Sohel Anwar

Introduction: Since its initial inception, robotic‐assisted minimally invasive surgery (RAMIS) is continually being applied to a wider array of procedures. However, there are significant limitations in current surgical subsystems, including a large learning curve, lack of effective haptic feedback, decreased surgical intuition, and decreased dexterity. Methods: A narrative review of the literature was conducted to provide an overview of key developments in the field. Relevant articles were identified through searches of PubMed, IEEE, Google Scholar, and EMBASE, along with manual screening of references. Results: A selection of representative articles covering topics including teleoperation, kinematics, dynamics, and haptic feedback is discussed to highlight major themes and recent advances. Conclusions: Despite recent advances, limitations to operator experience remain. Numerous strategies and approaches are currently being proposed and explored to further advance surgical subsystems, improving dexterity, haptic feedback, movement time delay, and learning curve.

Reliable condition monitoring of vanadium redox flow batteries (VRFBs) is essential for ensuring safe and efficient operation under varying stress conditions. This study presents a model-based operating-condition classification framework based on Multi-Model Adaptive Estimation (MMAE) for real-time identification of battery operating regimes. A reduced-order equivalent circuit model is used to represent battery dynamics, and condition-specific parameter sets are obtained offline using Particle Swarm Optimization (PSO) methodology to build a model bank. Four operating conditions derived from Accelerated Stressor Lifetime Testing (ASLT) datasets are considered: nominal operation (1.6 V, cycle 5), high-voltage operation (1.8 V, cycle 5), aged operation (1.6 V, cycle 70), and combined aged and high-voltage operation (1.8 V, cycle 70). For each of these operating conditions, an extended Kalman filter (EKF)-based residual generation scheme is developed and embedded within the MMAE framework to evaluate model likelihoods and update operating-condition probabilities in real time. The proposed MMAE-based approach was able to successfully identify the specific operating regimes and detect the transitions between stress conditions using only terminal current and voltage measurements. This study demonstrates that distinct operating conditions produce identifiable voltage signatures, enabling accurate and computationally efficient classification. The proposed methodology thus offers a robust framework for degradation-aware monitoring of VRFB systems, which could be leveraged in a battery management system for improved battery life.

While YOLO’s efficiency and accuracy have made it a popular choice for object detection and tracking in real-world applications, models trained on smaller datasets often suffer from intermittent detection failures, where objects remain undetected across multiple consecutive frames, significantly degrading tracking performance in practical scenarios. To address this challenge, we propose PrED (Predictive Enhancement of Detection), a novel framework that enhances object detection and aids in tracking by integrating low-confidence detections with multiple similarity metrics—including Intersection over Union (IoU), spatial distance similarity, and template similarity, and predicts the locations of undetected objects based on a parameter called predictability index. By maintaining object continuity during missed detections, PrED ensures robust tracking performance even when the underlying detection model experiences failures. Extensive evaluations across multiple benchmark datasets demonstrate PrED’s superior performance, achieving over 11% higher DetA with at least 6.9% MOTA improvement in our test scenarios, 17% higher detection accuracy (DetA) and 12.3% higher Multiple Object Tracking Accuracy (MOTA) on the KITTI training dataset, 8% higher DetA and 2.6% higher MOTA on the MOT17 training dataset, compared to ByteTrack, establishing PrED as an effective solution for enhancing tracking robustness in scenarios with suboptimal detection performance.

This paper presents a comparative study of data-driven modeling approaches for vanadium redox flow batteries (VRFBs), utilizing Multiple Linear Regression (MLR) and Random Forest (RF) algorithms. Experimental voltage–capacity datasets from a 1 kW/1 kWh VRFB system were digitized, processed, and used for model training, validation, and testing. The MLR model, built using eight optimized features, achieved a mean error (ME) of 0.0204 V, a residual sum of squares (RSS) of 8.87, and a root mean squared error (RMSE) of 0.1796 V on the test data, demonstrating high predictive performance in stationary operating regions. However, it exhibited limited accuracy during dynamic transitions. Optimized through out-of-bag (OOB) error minimization, the Random Forest model achieved a training RMSE of 0.093 V and a test RMSE of 0.110 V, significantly outperforming MLR in capturing dynamic behavior while maintaining comparable performance in steady-state regions. The accuracy remained high even at lower current densities. Feature importance analysis and partial dependence plots (PDPs) confirmed the dominance of current-related features and SOC dynamics in influencing VRFB terminal voltage. Overall, the Random Forest model offers superior accuracy and robustness, making it highly suitable for real-time VRFB system monitoring, control, and digital twin integration. This study highlights the potential of combining machine learning algorithms with electrochemical domain knowledge to enhance battery system modeling for future energy storage applications.

