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Performing complex manipulation tasks in dynamic environments requires efficient Task and Motion Planning (TAMP) approaches, which combine high-level symbolic plan with low-level motion planning. Advances in Large Language Models (LLMs), such as GPT-4, are transforming task planning by offering natural language as an intuitive and flexible way to describe tasks, generate symbolic plans, and reason. However, the effectiveness of LLM-based TAMP approaches is limited due to static and template-based prompting, which struggles in adapting to dynamic environments and complex task contexts. To address these limitations, this work proposes a novel ontology-driven prompt-tuning framework that employs knowledge-based reasoning to refine and expand user prompts with task contextual reasoning and knowledge-based environment state descriptions. Integrating domain-specific knowledge into the prompt ensures semantically accurate and context-aware task plans. The proposed framework demonstrates its effectiveness by resolving semantic errors in symbolic plan generation, such as maintaining logical temporal goal ordering in scenarios involving hierarchical object placement. The proposed framework is validated through both simulation and real-world scenarios, demonstrating significant improvements over the baseline approach in terms of adaptability to dynamic environments, and the generation of semantically correct task plans., Comment: Submitted to Robotics and Automation Letters

Vision-based target tracking is crucial for unmanned surface vehicles (USVs) to perform tasks such as inspection, monitoring, and surveillance. However, real-time tracking in complex maritime environments is challenging due to dynamic camera movement, low visibility, and scale variation. Typically, object detection methods combined with filtering techniques are commonly used for tracking, but they often lack robustness, particularly in the presence of camera motion and missed detections. Although advanced tracking methods have been proposed recently, their application in maritime scenarios is limited. To address this gap, this study proposes a vision-guided object-tracking framework for USVs, integrating state-of-the-art tracking algorithms with low-level control systems to enable precise tracking in dynamic maritime environments. We benchmarked the performance of seven distinct trackers, developed using advanced deep learning techniques such as Siamese Networks and Transformers, by evaluating them on both simulated and real-world maritime datasets. In addition, we evaluated the robustness of various control algorithms in conjunction with these tracking systems. The proposed framework was validated through simulations and real-world sea experiments, demonstrating its effectiveness in handling dynamic maritime conditions. The results show that SeqTrack, a Transformer-based tracker, performed best in adverse conditions, such as dust storms. Among the control algorithms evaluated, the linear quadratic regulator controller (LQR) demonstrated the most robust and smooth control, allowing for stable tracking of the USV., Comment: submitted to IEEE Access

This paper presents an autonomous aerial system specifically engineered for operation in challenging marine GNSS-denied environments, aimed at transporting small cargo from a target vessel. In these environments, characterized by weakly textured sea surfaces with few feature points, chaotic deck oscillations due to waves, and significant wind gusts, conventional navigation methods often prove inadequate. Leveraging the DJI M300 platform, our system is designed to autonomously navigate and transport cargo while overcoming these environmental challenges. In particular, this paper proposes an anchor-based localization method using ultrawideband (UWB) and QR codes facilities, which decouples the UAV's attitude from that of the moving landing platform, thus reducing control oscillations caused by platform movement. Additionally, a motor-driven attachment mechanism for cargo is designed, which enhances the UAV's field of view during descent and ensures a reliable attachment to the cargo upon landing. The system's reliability and effectiveness were progressively enhanced through multiple outdoor experimental iterations and were validated by the successful cargo transport during the 2024 Mohamed BinZayed International Robotics Challenge (MBZIRC2024) competition. Crucially, the system addresses uncertainties and interferences inherent in maritime transportation missions without prior knowledge of cargo locations on the deck and with strict limitations on intervention throughout the transportation.

