Search Results (240)

This paper presents the development of a robot-oriented Distributed Acoustic Sensing (DAS) system designed for environmental monitoring and hazard detection on ground robotic platforms. Unlike conventional DAS solutions primarily intended for stationary or quasi-stationary infrastructures, the proposed approach explicitly accounts for robot-induced mechanical vibrations, mobility constraints, and limited onboard resources. A dedicated anti-jitter signal processing pipeline combined with edge-based data processing is introduced to suppress motion-induced strain components while preserving weak external acoustic signals. The system integrates optical fiber deployment along the robot structure using flexible guides and vibration-isolated clamps, ensuring stable mechanical coupling under continuous motion. Experimental validation, including laboratory tests and preliminary outdoor field trials, demonstrates reliable detection of acoustic events in the 10–200 Hz frequency range, with reduced processing latency of 80–100 ms and a detection reliability of up to 95%. Comparative analysis with conventional sensors confirms the advantages of the proposed DAS-based approach in terms of sensitivity, spatial coverage, and robustness. The results demonstrate the feasibility and effectiveness of DAS technology for real-time sensing applications on mobile robotic platforms. Full article

As robotic platforms have become more capable, the need for improved power efficiency has grown due to increased applications and computational loads. Several methods and controllers are available in various types of robotics that can achieve increased power efficiency. This paper reviews intelligent power management methods and energy-efficient controls in untethered battery-powered robotics including dynamic power management (DPM), dynamic voltage and frequency scaling (DVFS), AI-assisted adaptive dynamic programming (DP) control systems, AI-assisted model predictive control (MPC) systems, and hybrid energy storage system (HESS) hardware well suited for multi-objective AI integration. Robotic neural networks and AI-enhancement are identified as promising directions for advanced research. However, the need to improve training power efficiency calls for further research if these AI-enhancement systems are to be integrated onboard robotic platforms. This paper provides the background and case study implementation of robotic power efficiency methods across various scales of development to illustrate the current capabilities of robotic platforms. Efficiency improvements are quantified and opportunities for advancements are presented, as well as key findings reached through this in-depth review. Full article

Future in-orbit servicing missions, such as spacecraft capture, repair, and assembly, demand robotic systems capable of autonomously computing dynamically feasible, constrained trajectories in real time. Sequential Convex Programming (SCP) has emerged as an effective method for online trajectory optimization in these resource-constrained settings, addressing nonconvex problems through iterative refinement while maintaining the formal guarantees essential for safety-critical applications. While emerging machine learning (ML) methods offer potential enhancements to trajectory generation, they often lack these rigorous guarantees. To address this, we propose a hybrid trajectory optimization framework for robotic servicers, using autoregressive trajectory-generator networks to produce high-quality initial guesses and warm-start an SCP module, enabling the system to produce optimal trajectories quickly and reliably. A key advantage of this approach is the elimination of inverse-kinematics optimization for redundant manipulators during both guess generation and subsequent refinement. By conditioning on exogenous inputs shared with the SCP solver, the networks are inherently task- and obstacle-aware, yielding a tightly integrated architecture that minimizes on-board computational requirements. Results demonstrate that this network-based warm-starting strategy substantially accelerates trajectory generation, reducing both SCP computational time and iterations, while preserving the theoretical guarantees of convex optimization. Full article

Accurate trajectory tracking remains a central challenge in differential-drive mobile robots (DDMRs), particularly when operating under real-world conditions. Model Predictive Control (MPC) provides a powerful framework for this task, but its performance degrades when the robot deviates significantly from the nominal path. To address this limitation, robust recovery mechanisms are required to ensure stable and precise tracking. This work presents an experimental validation of an MPC controller applied to a four-wheel DDMR, whose odometry is corrected by a SLAM algorithm running in ROS 2. The MPC is formulated as a quadratic program with state and input constraints on linear (v) and angular ($\omega$) velocities, using a prediction horizon of $N_p=15$ future states, adjusted to the computational resources of the onboard computer. A novel dynamic look-ahead re-entry strategy is proposed, which activates when the robot exits a predefined lateral error band ($\delta =0.05$ m) and interpolates a smooth reconnection trajectory based on a forward look-ahead point, ensuring gradual convergence and avoiding abrupt re-entry actions. Accuracy was evaluated through lateral and heading errors measured via geometric projection onto the nominal path, ensuring fair comparison. From these errors, RMSE, MAE, $P_{95}$, and in-band percentage were computed as quantitative metrics. The framework was tested on real hardware at 50 Hz through 5 nominal experiments and 3 perturbed experiments. Perturbations consisted of externally imposed velocity commands at specific points along the path, while configuration parameters were systematically varied across trials, including the weight R, smoothing distance $L_{\mathrm{smooth}}$, and activation of the re-entry strategy. In nominal conditions, the best configuration (ID 2) achieved a lateral RMSE of 0.05 m, a heading RMSE of 0.06 rad, and maintained 68.8% of the trajectory within the validation band. Under perturbations, the proposed strategy substantially improved robustness. For instance, in experiment ID 6 the robot sustained a lateral RMSE of 0.12 m and preserved 51.4% in-band, outperforming MPC without re-entry, which suffered from larger deviations and slower recoveries. The results confirm that integrating MPC with the proposed re-entry strategy enhances both accuracy and robustness in DDMR trajectory tracking. By combining predictive control with a spatially grounded recovery mechanism, the approach ensures consistent performance in challenging scenarios, underscoring its relevance for reliable mobile robot navigation in uncertain environments. Full article