The United States leads in corn production and consumption in the world with an estimated USD 50 billion per year. There is a pressing need for the development of novel and efficient techniques aimed at enhancing the identification and eradication of weeds in a manner that is both environmentally sustainable and economically advantageous. Weed classification for autonomous agricultural robots is a challenging task for a single-camera-based system due to noise, vibration, and occlusion. To address this issue, we present a multi-camera-based system with decision-level sensor fusion to improve the limitations of a single-camera-based system in this paper. This study involves the utilization of a convolutional neural network (CNN) that was pre-trained on the ImageNet dataset. The CNN subsequently underwent re-training using a limited weed dataset to facilitate the classification of three distinct weed species: Xanthium strumarium (Common Cocklebur), Amaranthus retroflexus (Redroot Pigweed), and Ambrosia trifida (Giant Ragweed). These weed species are frequently encountered within corn fields. The test results showed that the re-trained VGG16 with a transfer-learning-based classifier exhibited acceptable accuracy (99% training, 97% validation, 94% testing accuracy) and inference time for weed classification from the video feed was suitable for real-time implementation. But the accuracy of CNN-based classification from video feed from a single camera was found to deteriorate due to noise, vibration, and partial occlusion of weeds. Test results from a single-camera video feed show that weed classification accuracy is not always accurate for the spray system of an agricultural robot (AgBot). To improve the accuracy of the weed classification system and to overcome the shortcomings of single-sensor-based classification from CNN, an improved Dempster–Shafer (DS)-based decision-level multi-sensor fusion algorithm was developed and implemented. The proposed algorithm offers improvement on the CNN-based weed classification when the weed is partially occluded. This algorithm can also detect if a sensor is faulty within an array of sensors and improves the overall classification accuracy by penalizing the evidence from a faulty sensor. Overall, the proposed fusion algorithm showed robust results in challenging scenarios, overcoming the limitations of a single-sensor-based system.

Lane-keeping systems are a major part of advanced driver assistance systems (ADAS). Existing lane detection algorithms are based on either Computer Vision (CV) models or deep learning techniques which are often vulnerable to unfamiliar routes, lane marking conditions, night-time driving, weather conditions, etc. To improve lane detection accuracy under various challenging conditions, we propose a framework that utilizes several lane detection models with different features to obtain a robust algorithm. The proposed Multiple Model Adaptive Estimation (MMAE) algorithm works with two cameras, one front camera and one rear camera. The front camera is used for lane offset estimates whereas the rear camera serves as a time-delayed reference for the estimated lane offsets. The offsets from front camera CV models (two) are used as inputs to the MMAE algorithm which compares the offset computed by the rear camera CV model (time-delayed) as the reference. The proposed MMAE algorithm then estimates the probability of lane offsets to match the time-delayed reference model lane offset and selects the offset with higher probability of matching with reference model. The offset from the time-delayed reference model cannot be used for the real-time lane keeping control system since it would produce erroneous steering output due to the time lag in offset estimated by the real camera model. Thus, the MMAE estimated offset offers a more accurate lane offset and hence used in a PID steering controller for the lane keeping system. The proposed algorithm is then deployed in an AirSim simulation environment for performance evaluation. The simulation results show that the proposed MMAE algorithm performed robustly even when one of the models performed poorly. The proposed lane detection algorithm was able to identify the poorly performing model and switch to the other model to ensure better lane detection performance.

Autonomous navigation in off-road environments presents significant challenges due to the diverse and unpredictable characteristics of natural terrains. Due to the class imbalances prevalent in the existing datasets, the current models exhibit difficulties adapting to different environmental conditions. In this paper, we present an approach to address these challenges through the development of a semantic segmentation model tailored specifically to aid in navigating diverse off-road scenarios. Our methodology consists of two primary components: dataset development and segmentation model construction. The dataset comprises 1,414 images derived from the Yamaha-CMU Off-Road dataset (YCOR) through accuracy enhancements and augmentation techniques. Subsequently, a segmentation model is developed employing an encoder-decoder architecture using Resnet34 as the feature extractor and U-Net as the decoder. The proposed model demonstrates a notably high segmentation accuracy and attains a micro- F₁ score of 90% or higher on benchmark datasets such as YCOR, Rellis-3D, and RUGD with minimal or no transfer learning, highlighting its versatility and adaptability across various environmental settings. The model also exhibits a per-frame inference time of 40ms, rendering it feasible for real-world application.