The decline of bee and wind-based pollination systems in greenhouses due to controlled environments and limited access has boost the importance of finding alternative pollination methods. Robotic based pollination systems have emerged as a promising solution, ensuring adequate crop yield even in challenging pollination scenarios. This paper presents a comprehensive review of the current robotic-based pollinators employed in greenhouses. The review categorizes pollinator technologies into major categories such as air-jet, water-jet, linear actuator, ultrasonic wave, and air-liquid spray, each suitable for specific crop pollination requirements. However, these technologies are often tailored to particular crops, limiting their versatility. The advancement of science and technology has led to the integration of automated pollination technology, encompassing information technology, automatic perception, detection, control, and operation. This integration not only reduces labor costs but also fosters the ongoing progress of modern agriculture by refining technology, enhancing automation, and promoting intelligence in agricultural practices. Finally, the challenges encountered in design of pollinator are addressed, and a forward-looking perspective is taken towards future developments, aiming to contribute to the sustainable advancement of this technology., Comment: 20 pages, 21 figures

Autonomous space rovers face significant challenges when navigating deformable and heterogeneous terrains due to variability in soil properties, which can lead to severe wheel slip, compromising navigation efficiency and increasing the risk of entrapment. To address this problem, we introduce SlipNet, a novel approach for predicting wheel slip in segmented regions of diverse terrain surfaces without relying on prior terrain classification. SlipNet employs dynamic terrain segmentation and slip assignment techniques on previously unseen data, enhancing rover navigation capabilities in uncertain environments. We developed a synthetic data generation framework using the high-fidelity Vortex Studio simulator to create realistic datasets that replicate a wide range of deformable terrain conditions for training and evaluation. Extensive simulation results demonstrate that our model, combining DeepLab v3+ with SlipNet, significantly outperforms the state-of-the-art TerrainNet method, achieving lower mean absolute error (MAE) across five distinct terrain samples. These findings highlight the effectiveness of SlipNet in improving rover navigation in challenging terrains.

The global positioning system (GPS) has become an indispensable navigation method for field operations with unmanned surface vehicles (USVs) in marine environments. However, GPS may not always be available outdoors because it is vulnerable to natural interference and malicious jamming attacks. Thus, an alternative navigation system is required when the use of GPS is restricted or prohibited. To this end, we present a novel method that utilizes an Unmanned Aerial Vehicle (UAV) to assist in localizing USVs in GNSS-restricted marine environments. In our approach, the UAV flies along the shoreline at a consistent altitude, continuously tracking and detecting the USV using a deep learning-based approach on camera images. Subsequently, triangulation techniques are applied to estimate the USV's position relative to the UAV, utilizing geometric information and datalink range from the UAV. We propose adjusting the UAV's camera angle based on the pixel error between the USV and the image center throughout the localization process to enhance accuracy. Additionally, visual measurements are integrated into an Extended Kalman Filter (EKF) for robust state estimation. To validate our proposed method, we utilize a USV equipped with onboard sensors and a UAV equipped with a camera. A heterogeneous robotic interface is established to facilitate communication between the USV and UAV. We demonstrate the efficacy of our approach through a series of experiments conducted during the ``Muhammad Bin Zayed International Robotic Challenge (MBZIRC-2024)'' in real marine environments, incorporating noisy measurements and ocean disturbances. The successful outcomes indicate the potential of our method to complement GPS for USV navigation.

Moving object segmentation is critical to interpret scene dynamics for robotic navigation systems in challenging environments. Neuromorphic vision sensors are tailored for motion perception due to their asynchronous nature, high temporal resolution, and reduced power consumption. However, their unconventional output requires novel perception paradigms to leverage their spatially sparse and temporally dense nature. In this work, we propose a novel event-based motion segmentation algorithm using a Graph Transformer Neural Network, dubbed GTNN. Our proposed algorithm processes event streams as 3D graphs by a series of nonlinear transformations to unveil local and global spatiotemporal correlations between events. Based on these correlations, events belonging to moving objects are segmented from the background without prior knowledge of the dynamic scene geometry. The algorithm is trained on publicly available datasets including MOD, EV-IMO, and \textcolor{black}{EV-IMO2} using the proposed training scheme to facilitate efficient training on extensive datasets. Moreover, we introduce the Dynamic Object Mask-aware Event Labeling (DOMEL) approach for generating approximate ground-truth labels for event-based motion segmentation datasets. We use DOMEL to label our own recorded Event dataset for Motion Segmentation (EMS-DOMEL), which we release to the public for further research and benchmarking. Rigorous experiments are conducted on several unseen publicly-available datasets where the results revealed that GTNN outperforms state-of-the-art methods in the presence of dynamic background variations, motion patterns, and multiple dynamic objects with varying sizes and velocities. GTNN achieves significant performance gains with an average increase of 9.4% and 4.5% in terms of motion segmentation accuracy (IoU%) and detection rate (DR%), respectively.

