Vishnu G. Nair

Ensuring the safety and survivability of unmanned aerial vehicles (UAVs) during stall conditions is critical for minimizing operational risks, financial losses, and system failures. This paper presents a novel stall detection and emergency recovery system that integrates an STM32-based flight controller with a multi-parameter, altitude-aware parachute deployment mecha- nism, distinguishing it from conventional approaches that rely solely on single-threshold pitch/roll triggers. Stall events are detected using gyroscope and accelerometer data from an onboard inertial measurement unit (IMU), combined with angular velocity, vertical acceleration, and motor saturation to accurately identify unrecoverable conditions. Emergency responses are triggered within 1.1 s of stall confirmation. System performance was validated through Mission Planner simulations and. Hardware-in-the-Loop (HIL) testing, demonstrating that parachute deployment at 25 m reduced impact velocity from 22.2 m/s to. 3.2 m/s (95% survival rate), while deployments at 10 m proved largely ineffective (survival rate ∼40%), emphasizing the need for low-altitude impact mitigation strategies below 15 m. Additionally, the system transmits GPS coordinates upon landing to facilitate rapid UAV retrieval. The proposed approach offers improved reliability, multi-parameter detection, and hybrid recovery. compared to existing methods, with direct applicability to delivery, surveillance, and industrial inspection drones. The design has been filed under Indian patent number 202,541,051,308.

This paper presents a four-wheel differential-drive (4WD) autonomous platform that consolidates grass cutting, collection, leaf crushing, and precision seeding through modular, quick-release toolheads. A vertically stacked two-unit architecture separates the drive/blower subsystem in a steel-framed base from a high-capacity collection chamber; transparent panels aid inspection and service. System specifications are formalized, and operating energy budgets are modelled to predict runtimes across cutting (≈ 1.2 h), crushing (≈ 2.0 h), and seeding (≈ 8.0 h) modes. Coverage-path algorithms (zigzag, spiral, concentric) are simulated, with results confirming that the boustrophedon pattern achieves complete rectangular coverage with minimal redundancy. Robustness simulations quantify debris deflection (> 95% rejection), slope climb limits (≈ 25° at < 20% slip), and stone-ingestion probability (≈ 10%), validating operational resilience. Finite-element analysis of the steel and aluminum chassis demonstrates high safety factors (> 15) with negligible stress or deformation under representative static loads. Beyond robotic functions, composting pathways for collected biomass are outlined to close the loop on sustainability. While dynamic load events and hardware validation are deferred to future work, the results indicate that the proposed modular 4WD platform integrates cutting, collection, and seed delivery with serviceability, structural robustness, and environmental benefit, making it a promising candidate for campus and small-scale agricultural automation.

The accurate estimation of spacecraft pose during close-proximity operations is a fundamental capability required for various space missions, including on-orbit servicing, debris management, and formation flying. This review provides a comprehensive overview of the latest techniques employed in spacecraft pose estimation, highlighting both cooperative and non-cooperative scenarios. We discuss a range of algorithmic approaches and sensor technologies, focusing on electro-optic sensors that meet the power and size limitations of modern satellites. The paper further explores the integration of Deep Learning (DL) methodologies, which are gaining traction due to their potential to enhance pose estimation precision. However, significant hurdles remain, including high computational demands and the challenges of transferring learning from synthetic datasets to real-world applications. The paper also emphasizes significant obstacles in determining spacecraft orientation, including variable illumination conditions, computational limitations, and restricted available data. Through an evaluation of current methodologies and the identification of areas necessitating further investigation, this survey aims to advance the development of more robust, efficient, and reliable autonomous navigation systems for future space missions. This research supports SDG 9 (Industry, Innovation, and Infrastructure) by advancing autonomous navigation technologies for safer and more efficient space operations. Enhanced spacecraft pose estimation facilitates responsible space sustainability (SDG 12) through enabling satellite life extension and debris mitigation, and contributes to SDG 13 (Climate Action) by improving precision in satellite-based Earth observation and environmental monitoring.

This study presents a finite element analysis (FEA)-driven design and preliminary experimental validation of a bio-inspired amphibious spider robot’s flapping fin mechanism for hybrid terrestrial–aquatic locomotion. The robot incorporates a six-legged walking system and a passive deployable fin-based swimming mechanism actuated via leg-tip hooks with spring-loaded retraction, enabling automatic transition between land and water operation when triggered by a water contact sensor. Structural performance of the fin under combined hydrostatic and dynamic pressures was evaluated in ANSYS, with dynamic loads derived from fin tip velocity corresponding to a baseline flapping frequency of 1 Hz. Candidate materials, including Nylon (PA12), PETG, TPU (98 A), and 304 L stainless steel foil, were compared through stress–strain–deformation analysis. A multi-criteria decision analysis identified 304 L stainless steel foil as the optimal choice for minimal deformation (0.64 mm) and high fatigue resistance. A functional prototype was fabricated using FDM-based 3D printing, integrating macro and micro servo motors for locomotion and fin deployment. Equipped with TPU fins (0.15 mm thickness) for initial trials, the 1.311 kg prototype achieved a measured flapping speed of 53.4 RPM (0.89 Hz) using a non-contact tachometer, closely matching simulation assumptions. The results confirm the feasibility of the proposed design, validate its actuation performance, and provide a foundation for future in-water propulsion measurements and fluid–structure interaction studies.