This article presents the design, fabrication, and experimental validation of a centimeter-scale autonomous robot that achieves bidirectional locomotion and trajectory control through 3D-printed resonators actuated by piezoelectricity and integrated with miniature legs. Building on previous works that employed piezoelectric bimorphs, the proposed system replaces them with custom-designed 3D-printed resonant plates that exploit the excitation of standing waves (SW) to generate motion. Each resonator is equipped with strategically positioned passive legs that convert vibratory energy into effective thrust, enabling both linear and rotational movement. A differential drive configuration, implemented through two independently actuated resonators, allows precise guidance and the execution of complex trajectories. The robot integrates onboard control electronics consisting of a microcontroller and inertial sensors, which enable closed-loop trajectory correction via a PD controller and allow autonomous navigation. The experimental results demonstrate high-precision motion control, achieving linear displacement speeds of 8.87 mm/s and a maximum angular velocity of 37.88°/s, while maintaining low power consumption and a compact form factor. Furthermore, the evaluation using the mean absolute error (MAE) yielded a value of 0.83° in trajectory tracking. This work advances the field of robotics and automatic control at the insect scale by integrating efficient piezoelectric actuation, additive manufacturing, and embedded sensing into a single autonomous platform capable of agile and programmable locomotion. Full article

Unmanned Aerial Vehicles (UAVs) have become integral to modern applications, including smart agricultural robotics, where reliability is essential to ensure safe and efficient operation. It is commonly recognized that traditional fault diagnosis approaches usually rely on vibration and noise measurements acquired via onboard sensors or similar methods, which typically require continuous data acquisition and non-negligible onboard computational resources. This study presents a portable Laser Doppler Vibrometer (LDV)-based system designed for noncontact, offboard, and high-sensitivity measurement of UAV vibration signatures. The LDV measurements are analyzed using a Deep Extreme Learning-based Neural Network (DeepELM-DNN) capable of identifying both propeller fault type and severity from a single 1 s measurement. Experimental validation on a commercial quadcopter using 50 datasets across multiple induced fault types and severity levels demonstrates a classification accuracy of 97.9%. Compared to conventional onboard sensor-based approaches, the proposed framework shows strong potential for reduced computational effort while maintaining high diagnostic accuracy, owing to its short measurement duration and closed-form learning structure. The proposed LDV setup and DeepELM-DNN framework enable noncontact fault inspection while minimizing or eliminating the need for additional onboard sensing hardware. This approach offers a practical and scalable diagnostic solution for large UAV fleets and next-generation smart agricultural and industrial aerial robotics. Full article

Real-time path planning for autonomous Unmanned Aerial Vehicles (UAVs) under strict hardware limitations remains a central challenge in embedded robotics. This study presents a refined Rapidly-Exploring Random Tree (RRT) algorithm implemented within an onboard embedded system based on a 32-bit STM32 microcontroller, demonstrating that real-time autonomous navigation can be achieved under low-power computation constraints. The proposed framework integrates a three-stage process—path pruning, Bézier curve smoothing, and iterative optimization—designed to minimize computational overhead while maintaining flight stability. By leveraging the STM32’s limited 72 MHz ARM Cortex-M3 core and 20 KB SRAM, the system performs all planning stages directly on the microcontroller without external computation. Experimental flight tests verify that the UAV can autonomously generate and follow smooth, collision-free trajectories across static obstacle fields with high tracking accuracy. The results confirm the feasibility of executing a full RRT-based planner on an STM32-class embedded platform, establishing a practical pathway for resource-efficient, onboard UAV autonomy. Full article