Bilateral teleoperation of low-speed Unmanned Ground Vehicles (UGVs) on soft terrains is crucial for applications like lunar exploration, offering effective control of terrain-induced longitudinal slippage. However, latency arising from transmission delays over a network presents a challenge in maintaining high-fidelity closed-loop integration, potentially hindering UGV controls and leading to poor command-tracking performance. To address this challenge, this paper proposes a novel predictor framework that employs a Physics-informed Long Short-Term Memory (PiLSTM) network for designing bilateral teleoperator controls that effectively compensate for large delays. Contrasting with conventional model-free predictor frameworks, which are limited by their linear nature in capturing nonlinear and temporal dynamic behaviors, our approach integrates the LSTM structure with physical constraints for enhanced performance and better generalization across varied scenarios. Specifically, four distinct predictors were employed in the framework: two compensate for forward delays, while the other two compensate for backward delays. Due to their effectiveness in learning from temporal data, the proposed PiLSTM framework demonstrates a 26.1\ improvement in delay compensation over the conventional model-free predictors for large delays in open-loop case studies. Subsequently, experiments were conducted to validate the efficacy of the framework in close-loop scenarios, particularly to compensate for the real-network delays experienced by teleoperated UGVs coupled with longitudinal slippage. The results confirm the proposed framework is effective in restoring the fidelity of the closed-loop integration. This improvement is showcased through improved performance and transparency, which leads to excellent command-tracking performance.

Tomato leaf diseases pose a significant challenge for tomato farmers, resulting in substantial reductions in crop productivity. The timely and precise identification of tomato leaf diseases is crucial for successfully implementing disease management strategies. This paper introduces a transformer-based model called TomFormer for the purpose of tomato leaf disease detection. The paper's primary contributions include the following: Firstly, we present a novel approach for detecting tomato leaf diseases by employing a fusion model that combines a visual transformer and a convolutional neural network. Secondly, we aim to apply our proposed methodology to the Hello Stretch robot to achieve real-time diagnosis of tomato leaf diseases. Thirdly, we assessed our method by comparing it to models like YOLOS, DETR, ViT, and Swin, demonstrating its ability to achieve state-of-the-art outcomes. For the purpose of the experiment, we used three datasets of tomato leaf diseases, namely KUTomaDATA, PlantDoc, and PlanVillage, where KUTomaDATA is being collected from a greenhouse in Abu Dhabi, UAE. Finally, we present a comprehensive analysis of the performance of our model and thoroughly discuss the limitations inherent in our approach. TomFormer performed well on the KUTomaDATA, PlantDoc, and PlantVillage datasets, with mean average accuracy (mAP) scores of 87%, 81%, and 83%, respectively. The comparative results in terms of mAP demonstrate that our method exhibits robustness, accuracy, efficiency, and scalability. Furthermore, it can be readily adapted to new datasets. We are confident that our work holds the potential to significantly influence the tomato industry by effectively mitigating crop losses and enhancing crop yields., Comment: 5 Figures and 1 Table

The underwater environment presents unique challenges, including color distortions, reduced contrast, and blurriness, hindering accurate analysis. In this work, we introduce MuLA-GAN, a novel approach that leverages the synergistic power of Generative Adversarial Networks (GANs) and Multi-Level Attention mechanisms for comprehensive underwater image enhancement. The integration of Multi-Level Attention within the GAN architecture significantly enhances the model's capacity to learn discriminative features crucial for precise image restoration. By selectively focusing on relevant spatial and multi-level features, our model excels in capturing and preserving intricate details in underwater imagery, essential for various applications. Extensive qualitative and quantitative analyses on diverse datasets, including UIEB test dataset, UIEB challenge dataset, U45, and UCCS dataset, highlight the superior performance of MuLA-GAN compared to existing state-of-the-art methods. Experimental evaluations on a specialized dataset tailored for bio-fouling and aquaculture applications demonstrate the model's robustness in challenging environmental conditions. On the UIEB test dataset, MuLA-GAN achieves exceptional PSNR (25.59) and SSIM (0.893) scores, surpassing Water-Net, the second-best model, with scores of 24.36 and 0.885, respectively. This work not only addresses a significant research gap in underwater image enhancement but also underscores the pivotal role of Multi-Level Attention in enhancing GANs, providing a novel and comprehensive framework for restoring underwater image quality.

