Search Results (199)

In ethics-sensitive product development, Generative AI can improve the efficiency of concept generation, but it also raises challenges related to accountability, value alignment, and decision transparency. To address limitations in current human-AI co-design processes, including unclear allocation of decision-making authority, insufficiently structured translation from design requirements to design constraints, and limited explainability in scheme evaluation, this study proposes an explainable Human–Computer Interaction (HCI)-based decision support framework for human-AI co-design, termed GAGT. The framework integrates Generative AI with multi-criteria decision-making methods. Specifically, the Analytic Hierarchy Process (AHP) is used to structure design requirements and determine their priorities, Grey Relational Analysis (GRA) is used to compare candidate schemes, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to support transparent final ranking. Within the framework, human designers are mainly responsible for requirement confirmation, priority judgment, review at key checkpoints, and final scheme selection, while AI mainly supports information organization, candidate scheme generation, and quantitative comparison. The framework was applied to the design of a community medical vehicle through a small-sample, case-based, quasi-experimental study. Compared with the human-only condition, the GAGT-supported condition reduced design time by 56.1%. Compared with the AI-autonomous condition, it showed no observed HIPAA violations and a Value Drift Index of 16.1%, indicating better consistency with human-defined priorities. The results suggest that the proposed framework may improve design efficiency while supporting clearer human oversight and decision explainability in Generative AI-assisted design, and may provide a structured approach to organizing human and AI roles in ethics-sensitive design tasks. Full article

Search and rescue (SAR) missions in collapsed or underground environments remain challenging due to GPS unavailability, which hinders localization and autonomous navigation. Systems that rely on single-sensor inputs or structured settings often degrade under smoke, dust, or dynamic clutter. This paper presents an autonomous ground robot for GPS-denied SAR that integrates low-cost thermal, visual, inertial, and acoustic cues within a unified, computation-efficient architecture. The stack combines Thermal–Visual Odometry (TV–VO) with Zero-Velocity Updates (ZUPT) for drift-resistant localization, RescueGraph for multimodal survivor detection, and a Proximal Policy Optimization (PPO) planner for adaptive navigation under uncertainty. Across simulated disaster scenarios and benchmark corridor runs, the system shows embedded-feasible runtime behavior and supports return to base without external beacons under the evaluated conditions. Quantitatively, TV–VO+ZUPT reduces drift in short internal evaluations, while RescueGraph attains an F1-score of 0.6923 and an area under the ROC curve (AUC) of 0.976 for survivor detection. At the system level, the integrated navigation stack achieves full mission completion in the reported SAR-style trials, while the separate A*/PPO comparison highlights a trade-off between completion rate, traversal time, and collisions. Overall, the results support the practical promise of a low-cost sensor-fusion and learning-assisted navigation framework for GPS-denied SAR robotics. Full article

With the rapid development of underground spaces and demand for infrastructure-independent autonomous positioning in post-disaster rescue, Pedestrian Dead Reckoning (PDR) has become a key research focus. However, traditional PDR suffers from cumulative heading drift, inadequate 3D positioning performance, and poor anti-magnetic interference capabilities, failing to meet the high-precision positioning requirements of rescuers in underground and multistory buildings. To address these issues, this paper proposes an adaptive 3D-PDR method fusing inertial, geomagnetic, and barometric (3D-IMB-APDR). Sensor data are optimized via FFT dominant frequency extraction and Butterworth zero-phase filtering, with magnetic interference compensated by geomagnetic ellipse fitting. A segmental heading correction with a multi-criteria dynamic geomagnetic reliability model suppresses heading drift. A barometer-based coarse estimation and inertial fine correction architecture is adopted, where a lightweight CNN-BiLSTM network extracts inertial features for step height, and AEKF fuses multi-source data to achieve accurate vertical height estimation and precise 3D positioning. Validated in sports fields, underground parking garages, and staircases, the method outperforms four comparative methods, reducing positional RMSE by 65.77–98.23%, with endpoint errors of 1.40 m, 2.56 m, and 0.32 m, respectively. Relying solely on chest-worn sensors, it provides a reliable 3D autonomous positioning solution for rescuers in post-disaster rescue and underground engineering. Full article