This study presents the design and development of an intelligent end-effector integrated into a custom 7-degree-of-freedom (DOF) robotic arm for monitoring the health status of tomato plants during their growth stages. The robotic system combines five rotational and two prismatic joints, enabling both horizontal reach and vertical adaptability to inspect plants of varying heights without repositioning the robot’s base. The integrated vision module employs a YOLOv5 neural network trained with 7864 images of tomato leaves, including both healthy and diseased samples. Image preprocessing included normalization and data augmentation to enhance robustness under natural lighting conditions. The optimized model achieved a detection accuracy of 90.2% and a mean average precision (mAP) of 92.3%, demonstrating high reliability in real-time disease classification. The end-effector, fabricated using additive manufacturing, incorporates a Raspberry Pi 4 for onboard processing, allowing autonomous operation in agricultural environments. The experimental results validate the feasibility of combining a custom 7-DOF robotic structure with a deep learning-based detector for continuous plant monitoring. This research contributes to the field of agricultural robotics by providing a flexible and precise platform capable of early disease detection in dynamic cultivation conditions, promoting sustainable and data-driven crop management. Full article

For a robot to operate robustly in diverse real-world environments, reliable obstacle perception is essential, which fundamentally requires depth information of the surrounding scene. Monocular depth estimation provides a lightweight alternative to active sensors by predicting depth from a single RGB image. However, due to the absence of sufficient geometric and optical cues, it suffers from inherent depth ambiguity. To address this limitation, we propose R-Depth Net, a monocular absolute depth estimation network that utilizes distance-dependent defocus blur variations and optical flow as complementary depth signals. Furthermore, based on the depth maps generated by R-Depth Net, we design an algorithm for obstacle height estimation and traversability assessment. Experimental results in real-world environments show that the proposed method achieves an average RMSE of 0.30 m (15.7%) and MAE of 0.26 m (15.7%) for distance estimation within the 1.0–3.0 m range. For obstacle height estimation in the range of 0.10–0.20 m, the system achieves an average RMSE of 0.048 m (29.3%) and MAE of 0.040 m (26.4%). Finally, real-time deployment on a quadruped robot demonstrates that the estimated depth and height are sufficiently accurate to support on-board obstacle traversal decision-making. Full article

Autonomous navigation in unknown; map-free environments is a core requirement for advanced robotics. While significant breakthroughs have been achieved in terrestrial scenarios, extending this capability to the unstructured, dynamic, and harsh underwater domain remains an enormous challenge. This review comprehensively analyzes the mainstream technologies underpinning mapless autonomous underwater navigation, with a primary focus on conventional Autonomous Underwater Vehicles (AUVs). It systematically examines key technical pillars of AUV navigation, including Dead Reckoning and Simultaneous Localization and Mapping (SLAM). Furthermore, inspired by the emerging concept of fourth-generation submersibles—which leverage living organisms rather than conventional machinery—this review expands its scope to include live fish as potential controlled platforms for underwater navigation. It first dissects the sophisticated sensory systems and hierarchical navigational strategies that enable aquatic animals to thrive in complex underwater habitats. Subsequently, it categorizes and evaluates state-of-the-art methods for controlling live fish via Brain-Computer Interfaces (BCIs), proposing a three-stage control hierarchy: Direct Motor Control, Semi-Autonomous Control with Task-Level Commands, and Autonomous Control by Biological Intelligence. Finally, the review summarizes current limitations in both conventional AUV technologies and bio-hybrid systems and outlines future directions, such as integrating external sensors with fish, developing onboard AI for adaptive control, and constructing bio-hybrid swarms. This work bridges the gap between robotic engineering and biological inspiration, providing a holistic reference for advancing mapless autonomous underwater navigation. Full article

Ensuring precise trajectory tracking and stability in unconventional UAVs is a critical challenge in aerial robotics. This paper investigates a three-rotor UAV with complex underactuated dynamics and develops a nonlinear backstepping controller. The UAV model highlights the essential role of onboard sensors, since position and angular velocity measurements are fundamental for feedback and must be continuously exploited by the control law. Using these sensor-based signals in simulation, the proposed controller achieves accurate trajectory tracking, fast convergence, and stable behavior. The study emphasizes that sensor integration is crucial for enabling reliable autonomous flight of unconventional UAVs. Full article