Underwater robotic vision encounters significant challenges, necessitating advanced solutions to enhance performance and adaptability. This paper presents MARS (Multi-Scale Adaptive Robotics Vision), a novel approach to underwater object detection tailored for diverse underwater scenarios. MARS integrates Residual Attention YOLOv3 with Domain-Adaptive Multi-Scale Attention (DAMSA) to enhance detection accuracy and adapt to different domains. During training, DAMSA introduces domain class-based attention, enabling the model to emphasize domain-specific features. Our comprehensive evaluation across various underwater datasets demonstrates MARS's performance. On the original dataset, MARS achieves a mean Average Precision (mAP) of 58.57\%, showcasing its proficiency in detecting critical underwater objects like echinus, starfish, holothurian, scallop, and waterweeds. This capability holds promise for applications in marine robotics, marine biology research, and environmental monitoring. Furthermore, MARS excels at mitigating domain shifts. On the augmented dataset, which incorporates all enhancements (+Domain +Residual+Channel Attention+Multi-Scale Attention), MARS achieves an mAP of 36.16\%. This result underscores its robustness and adaptability in recognizing objects and performing well across a range of underwater conditions. The source code for MARS is publicly available on GitHub at https://github.com/LyesSaadSaoud/MARS-Object-Detection/

This research presents ADOD, a novel approach to address domain generalization in underwater object detection. Our method enhances the model's ability to generalize across diverse and unseen domains, ensuring robustness in various underwater environments. The first key contribution is Residual Attention YOLOv3, a novel variant of the YOLOv3 framework empowered by residual attention modules. These modules enable the model to focus on informative features while suppressing background noise, leading to improved detection accuracy and adaptability to different domains. The second contribution is the attention-based domain classification module, vital during training. This module helps the model identify domain-specific information, facilitating the learning of domain-invariant features. Consequently, ADOD can generalize effectively to underwater environments with distinct visual characteristics. Extensive experiments on diverse underwater datasets demonstrate ADOD's superior performance compared to state-of-the-art domain generalization methods, particularly in challenging scenarios. The proposed model achieves exceptional detection performance in both seen and unseen domains, showcasing its effectiveness in handling domain shifts in underwater object detection tasks. ADOD represents a significant advancement in adaptive object detection, providing a promising solution for real-world applications in underwater environments. With the prevalence of domain shifts in such settings, the model's strong generalization ability becomes a valuable asset for practical underwater surveillance and marine research endeavors.

In marine aquaculture, inspecting sea cages is an essential activity for managing both the facilities' environmental impact and the quality of the fish development process. Fish escape from fish farms into the open sea due to net damage, which can result in significant financial losses and compromise the nearby marine ecosystem. The traditional inspection system in use relies on visual inspection by expert divers or ROVs, which is not only laborious, time-consuming, and inaccurate but also largely dependent on the level of knowledge of the operator and has a poor degree of verifiability. This article presents a robotic-based automatic net defect detection system for aquaculture net pens oriented to on-ROV processing and real-time detection. The proposed system takes a video stream from an onboard camera of the ROV, employs a deep learning detector, and segments the defective part of the image from the background under different underwater conditions. The system was first tested using a set of collected images for comparison with the state-of-the-art approaches and then using the ROV inspection sequences to evaluate its effectiveness in real-world scenarios. Results show that our approach presents high levels of accuracy even for adverse scenarios and is adequate for real-time processing on embedded platforms.