Seamless localization across indoor and outdoor environments remains a fundamental challenge for wearable navigation systems, particularly those intended to assist visually impaired individuals. This challenge arises from the unreliability of GNSS signals in indoor and transitional spaces and the cumulative drift inherent to IMU–based dead reckoning. To address these limitations, this paper proposes a physics-informed GNSS–IMU sensor fusion framework that enables robust, real-time wearable navigation across heterogeneous environments. The proposed system dynamically adapts to environmental context, employing GNSS dominant localization in outdoor settings and PINN enhanced IMU-based dead reckoning during GNSS denied indoor operation. At the core of the framework is a tightly coupled Physics-Informed Neural Network (PINN) and Extended Kalman Filter (EKF), where the PINN embeds kinematic motion constraints to correct inertial drift and suppress sensor noise, while the EKF performs probabilistic state estimation and sensor fusion. The framework is implemented on a compact, energy-efficient wearable platform and evaluated using real-world indoor–outdoor pedestrian trajectories. Experimental results demonstrate improved localization accuracy, significantly reduced drift during indoor navigation, and stable indoor–outdoor transitions compared to conventional GNSS–IMU fusion methods. The proposed approach offers a practical and reliable solution for wearable assistive navigation and has broader applicability in smart mobility and autonomous wearable systems. Full article

Aiming at the divergence problem of standalone strapdown inertial navigation system (SINS) affected by initial errors, sensor drift, and cumulative errors in complex marine environments, this paper proposes a long-endurance autonomous navigation scheme without external measurement to suppress Schuler oscillations and improve dynamic navigation performance. First, based on the dynamic error model of SINS, the characteristics of Schuler oscillation are analyzed, and a multi-path delayed-solution strategy is developed. By sequentially delaying the SINS calculation loop and performing arithmetic averaging, periodic oscillation errors are automatically canceled. Second, a chi-square test is constructed to assess sea-state complexity in real time, and a robust adaptive Kalman filter is designed with adaptive filter selection to further improve estimation accuracy under dynamic conditions. Finally, the proposed method is systematically validated through static simulations, dynamic simulations, and full-scale ship experiments. Results show that the delayed-solution strategy significantly mitigates Schuler oscillation in attitude and velocity under static conditions. In dynamic simulations and ship trials, compared with pure SINS, single delayed-calculation, and conventional Kalman filter, the proposed approach achieves superior suppression of attitude, velocity, and position errors, with core navigation error indices reduced by at least one order of magnitude. These findings demonstrate that the Schuler period characteristic of inertial navigation errors can be effectively exploited in dynamic conditions, and the coupling of multi-path delayed calculation with robust adaptive filtering enables substantial improvements in autonomous navigation accuracy without external measurement. The proposed method expands the theoretical and engineering framework of autonomous navigation at no additional hardware cost, providing a new technical route for the practical deployment of long-duration SINS. Full article

Existing autonomous navigation systems for Unmanned Aerial Vehicles (UAVs) face the dual challenges of local minima entrapment and computational complexity that scales with environmental density. This paper proposes a hierarchical navigation architecture integrating deep representation learning with an improved Vortex Artificial Potential Field (APF). At the decision layer, a Convolutional Neural Network (CNN) encodes the environment as a fixed-dimensional tensor and generates global waypoints with constant-time inference, independent of obstacle count. At the control layer, a Vortex APF resolves the Goal Non-Reachable with Obstacles Nearby (GNRON) problem and limit-cycle oscillations through tangential rotational potentials, achieving significant improvement in trajectory smoothness compared to traditional APF methods. A closed-loop replanning mechanism further ensures robust performance under execution drift. Experiments across varying obstacle densities demonstrate that the combined system achieves high navigation success rates in dense environments with substantially reduced computation time compared to sampling-based planners such as Rapidly exploring Random Tree star (RRT*), while maintaining superior trajectory quality. This architecture provides a computationally efficient solution for resource-constrained UAV platforms operating in GPS-denied or obstacle-rich environments such as warehouses, forests, and disaster sites. Full article