Strawberries, a high-value crop with growing demand, face increasing challenges from labour shortages, declining pollinator populations, and the limitations of inconsistent manual pollination. This paper presents an IoT-enabled robotic system designed to automate strawberry pollination in open-field raised-bed environments with minimal human intervention. The system consists of a mobile rover equipped with an ESP32-CAM for image capture and a robotic arm mounted on an Arduino Uno, capable of controlled X, Y, and Z positioning to perform targeted pollination. Images of strawberry beds are transmitted to a locally deployed server, which uses a lightweight detection model to identify flowers. System components communicate asynchronously via HTTP and I2C protocols, and the onboard event-driven architecture enables responsive behaviour while minimizing RAM and power usage, which is an essential requirement for low-cost, field-deployable robotics. The server also manages multi-rover scheduling through a custom priority queue designed for low-end hardware. In controlled lo0ad tests, the scheduler improved average response time by 6.9% and handled 2.4% more requests compared to the default queueing system, while maintaining stability. Preliminary field tests demonstrate successful flower identification and reliable arm positioning under real-world conditions. Although full system yield measurements are ongoing, current results validate the core design’s functional feasibility. Unlike previous systems that focus on greenhouse deployments or simpler navigation approaches, this work emphasizes modularity, affordability, and adaptability for small and medium farms, particularly in resource-constrained agricultural regions such as Sri Lanka. This study presents a promising step toward autonomous and scalable pollination systems that integrate embedded systems, robotics, and IoT for practical use in precision agriculture. Full article

Regular monitoring of mineral dust is essential in order to assess its impact on air quality, human health, and climate, with satellite observations in recent decades playing a crucial role by providing consistent global coverage of various aerosol properties. In this study, the Dust Flag product of the Infrared Atmospheric Sounding Interferometer (IASI), onboard the Meteorological Operational (MetOp) satellites, is evaluated using ground-based measurements from 120 Aerosol Robotic Network (AERONET) sites worldwide. The Dust Flag serves as both an indicator of dust presence and a pseudo-indicator of dust loading. To evaluate this product, a well-established aerosol classification scheme was applied, based on AERONET Aerosol Optical Depth (AOD) and Angstrom Exponent products. Results show that the Dust Flag reliably identifies dust, achieving a 74.1% agreement score with AERONET, although some cases are misclassified. Also, this study concludes that the Dust Flag signal increases with particle load, reaching maximum values during extreme coarse dust events. Cases when IASI does not agree with AERONET are further examined and may stem either from limitations in the AERONET classification methodology or from low atmospheric particle concentrations. Finally, the spatial variability of the agreement score is examined, with the highest scores found within and near the global “dust belt”. Full article

Robotic Process Automation (RPA) is a software-based technology that uses configurable algorithmic software agents (bots) to replicate manual user activities across digital systems. It represents an evolution from earlier workflow scripting tools, and is distinguished by its ability to be used without requiring substantial IT infrastructure modifications or extensive programming knowledge. In the banking and insurance sectors, organizations face increasing pressure to adopt modern technologies that streamline operations and reduce costs while complying with strict regulatory requirements. Robotic Process Automation (RPA) has emerged as a viable and cost-effective solution, enabling automation of repetitive and rule-based tasks without requiring major changes to legacy IT systems. This paper conducts a literature review to examine the current use cases of RPA technologies in banking and insurance, analyzing how these technologies are employed to enhance corporate efficiency and performance. The review draws from recent academic publications and case studies between 2017 and 2025, identifying core implementation areas such as customer onboarding, claims processing, compliance reporting, and underwriting automation. The results highlight substantial improvements in processing speed, error reduction, and resource optimization, along with evolving metrics for measuring effectiveness. The study concludes by identifying key success factors, performance measurement approaches, and challenges in RPA implementation, offering insights for both practitioners and researchers aiming to understand the role of automation in financial services transformation. Full article

Autonomous ground vehicles (AGVs) are of major importance in exploration missions since they perform difficult tasks in changing or harmful environments. Mapping and exploration is crucial in hazardous areas, or areas inaccessible to humans, demanding autonomous navigation. This paper proposes a lightweight, low-cost AGV platform, which will be used in resource-constrained situations and aimed at scenarios like exploration missions (e.g., cave interiors, biohazard environments, or fire-stricken buildings) where there are serious security threats to humans. The proposed system relies on simple ultrasonic sensors when navigating and applied traversal algorithms (e.g., BFS, DFS, or $A^{*}$) during path planning. Since on-board microcomputers have limited memory, the traversal data and direction decisions are stored in a file located on an SD card, which supports long-term, energy-saving navigation and risk-free backtracking. A fish-eye camera set on a servo motor captures three photos ordered from left to right and stores them on the SD card for further off-line processing, integrating each frame into a low-frame-rate video. Moreover, when the battery level falls below 50%, the exploration path does not extend further and the AGV returns to the base station, thus combining a secure backtracking procedure with energy-efficient decisions. The resultant platform is low-cost, modular, and efficient at augmenting; thus it is suitable for exploring missions with applications in search and rescue, educational robotics, and real-time applications in low-infrastructure environments. Full article