Aquaculture is a thriving food-producing sector producing over half of the global fish consumption. However, these aquafarms pose significant challenges such as biofouling, vegetation, and holes within their net pens and have a profound effect on the efficiency and sustainability of fish production. Currently, divers and/or remotely operated vehicles are deployed for inspecting and maintaining aquafarms; this approach is expensive and requires highly skilled human operators. This work aims to develop a robotic-based automatic net defect detection system for aquaculture net pens oriented to on- ROV processing and real-time detection of different aqua-net defects such as biofouling, vegetation, net holes, and plastic. The proposed system integrates both deep learning-based methods for aqua-net defect detection and feedback control law for the vehicle movement around the aqua-net to obtain a clear sequence of net images and inspect the status of the net via performing the inspection tasks. This work contributes to the area of aquaculture inspection, marine robotics, and deep learning aiming to reduce cost, improve quality, and ease of operation., Comment: arXiv admin note: text overlap with arXiv:2308.13826

A thorough analysis of wheel-terrain interaction is critical to ensure the safe and efficient operation of space rovers on extraterrestrial surfaces like the Moon or Mars. This paper presents an approach for developing and experimentally validating a virtual wheel-terrain interaction model for the UAE Rashid rover. The model aims to improve the fidelity and capability of current simulation methods for space rovers and facilitate the design, evaluation, and control of their locomotion systems. The proposed method considers various factors, such as wheel grouser properties, wheel slippage, loose soil properties, and interaction mechanics. The model accuracy was validated through experiments on a Test-rig testbed that simulated lunar soil conditions. In specific, a set of experiments was carried out to test the behaviors acted on a Grouser-Rashid rover wheel by the lunar soil with different slip ratios of 0, 0.25, 0.50, and 0.75. The obtained results demonstrate that the proposed simulation method provides a more accurate and realistic simulation of the wheel-terrain interaction behavior and provides insight into the overall performance of the rover, Comment: Submitted to ICAR2023 conference

Visual perception is an important component for autonomous navigation of unmanned surface vessels (USV), particularly for the tasks related to autonomous inspection and tracking. These tasks involve vision-based navigation techniques to identify the target for navigation. Reduced visibility under extreme weather conditions in marine environments makes it difficult for vision-based approaches to work properly. To overcome these issues, this paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions. The proposed framework consists of an integrated perception pipeline that uses a generative adversarial network (GAN) to remove noise and highlight the object features before passing them to the object detector (i.e., YOLOv5). The detected visual features are then used by the USV to track the target. The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog. The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset, on which the proposed scheme has outperformed the existing methods across various metrics., Comment: IEEE/RSJ International Conference on Intelligent Robots (IROS-2023)

Tomatoes are a major crop worldwide, and accurately classifying their maturity is important for many agricultural applications, such as harvesting, grading, and quality control. In this paper, the authors propose a novel method for tomato maturity classification using a convolutional transformer. The convolutional transformer is a hybrid architecture that combines the strengths of convolutional neural networks (CNNs) and transformers. Additionally, this study introduces a new tomato dataset named KUTomaData, explicitly designed to train deep-learning models for tomato segmentation and classification. KUTomaData is a compilation of images sourced from a greenhouse in the UAE, with approximately 700 images available for training and testing. The dataset is prepared under various lighting conditions and viewing perspectives and employs different mobile camera sensors, distinguishing it from existing datasets. The contributions of this paper are threefold:Firstly, the authors propose a novel method for tomato maturity classification using a modular convolutional transformer. Secondly, the authors introduce a new tomato image dataset that contains images of tomatoes at different maturity levels. Lastly, the authors show that the convolutional transformer outperforms state-of-the-art methods for tomato maturity classification. The effectiveness of the proposed framework in handling cluttered and occluded tomato instances was evaluated using two additional public datasets, Laboro Tomato and Rob2Pheno Annotated Tomato, as benchmarks. The evaluation results across these three datasets demonstrate the exceptional performance of our proposed framework, surpassing the state-of-the-art by 58.14%, 65.42%, and 66.39% in terms of mean average precision scores for KUTomaData, Laboro Tomato, and Rob2Pheno Annotated Tomato, respectively., Comment: 23 pages, 6 figures and 8 Tables