Autonomous vehicles at level 3 and above must maintain high navigation accuracy, particularly in global navigation satellite system (GNSS)-denied environments. The main innovations of this work are threefold. First, we integrate visual inertial odometry (VIO) and light detection and ranging (LiDAR) inertial odometry (LIO) as external updates to mitigate the rapid drift of micro-electromechanical system (MEMS)-based industrial-grade inertial measurement units (IMUs) during long-term GNSS outages. Second, we adopt a redundant IMU (RIMU) approach that fuses multiple low-cost IMUs to reduce sensor noise and improve reliability. Third, we propose a system calibration methodology using both static and dynamic vehicle motion to estimate extrinsic parameters (boresight angles and lever arms) of the sensors, achieving an overall boresight angle root-mean-square error of 0.04 degrees in the simulation. Experiments were conducted under a 7 min GNSS-denied scenario in an underground parking lot, allowing for comparison of the error characteristics of multi-sensor fusion schemes against a navigation-grade reference. The INS/GNSS/LIO framework achieved a two-dimensional root-mean-square position error of 1.22 m (95% position error within 2.5 m), meeting the lane-level (1.5 m) accuracy requirement under a GNSS outage exceeding 7 min without prior maps. In contrast, the RINS/GNSS/VIO framework yielded a 4.71 m 2D mean position error under the same conditions. This paper provides a quantitative comparison of the baseline error characteristics of VIO-, LIO-, and RIMU-assisted INS/GNSS fusion under a GNSS-denied navigation scenario. Full article

How does the complex, adaptive, and autonomous organization of life emerge from the laws of physics and information? This review argues that the answer lies in a convergent set of universal organizational principles that constitute a physical and informational grammar of the living. Living systems are dissipative structures that achieve organizational closure—materially and energetically open, yet causally closed—thereby attaining genuine autonomy and agency. Their architecture exhibits fractal and modular scaling laws that maximize energy flow, robustness, and evolvability under universal physical constraints. Critically, organisms operate at critical transitions—zones of controlled instability where fluctuations amplify information processing, transforming noise into adaptive signal. This self-organized criticality enables functional degeneracy, relational redundancy, and evolutionary antifragility. Cognition emerges as a distributed process of active inference, operating through a predictive–corrective cycle that integrates perception, action, and learning under the Free Energy Principle. From molecular networks to ecosystems, the same physico-informational grammars unfold recursively, revealing a deep organizational holography: the principles of organization are replicated across scales. Evolution under the Law of Increasing Functional Information is not random drift, but a directional expansion of functional complexity—a thermodynamic gradient towards greater agency. This synthesis challenges biological exceptionalism: the trajectory from thermodynamics to cognition is continuous, physically constrained, and potentially inevitable. Life does not violate physical laws—it fulfills them in regimes of high informational complexity, instantiating fundamental principles in self-organized architectures capable of prediction, memory, and purpose. The objective of this work is to articulate how the synthesis of these principles not only unifies physics and biology, but also illuminates the profound continuity between thermodynamics, chemistry, informational constraints, organization, and the mind. Full article

In recent years, the large-scale deployment of Low Earth Orbit (LEO) constellations has made autonomous time synchronization and reference maintenance within constellations a critical enabling technology. Achieving high-precision synchronization with low cost and low power consumption, without relying on onboard atomic clocks or Global Navigation Satellite System (GNSS) signals, remains a significant challenge. This paper proposes an autonomous time synchronization method for LEO constellations that relies solely on high-stability crystal oscillators as local oscillators. By leveraging satellite-to-ground and inter-satellite measurement links, the proposed approach enables constellation-wide time synchronization without external timing references. A satellite-to-ground link visibility time model is established based on orbital parameters and ground station visibility geometry. On this basis, a discrete state-space model is constructed, incorporating temperature-induced frequency perturbation compensation, frequency offset estimation, and control voltage regulation. A combined Kalman filtering and Linear Quadratic Regulator (LQR) control framework is employed to achieve precise time offset synchronization and long-term maintenance. Experimental results demonstrate that, under a Walker-Delta constellation configuration with an orbital altitude of 800 km and an inclination of 55°, the proposed method introduces a time synchronization performance better than 5 ns (1$\sigma$), with a peak-to-peak error below 30 ns. This level of performance satisfies the timing requirements of typical LEO constellation applications, including communication scheduling, high-rate modulation, and critical infrastructure timing services. Moreover, the proposed scheme supports decentralized deployment and provides local physical time signal outputs, making it well suited for large-scale satellite networks requiring high-precision autonomous time synchronization. Full article

