efficient robotic operations within modern electrical engineering contexts. Full article

Robot manipulators are suitable for many industrial tasks, such as assembly and pick-and-place operations. However, high-acceleration motions result in shaking forces and moments to the base, which can cause vibration of the manipulator and instability in the case of a mobile base. Furthermore, gravity compensation of the manipulator links requires additional motor torque, which can increase energy consumption. Balanced manipulators address these problems by employing a mechanical design that results in the balancing of gravity and other static forces, or the removal of shaking forces and/or moments. This review paper provides an overview of mechanical design approaches for balanced robotic manipulation, with an emphasis on experimentally prototyped designs. We first define the types of balancing according to the literature. We then provide an overview of different approaches to the mechanical design of balanced manipulators, along with simple examples of their implementation. Experimental prototypes in this field are then comprehensively presented and summarized to allow readers to compare their development maturity. At the end of the paper, we outline challenges and future directions of research. Full article

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions in power consumption while preserving navigation reliability. The proposed system is implemented on a low-power microcontroller and evaluated through simulations and prototype testing in constrained channel environments. Results show a 35% reduction in energy consumption with minimal impact on navigation performance, demonstrating the framework’s effectiveness for long-duration autonomous operations in pipelines, sewers, and industrial ducts. Full article

The deployment of autonomous robots is critical for advancing sustainable agriculture, but their effectiveness hinges on visual perception systems that can reliably operate in natural, real-world environments. Selecting an appropriate vision model for these robots requires a practical evaluation that extends beyond standard accuracy metrics to include critical deployment factors such as computational efficiency, energy consumption, and robustness to environmental disturbances. To address this need, this study presents a deployment-oriented benchmark of state-of-the-art You Look Only Once (YOLO)-based models for orange detection and segmentation. Following a systematic process, the selected models were evaluated on a unified public dataset, annotated to rigorously assess real-world challenges. Performance was compared across five key dimensions: (i) identification accurac, (ii) robustness, (iii) model complexity, (iv) execution time, and (v) energy consump-tion. The results show that the YOLOv5 variants achieved the most accurate detection and segmentation. Notably, YOLO11-based models demonstrated strong and consistent results under all disturbance levels, highlighting their robustness. Lightweight architectures proved well-suited for resource-constrained operations. Interestingly, custom models did not consistently outperform their baselines, while nanoscale models showed demonstra-ble potential for meeting real-time and energy-efficient requirements. These findings offer valuable, evidence-based guidelines for the vision systems of precision agriculture robots. Full article

Deep-sea mining has gradually emerged as a core domain in global resource exploitation. Underwater autonomous robots, characterized by low cost, high flexibility, and lightweight properties, demonstrate significant advantages in deep-sea mineral development. To address the limitations of traditional deep-sea mining equipment, such as large volume, high energy consumption, and insufficient flexibility, this paper proposes an innovative Underwater Vehicle Collector System (UVCS). Integrating bionic design with autonomous robotic technology, this system features a collection device mimicking the large opening–closing kinematics of the mouth of deep-sea dragonfish (Stomiidae). A dual-rocker mechanism is employed to realize the mouth opening-closing function, and the collection process is driven by the pitching motion of the vehicle without the need for additional motors, thus achieving the advantages of high flexibility, low energy consumption, and light weight. The system is capable of collecting seabed polymetallic nodules with diameters ranging from 1 to 12 cm, thus providing a new solution for sustainable deep-sea mining. Based on the dynamics of UVCS, this paper verifies its attitude stability and collection efficiency in planar motions through single-cycle and multi-cycle simulation analyses. The simulation results indicate that the system operates stably with reliable collection actions. Furthermore, water tank testings demonstrate the opening and closing functions of the UVCS collection device, fully confirming its design feasibility and application potential. In conclusion, the UVCS system, through the integration of bionic design, opens up a new path for practical applications in deep-sea resource exploitation. Full article