Event cameras triggered a paradigm shift in the computer vision community delineated by their asynchronous nature, low latency, and high dynamic range. Calibration of event cameras is always essential to account for the sensor intrinsic parameters and for 3D perception. However, conventional image-based calibration techniques are not applicable due to the asynchronous, binary output of the sensor. The current standard for calibrating event cameras relies on either blinking patterns or event-based image reconstruction algorithms. These approaches are difficult to deploy in factory settings and are affected by noise and artifacts degrading the calibration performance. To bridge these limitations, we present E-Calib, a novel, fast, robust, and accurate calibration toolbox for event cameras utilizing the asymmetric circle grid, for its robustness to out-of-focus scenes. The proposed method is tested in a variety of rigorous experiments for different event camera models, on circle grids with different geometric properties, and under challenging illumination conditions. The results show that our approach outperforms the state-of-the-art in detection success rate, reprojection error, and estimation accuracy of extrinsic parameters., Comment: IEEE Transactions on Image Processing

Countersink inspection is crucial in various automated assembly lines, especially in the aerospace and automotive sectors. Advancements in machine vision introduced automated robotic inspection of countersinks using laser scanners and monocular cameras. Nevertheless, the aforementioned sensing pipelines require the robot to pause on each hole for inspection due to high latency and measurement uncertainties with motion, leading to prolonged execution times of the inspection task. The neuromorphic vision sensor, on the other hand, has the potential to expedite the countersink inspection process, but the unorthodox output of the neuromorphic technology prohibits utilizing traditional image processing techniques. Therefore, novel event-based perception algorithms need to be introduced. We propose a countersink detection approach on the basis of event-based motion compensation and the mean-shift clustering principle. In addition, our framework presents a robust event-based circle detection algorithm to precisely estimate the depth of the countersink specimens. The proposed approach expedites the inspection process by a factor of 10$\times$ compared to conventional countersink inspection methods. The work in this paper was validated for over 50 trials on three countersink workpiece variants. The experimental results show that our method provides a precision of 0.025 mm for countersink depth inspection despite the low resolution of commercially available neuromorphic cameras., Comment: 14 pages, 11 figures, 7 tables, submitted to Journal of Intelligent Manufacturing

This paper presents a theoretical study on the ability of multi-rotor aerial vehicles (MRAVs) with tiltable propellers to achieve and sustain static hovering at different orientations. To analyze the ability of MRAVs with tiltable propellers to achieve static hovering, a novel linear map between the platform's control inputs and applied forces and moments is introduced. The relation between the introduced map and the platform's ability to hover at different orientations is developed. Correspondingly, the conditions for MRAVs with tiltable propellers to realize and sustain static hovering are detailed. A numerical metric is then introduced, which reflects the ability of MRAVs to sustain static hovering at different orientations. A subclass of MRAVs with tiltable propellers is defined as the Critically Statically Hoverable platforms (CSH), where CSH platforms are MRAVs that cannot sustain static hovering with fixed propellers, but can achieve static hovering with tilting propellers. Finally, extensive simulations are conducted to test and validate the above findings, and to demonstrate the effect of the proposed numerical metric on the platform's dynamics.

Space exploration has witnessed revolutionary changes upon landing of the Perseverance Rover on the Martian surface and demonstrating the first flight beyond Earth by the Mars helicopter, Ingenuity. During their mission on Mars, Perseverance Rover and Ingenuity collaboratively explore the Martian surface, where Ingenuity scouts terrain information for rover's safe traversability. Hence, determining the relative poses between both the platforms is of paramount importance for the success of this mission. Driven by this necessity, this work proposes a robust relative localization system based on a fusion of neuromorphic vision-based measurements (NVBMs) and inertial measurements. The emergence of neuromorphic vision triggered a paradigm shift in the computer vision community, due to its unique working principle delineated with asynchronous events triggered by variations of light intensities occurring in the scene. This implies that observations cannot be acquired in static scenes due to illumination invariance. To circumvent this limitation, high frequency active landmarks are inserted in the scene to guarantee consistent event firing. These landmarks are adopted as salient features to facilitate relative localization. A novel event-based landmark identification algorithm using Gaussian Mixture Models (GMM) is developed for matching the landmarks correspondences formulating our NVBMs. The NVBMs are fused with inertial measurements in proposed state estimators, landmark tracking Kalman filter (LTKF) and translation decoupled Kalman filter (TDKF) for landmark tracking and relative localization, respectively. The proposed system was tested in a variety of experiments and has outperformed state-of-the-art approaches in accuracy and range.