High-precision localization is essential for autonomous navigation and environment perception of unmanned aerial vehicles (UAVs) in complex power inspection scenarios. To overcome the limited accuracy and accumulated drift of conventional GPS-based single-sensor localization, this paper proposes a LiDAR–IMU–GPS-aided navigation method that combines a tightly coupled front-end and a loosely coupled back-end. The front-end employs an improved Lie-group-based UKF-SLAM framework to explicitly handle the nonlinearities of rotational motion, thereby improving the stability of local pose estimation. The back-end integrates GPS absolute constraints, loop closure detection, and point cloud registration via pose graph optimization, which effectively suppresses long-term accumulated drift. The framework achieves accurate and robust localization for UAV power inspection. Experiments on public benchmark datasets and real-world power inspection scenarios demonstrate the effectiveness of the proposed method. On the MH_02_easy sequence, the absolute trajectory error is reduced from 0.521 m to 0.170 m compared with ROVIO, while in a real inspection sequence the cumulative error is reduced by more than 99% after back-end optimization. Moreover, the system maintains stable navigation under GPS-degraded conditions, indicating strong robustness and practical applicability. Full article

Polarisation encoding is widely used in fibre-based Quantum Key Distribution (QKD), but random birefringence in optical fibres causes the transmitted states to drift, requiring active compensation at the receiver. Electronic Polarisation Controllers (EPCs) are commonly used for this purpose, yet the relationship between their control voltages and the resulting polarisation transformation is highly nonlinear and difficult to model. While optimisation algorithms are frequently employed to align and stabilise polarisation states, their comparative performance has not been systematically studied in realistic QKD settings. In this work, we benchmark four optimisation algorithms for electronic polarisation control, using both a numerical model and a 50 km fibre-based experimental setup. We evaluate each algorithm in terms of convergence time, failure rate, and stability, under both initial alignment and continuous drift compensation scenarios. Coordinate Descent achieved the fastest average alignment time (2.1 ms in simulation; 34.6 s experimentally), while Simulated Annealing delivered perfect reliability. We further propose a hybrid control strategy that combines fast initial alignment with high-reliability realignment. This approach was validated over a continuous 2 h QKD simulation with real fibre drift, demonstrating robust polarisation control without manual intervention. Our results provide guidance for algorithm selection in practical QKD deployments and suggest a pathway to resilient, autonomous polarisation tracking in long-distance quantum networks. Full article

Autonomous vehicle path-tracking and lateral stability depend critically on reliable steering actuator operation. However, steering systems are susceptible to bias faults from mechanical misalignment, friction, drivetrain asymmetry, and degradation. These faults distort commanded versus actual steering inputs, causing accumulated lateral and heading errors during high-speed driving. Actuator biases manifest as constant offsets, gradual drift, or intermittent activations, which complicate reliable diagnosis. This study presents an adaptive sliding mode observer-based active fault-tolerant control framework for real-time detection, estimation, and mitigation. An extended four-state lateral error model incorporating distance and heading errors captures the influence of steering bias on vehicle behavior and stability. Adaptive observer gain tuning addresses modeling uncertainties arising from speed variations, linearization residuals, and tire stiffness changes to ensure robust estimation under realistic driving conditions. The effectiveness of the proposed method is validated through high-speed double lane change simulations considering three representative bias scenarios: an initial constant bias, a gradually increasing drift bias, and an intermittent bias. Results demonstrate reliable bias estimation and significantly improved path-tracking accuracy compared to uncompensated cases. Operating without additional sensors, hardware redundancies, or controller switching, the framework is suitable for practical implementation in autonomous vehicle steering systems. Full article