Unmanned Aerial Vehicles (UAVs), particularly quadcopters, are increasingly deployed in high-stakes operations including defense, tactical surveillance, disaster response, and GPS-denied missions. However, aerodynamic stall—caused by conditions like vortex ring state or rotor stall—remains a critical failure mode that can lead to sudden lift loss and catastrophic crashes. This study presents a hybrid stall prediction framework that unifies physics-informed synthetic data generation, interpretable machine learning, and embedded rule-based logic for real-time, onboard risk assessment. A custom simulator was developed to generate 25,921 stall-prone flight states by modeling key parameters such as throttle, vertical speed, blade angle of attack (AoA), airspeed, disc loading, and thrust-to-weight ratio. Using this dataset, an XGBoost classifier was trained for binary classification with two output classes: stall and non-stall, and optimized via threshold tuning and SMOTE-based class balancing. Using this dataset, an XGBoost classifier achieved 0.97 precision, 0.98 recall, 0.98 F1-score, and 0.96 accuracy for stall prediction after integration with domain-specific rules. SHapley Additive exPlanations (SHAP) values were applied to provide transparent, instance-level justifications, revealing that throttle, VRS, and disc loading were consistently the top contributors to stall predictions. In parallel, domain-specific aerodynamic rules—such as critical AoA and high disc loading thresholds—were deployed to catch edge cases such as near-threshold predictions, ambiguous vortex ring state regions, or unseen aerodynamic configurations. This modular system is designed for compatibility with onboard processors like Raspberry Pi, enabling real-time deployment even in resource-constrained environments. It supports frugal innovation without compromising safety, making it suitable for stealth drones, ISR swarms, payload-carrying UAVs, and companion-computer-based quadcopters. By combining data-driven predictions with physics-aware rule triggers, the system reduces false negatives and false positives, improving reliability in mission-critical conditions. Ultimately, this work pushes drone autonomy toward proactive, explainable, and hardware-efficient safety mechanisms—where UAVs not only predict aerodynamic stall but also justify it in real time.

This paper presents MRSimEx-DSTC, a decentralized and adaptive framework for multi-robot coverage path planning in unknown and dynamic environments. The proposed method integrates frontier-based exploration with Dynamic Spanning Tree Coverage (DSTC), allowing each robot to incrementally map the environment while dynamically replanning its coverage path in response to both static and moving obstacles detected via onboard LiDAR. To enable decentralized execution and prevent task redundancy, the workspace is partitioned using Manhattan-distance-based Voronoi decomposition, ensuring disjoint task allocation and collision-free parallel operation without centralized coordination. The framework is validated through simulations in Python and Gazebo across varying obstacle densities and robot–obstacle speed scenarios. Experimental results show that MRSimEx-DSTC achieves high coverage efficiency (up to 99.5%), minimal overlap, and robust real-time adaptability. Compared to state-of-the-art methods such as MR-SimExCoverage and MAC-Planner, the proposed approach demonstrates superior performance, lower planning overhead, and greater resilience under real-world constraints.

In this paper, the design and control analysis of a lightweight, miniaturized worm-inspired robot actuated by 8 mini coreless DC motors are presented. The robot’s movement capabilities include straight-line motion, turning, and speed modulation, achieved through distinct motor configurations. Structural analysis played a vital role in assessing the forces, stresses and deformations the body experiences under various loads. Rigid body analysis is performed to analyze the contact forces and relative motion between the bodies.Authors explored two advanced control strategies—Fractional Order PID (FOPID) and Fuzzy PID—to compare their effectiveness in trajectory tracking and motor synchronization. The FOPID controller offers enhanced flexibility by fine-tuning the proportional, integral, and derivative gains using fractional calculus, ensuring smoother trajectory following. In contrast, the Fuzzy PID controller adapts its gains in real time, providing robust performance in dynamic environments. Simulation results over a 25-second interval show that the FOPID controller excels in smooth, precise motion, while the Fuzzy PID controller proves effective in handling external disturbances. This study contributes to soft robotics by showcasing effective control methodologies for bio-inspired locomotion, with potential applications in search and rescue missions, medical devices, and inspection tasks within confined spaces.