Neuromorphic vision is a bio-inspired technology that has triggered a paradigm shift in the computer-vision community and is serving as a key-enabler for a multitude of applications. This technology has offered significant advantages including reduced power consumption, reduced processing needs, and communication speed-ups. However, neuromorphic cameras suffer from significant amounts of measurement noise. This noise deteriorates the performance of neuromorphic event-based perception and navigation algorithms. In this paper, we propose a novel noise filtration algorithm to eliminate events which do not represent real log-intensity variations in the observed scene. We employ a Graph Neural Network (GNN)-driven transformer algorithm, called GNN-Transformer, to classify every active event pixel in the raw stream into real-log intensity variation or noise. Within the GNN, a message-passing framework, called EventConv, is carried out to reflect the spatiotemporal correlation among the events, while preserving their asynchronous nature. We also introduce the Known-object Ground-Truth Labeling (KoGTL) approach for generating approximate ground truth labels of event streams under various illumination conditions. KoGTL is used to generate labeled datasets, from experiments recorded in chalenging lighting conditions. These datasets are used to train and extensively test our proposed algorithm. When tested on unseen datasets, the proposed algorithm outperforms existing methods by 8.8% in terms of filtration accuracy. Additional tests are also conducted on publicly available datasets to demonstrate the generalization capabilities of the proposed algorithm in the presence of illumination variations and different motion dynamics. Compared to existing solutions, qualitative results verified the superior capability of the proposed algorithm to eliminate noise while preserving meaningful scene events.

Robotic vision plays a key role for perceiving the environment in grasping applications. However, the conventional framed-based robotic vision, suffering from motion blur and low sampling rate, may not meet the automation needs of evolving industrial requirements. This paper, for the first time, proposes an event-based robotic grasping framework for multiple known and unknown objects in a cluttered scene. Compared with standard frame-based vision, neuromorphic vision has advantages of microsecond-level sampling rate and no motion blur. Building on that, the model-based and model-free approaches are developed for known and unknown objects' grasping respectively. For the model-based approach, event-based multi-view approach is used to localize the objects in the scene, and then point cloud processing allows for the clustering and registering of objects. Differently, the proposed model-free approach utilizes the developed event-based object segmentation, visual servoing and grasp planning to localize, align to, and grasp the targeting object. The proposed approaches are experimentally validated with objects of different sizes, using a UR10 robot with an eye-in-hand neuromorphic camera and a Barrett hand gripper. Moreover, the robustness of the two proposed event-based grasping approaches are validated in a low-light environment. This low-light operating ability shows a great advantage over the grasping using the standard frame-based vision. Furthermore, the developed model-free approach demonstrates the advantage of dealing with unknown object without prior knowledge compared to the proposed model-based approach., Comment: 37 pages

Vision based control of Unmanned Aerial Vehicles (UAVs) has been adopted by a wide range of applications due to the availability of low-cost on-board sensors and computers. Tuning such systems to work properly requires extensive domain specific experience, which limits the growth of emerging applications. Moreover, obtaining performance limits of UAV based visual servoing is difficult due to the complexity of the models used. In this paper, we propose a novel noise tolerant approach for real-time identification and tuning of visual servoing systems, based on deep neural networks (DNN) classification of system response generated by the modified relay feedback test (MRFT). The proposed method, called DNN with noise protected MRFT (DNN-NP-MRFT), can be used with a multitude of vision sensors and estimation algorithms despite the high levels of sensor's noise. Response of DNN-NP-MRFT to noise perturbations is investigated and its effect on identification and tuning performance is analyzed. The proposed DNN-NP-MRFT is able to detect performance changes due to the use of high latency vision sensors, or due to the integration of inertial measurement unit (IMU) measurements in the UAV states estimation. Experimental identification closely matches simulation results, which can be used to explain system behaviour and predict the closed loop performance limits for a given hardware and software setup. We also demonstrate the ability of DNN-NP-MRFT tuned UAVs to reject external disturbances like wind, or human push and pull. Finally, we discuss the advantages of the proposed DNN-NP-MRFT visual servoing design approach compared with other approaches in literature.