Autonomous mobile robots require robust localization in complex and dynamic environments, where single-sensor solutions often fail due to accumulated drift or signal degradation. LiDAR–inertial odometry provides accurate short-term motion estimation, but suffers from long-term error accumulation, whereas RTK-GNSS offers absolute positioning that becomes unreliable under occlusion or multipath effects. To solve the above problems, this paper proposes an adaptive multi-sensor fusion positioning framework that dynamically fuses LiDAR, IMU, and RTK-GNSS data based on the real-time quality evaluation of sensors. The system uses the front-end tightly coupled LiDAR–IMU iterative extension Kalman filter (IEKF) as the core estimator and combines loop detection with incremental factor graph optimization to suppress long-term drift. In addition, a degradation detection method based on the minimum eigenvalue of the Jacobian matrix is proposed to identify unreliable matching constraints in real time. In order to avoid abrupt changes in positioning results caused by fluctuations in sensor data quality, the system adopts a smooth fusion strategy based on covariance weighting. Experiments on the KITTI benchmark and self-collected datasets demonstrate that the proposed method significantly improves localization accuracy and robustness compared with pure LiDAR-based approaches, achieving stable centimeter-level performance while maintaining real-time capability on embedded platforms. Full article

Simultaneous Localization and Mapping (SLAM) is essential for autonomous movement in intelligent robotic systems. Traditional SLAM using a single sensor, such as an Inertial Measurement Unit (IMU), faces challenges including noise and drift. This paper introduces a novel Cartographer-based SLAM approach for DAMR trajectory generation in indoor environments to reduce drift errors and improve localization accuracy. This SLAM approach integrates multi-sensor data with extended Kalman filter (EKF) fusion from wheel odometry, an RGB-D camera (RTAB-Map), and an IMU for precise mapping with DAMR trajectory generation and is compared with the heading reference trajectory generated by robot pose estimation and frame transformation. This system is implemented in the Robot Operating System (ROS 2) for coordinated data acquisition, processing, and visualization. After experimental verification, the DAMR trajectories generated are closer to the reference trajectory and drift errors are tuned. The experimental results revealed that the DAMR trajectory with multi-sensor data integration using the EKF effectively improved the positioning accuracy and robustness of the system. The proposed approach shows improved alignment with the reference trajectory, yielding a mean displacement error of 0.352% and an absolute trajectory error of 0.007 m, highlighting the effectiveness of the fusion approach for accurate indoor robot navigation. Full article

The absolute positioning accuracy of industrial manipulators is frequently bottlenecked by the interplay of geometric tolerances and complex, unmodeled non-geometric parameter drifts. Traditional static kinematic models, predicated on rigid-body assumptions, often struggle to characterize these state-dependent dynamic behaviors. To bridge this gap, this study introduces a PSO-Driven Neuro-Symbolic State-Space Framework incorporating Adaptive Wavelet Networks, drawing inspiration from two biological principles: the collective swarm intelligence observed in bird flocking and fish schooling, and the localized receptive field structure of mammalian visual cortex neurons. By reformulating calibration as a latent state estimation problem, we model kinematic parameters as stochastic states. Crucially, the observation model fuses symbolic Denavit–Hartenberg (D–H) predictions with an Adaptive Wavelet Network (AWNN). The AWNN utilizes Mexican Hat kernels, whose morphology mirrors the center-surround antagonism of cortical receptive fields, and leverages their precise time–frequency localization to effectively learn complex, configuration-dependent residuals. The framework employs a robust decoupled strategy. First, Particle Swarm Optimization (PSO) executes meta-optimization to autonomously determine hyperparameters, thereby mitigating initialization sensitivity. Second, a recursive inference engine estimates the hybrid states. Third, a global batch optimization refines the symbolic parameters against a frozen non-geometric error field. Experimental validation on an ABB IRB 120 robot (400 datasets) yielded a test RMSE of 0.73 mm. Compared to the standard Levenberg–Marquardt method, our approach reduced the RMSE by 40.16% and the maximum error by 35.71% (down to 0.99 mm). Moreover, it outperforms the state-of-the-art RPSO-DCFNN baseline by 12.05% while maintaining high computational efficiency (convergence within 20.15 s). These findings underscore the superiority of the proposed bio-inspired state-space fusion strategy for high-precision industrial applications. Full article