Rigorous testing of automotive sensors for accuracy and precision is essential before finalizing designs and proceeding with mass production. This testing process plays a crucial role in identifying these sensors’ potential shortcomings, which are essential components in the Advanced Driver Assistance Systems (ADAS). A virtual environment for sensor model simulation and testing provides a significant advantage in experimenting with the sensor in a vast array of realistic scenarios, which may be cost-prohibitive in physical testing. Thus, radar sensors, a commonly used sensor in ADAS, have been modeled using various methodologies in virtual environments. However, there is a need for a radar model that is both computationally efficient and accurate under different combinations of radar reflectivity, directivity, and signal-to-noise ratio (SNR). In this study, we propose a model of a Delphi-ESR 2.5 medium-range automotive radar in the Unreal Engine environment that leverages grid-based DBSCAN for point-cloud clustering and radar equations to calculate the SNR of grid points, facilitating object detection and tracking. The proposed model incorporates the reflectivity and directivity concept of Radar Cross-Section (RCS) to emulate the behavior of an actual radar. The sensor model exhibits superior accuracy and low computational requirement, with a mean error rate of 10.8% and an output rate of 8.7 Hz. This approach provides a reliable means of simulating and testing automotive radar sensors in a virtual environment, contributing to the further advancements of the ADAS systems.

Surgical scene understanding in Robot-assisted Minimally Invasive Surgery (RMIS) is highly reliant on visual cues and lacks tactile perception. Force-modulated surgical palpation with tactile feedback is necessary for localization, geometry/depth estimation, and dexterous exploration of abnormal stiff inclusions in subsurface tissue layers. Prior works explored surface-level tissue abnormalities or single layered tissue-tumor embeddings with more than 300 palpations for dense 2D stiffness mapping. Our approach focuses on 3D reconstructions of sub-dermal tumor surface profiles in multi-layered tissue (skin-fat-muscle) using a visually-guided novel tactile navigation policy. A robotic palpation probe with tri-axial force sensing was leveraged for tactile exploration of the phantom. From a surface mesh of the surgical region initialized from a depth camera, the policy explores a surgeon's region of interest through palpation, sampled from bayesian optimization. Each palpation includes contour following using a contact-safe impedance controller to trace the sub-dermal tumor geometry, until the underlying tumor-tissue boundary is reached. Projections of these contour following palpation trajectories allows 3D reconstruction of the subdermal tumor surface profile in less than 100 palpations. Our approach generates high-fidelity 3D surface reconstructions of rigid tumor embeddings in tissue layers with isotropic elasticities, although soft tumor geometries are yet to be explored.

Surgical Haptics is an emergent field of research to integrate and advance the sense of robotic touch in laparoscopic tools in robot-assisted minimally invasive surgery. Haptic feedback from the tooltip and soft tissue surface interaction during robotic palpation can be leveraged to detect the texture and contour of subsurface geometry. However, precise force modulation of the robotic palpating probe is necessary to determine stiff inclusions of the anatomy and maneuver successive manipulation tasks during surgery. This paper focuses on investigating the layered deformations associated with different force profiles involved in manipulating the superficial anatomy of soft tissues during dynamic robotic palpation to determine the underlying anomaly. A realistic three-dimensional (3D) cross-sectional soft tissue phantom with anatomical layers and tumor, as an anomaly, is designed, modeled, and analyzed to examine the effects of oriented palpating forces (0–5 N) of a 7 DOF robot arm equipped with a contoured palpation probe. Finite element static structural analysis of oriented robotic palpation on the developed 3D soft tissue phantoms (with and without anomaly) reveals the soft tissue layer deformations and associated strains needed to identify presence of stiffer inclusions or anomaly during Robotic palpation. The finite element analysis study shows that the difference in deformations of soft tissue layers (e.g., underlying myofascial layers) under stiffer inclusions at different force levels can facilitate haptic feedback to acquire information about subsurface tumors. The deformation variations are further compared to assess better palpation orientations for subsurface anomaly detection.