Slip detection is essential for robots to make robust grasping and fine manipulation. In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibration uncertainty. A threshold method is devised to autonomously sample noise in real-time to improve slip detection. Moreover, a fuzzy based suppression strategy using incipient slip feedback is proposed for regulating the grip force. A comprehensive experimental study of our proposed approaches under uncertainty and system for high-performance precision manipulation are presented. We also propose a slip metric to evaluate such performance quantitatively. Results indicate that the system can effectively detect incipient slip events at a sampling rate of 2kHz ($\Delta t = 500\mu s$) and suppress them before a gross slip occurs. The event-based approach holds promises to high precision manipulation task requirement in industrial manufacturing and household services., Comment: 18 pages, 14 figures

Robotic vision plays a major role in factory automation to service robot applications. However, the traditional use of frame-based camera sets a limitation on continuous visual feedback due to their low sampling rate and redundant data in real-time image processing, especially in the case of high-speed tasks. Event cameras give human-like vision capabilities such as observing the dynamic changes asynchronously at a high temporal resolution ($1\mu s$) with low latency and wide dynamic range. In this paper, we present a visual servoing method using an event camera and a switching control strategy to explore, reach and grasp to achieve a manipulation task. We devise three surface layers of active events to directly process stream of events from relative motion. A purely event based approach is adopted to extract corner features, localize them robustly using heat maps and generate virtual features for tracking and alignment. Based on the visual feedback, the motion of the robot is controlled to make the temporal upcoming event features converge to the desired event in spatio-temporal space. The controller switches its strategy based on the sequence of operation to establish a stable grasp. The event based visual servoing (EVBS) method is validated experimentally using a commercial robot manipulator in an eye-in-hand configuration. Experiments prove the effectiveness of the EBVS method to track and grasp objects of different shapes without the need for re-tuning., Comment: 8 pages, 10 figures

A significant barrier to deploying autonomous vehicles (AVs) on a massive scale is safety assurance. Several technical challenges arise due to the uncertain environment in which AVs operate such as road and weather conditions, errors in perception and sensory data, and also model inaccuracy. In this paper, we propose a system architecture for risk-aware AVs capable of reasoning about uncertainty and deliberately bounding the risk of collision below a given threshold. We discuss key challenges in the area, highlight recent research developments, and propose future research directions in three subsystems. First, a perception subsystem that detects objects within a scene while quantifying the uncertainty that arises from different sensing and communication modalities. Second, an intention recognition subsystem that predicts the driving-style and the intention of agent vehicles (and pedestrians). Third, a planning subsystem that takes into account the uncertainty, from perception and intention recognition subsystems, and propagates all the way to control policies that explicitly bound the risk of collision. We believe that such a white-box approach is crucial for future adoption of AVs on a large scale.

In spite of recent progress, soft robotics still suffers from a lack of unified modeling framework. Nowadays, the most adopted model for the design and control of soft robots is the piece-wise constant curvature model, with its consolidated benefits and drawbacks. In this work, an alternative model for multisection soft robots dynamics is presented based on a discrete Cosserat approach, which, not only takes into account shear and torsional deformations, essentials to cope with out-of-plane external loads, but also inherits the geometrical and mechanical properties of the continuous Cosserat model, making it the natural soft robotics counterpart of the traditional rigid robotics dynamics model. The soundness of the model is demonstrated through extensive simulation and experimental results for both plane and out-of-plane motions., Comment: 13 pages, 9 figures