The rapid growth of robotic warehouses and smart logistics has increased the demand for efficient scheduling of multi-tier shuttle systems (MTSSs). MTSS scheduling is a complex robotic task allocation problem, where throughput, energy efficiency, and service quality must be jointly optimized under operational constraints. To address this challenge, this study proposes a hybrid optimization framework that integrates the Minimum-Cost Maximum-Flow (MCMF) algorithm with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The MTSS is modeled as a cyber–physical robotic system that explicitly incorporates task flow, energy flow, and information flow. The lower-layer MCMF ensures efficient and feasible task–robot assignments under state-of-charge (SOC) and deadline constraints, while the upper-layer NSGA-II adaptively tunes cost-function weights to explore Pareto-optimal trade-offs among makespan, energy consumption, and waiting time. Simulation results show that the hybrid framework outperforms baseline heuristics and static optimization methods and reduces makespan by up to 5%, the energy consumption by 2.8%, and the SOC violations by over 90% while generating diverse Pareto fronts that enable flexible throughput-oriented, service-oriented, or energy-conservative scheduling strategies. The proposed framework thus provides a practical and scalable solution for energy-aware robotic scheduling in automated warehouses, thus bridging the gap between exact assignment methods and adaptive multi-objective optimization approaches. Full article

The encapsulation of pressure-sensitive electronic components plays a critical role in ensuring product reliability; however, the current process remains highly dependent on manual operations, leading to low efficiency and harsh working conditions. To address these limitations, this study investigates the layout optimization of a robotic encapsulation production line for the WL11 line of Company X, where peripheral equipment is fixed while the robot base is movable. A bi-objective optimization model was formulated to simultaneously minimize operation time and motion energy consumption. The motion energy index was derived from a complete robot dynamics model augmented with a frictional energy term, while the operation time was modeled using the maximum runtime of the robot’s first three joints. To solve this constrained optimization problem, an improved NSGA-II algorithm was developed with real-coded chromosome representation, constraint-violation handling, and customized genetic operators to ensure engineering feasibility. Experimental results demonstrate that the proposed method achieves 14.81% and 25.63% reductions in operation time and motion energy consumption, respectively, compared with the initial layout. This work provides a practical and generalizable framework for production line layout optimization under complex industrial constraints and offers valuable guidance for the intelligent upgrading of electronic component manufacturing. Full article

Robotisation is playing an increasingly important role in economic and technological life today. Industrial robotisation has a significant impact on the efficiency and productivity of manufacturing companies, and service robots are becoming more and more common in everyday life. The main objective of our research is to examine the impact of robotisation on energy consumption and sustainability, as well as the technological and corporate challenges facing the integration of robots. The research is based on a literature review, which we supplemented with a bibliographic analysis. In terms of methods, we relied on the Global Citation Score, Co-Coupling Network Analysis, and Burst Analysis. Our results suggest that research on industrial robotisation can be divided into complementary dimensions, ranging from engineering-level trajectory optimization and subsystem design to system-level modeling, macroeconomic sustainability analysis, and data-driven optimization. The findings highlight that the positive impacts of robotisation on both energy efficiency and carbon reduction can be maximized when these approaches are integrated into a systemic framework that connects micro- and macro-level perspectives. Full article

Due to their unique advantages—such as small size, easy integration, flexible wearability, low power consumption, high sensitivity, and material designability—organic field-effect transistor (OFET) gas sensors have significant application potential in fields such as environmental detection, smart healthcare, robotics, and artificial intelligence. Benzothiadiazole fused tetrathiafulvalenes (TTF) are promising organic semiconductor candidates due to their abundant S atoms and planar π-π conjugation skeletons. We designed a series of derivatives by side-chain modification, and conducted systematic computations on TTF derivatives, including reported and newly designed materials, to analyze how geometric factors affect the charge transport properties of materials at the PBE0/6-311G(d,p) level. The frontier molecular orbitals (FMOs) and reorganization energy indicate that the designed derivatives are promising candidates for organic semiconductor sensing materials. Furthermore, theoretical calculations reveal that the designed TTF derivatives are sensitive to gases like NH₃, H₂S, and SO₂, indicating organic field-effect transistors (OFETs) with gas-sensing functions. Full article