1,721,149 research outputs found
Sensors, SLAM and Long-term Autonomy: A Review
Full text linkSimultaneous Localization and Mapping, commonly known as SLAM, has been an active research area in the field of Robotics over the past three decades. For solving the SLAM problem, every robot is equipped with either a single sensor or a combination of similar/different sensors. This paper attempts to review, discuss, evaluate and compare these sensors. Keeping an eye on future, this paper also assesses the characteristics of these sensors against factors critical to the long-term autonomy challenge
Coverage Path Planning Techniques for Inspection of Disjoint Regions with Precedence Provision
Full text linkRecent times are witnessing an emergence of sites that are hazardous for human access. This has created a global demand to equip agents with the ability to autonomously inspect such environments by computing a coverage path effectively and efficiently. However, inspection of such sites requires agents to consider the correlation of work, providing precedence provision in visiting regions. The current approaches to compute coverage path in the hazardous sites, however, do not consider precedence provision. To this end, coverage path planning strategies are proposed, which provide precedence provision. To meet the challenges, the problem is divided into two phases: inter-region and intra-region path planning. In the ‘inter-region’ path planning of the approach, the site comprising of multiple disjoint regions is modelled as connectivity graph. Two novel approaches, Mixed Integer Linear Programming (MILP) solution and heuristic based techniques, are proposed to generate the ordered sequence of regions to be traversed. In the ‘intra-region’ path planning of the approach, each region is decomposed into a grid and Boustrophedon Motion is planned over each region. The ability of combined approach to provide complete coverage is proved under minor assumption. An investigative study has been conducted to elucidate the efficiency of the proposed approach in different scenarios using simulation experiments. The proposed approach is evaluated against baseline approaches. The results manifest a significant reduction in cost and execution time, which caters to inspection of target sites comprising of multiple disjoint regions with precedence provision
Gamma-Induced Image Degradation Analysis of Robot Vision Sensor for Autonomous Inspection of Nuclear Sites
Full text linkThere is an increasing desire to conduct autonomous inspection of nuclear sites using robots. However, the presence of gamma radiation in nuclear sites induces degradation in vision sensors. In this paper, the effects of gamma radiation on a robot vision sensor (CMOS camera) used for radiological inspection is examined. The analyses have been carried out for two types of images at different dose rates: a) dark images b) illuminated images. In this work, dark images and chessboard images under illumination are analysed using various evaluation metrics to evaluate the effect of gamma radiation on CMOS Integrated Circuit (IC) and electronic circuitry of the sensor. Experimental results manifest significant changes in electrical properties like the generation of radiation-induced photo signal in sensing circuitry and radiation-induced noise affecting the visual odometry of the robot. System-level degradation for gamma dose rates upto 3 Gy/min intensifies, making data from the imaging sensor unreliable for the visual odometry. However, images captured for gamma dose rate upto 3 Gy/min can be used for surveillance purpose
The Grasp Strategy of a Robot Passer Influences Performance and Quality of the Robot-Human Object Handover
No full textTask-aware robotic grasping is critical if robots are to successfully cooperate with humans. The choice of a grasp is multi-faceted; however, the task to perform primes this choice in terms of hand shaping and placement on the object. This grasping strategy is particularly important for a robot companion, as it can potentially hinder the success of the collaboration with humans. In this work, we investigate how different grasping strategies of a robot passer influence the performance and the perceptions of the interaction of a human receiver. Our findings suggest that a grasping strategy that accounts for the subsequent task of the receiver improves substantially the performance of the human receiver in executing the subsequent task. The time to complete the task is reduced by eliminating the need of a post-handover re-adjustment of the object. Furthermore, the human perceptions of the interaction improve when a task-oriented grasping strategy is adopted. The influence of the robotic grasp strategy increases as the constraints induced by the object's affordances become more restrictive. The results of this work can benefit the wider robotics community, with application ranging from industrial to household human-robot interaction for cooperative and collaborative object manipulation.</p
Application of a parallel robot in lower limb rehabilitation: A brief capability study
No full textRobotic rehabilitation has a significant potential to reduce the clinical labor costs of physiotherapy. Robotic therapy allows patients to have more in-depth repetitive movements while the therapists evaluate the progress of the recovery. This paper investigates the potential of a 6 degrees of freedom parallel robot, designed and built at the University of Birmingham, for use in robotic rehabilitation of stroke patients. The foot trajectories of eight post-stroke patients were recorded and analyzed in a gait laboratory. A graphical user interface (GUI) has been designed, which enables the physiotherapist to select the desired exercise from a dedicated database. Three different rehabilitation exercises were investigated: hip flexion/extension, ankle dorsiflexion /plantarflexion, and marching. The results show that the robot was able to repeat all of these foot trajectories successfully, while being able to lift 200kg load in its dynamic mode. This suggests that the robot has the capability to successfully deliver lower limb rehabilitation exercises.</p
Scene Reconstruction Pose Estimation and Tracking
Full text linkThis book reports recent advances in the use of pattern recognition techniques for computer and robot vision. The sciences of pattern recognition and computational vision have been inextricably intertwined since their early days, some four decades ago with the emergence of fast digital computing. All computer vision techniques could be regarded as a form of pattern recognition, in the broadest sense of the term. Conversely, if one looks through the contents of a typical international pattern recognition conference proceedings, it appears that the large majority (perhaps 70-80%) of all pattern recognition papers are concerned with the analysis of images. In particular, these sciences overlap in areas of low level vision such as segmentation, edge detection and other kinds of feature extraction and region identification, which are the focus of this book
Real-Time Application Processing for FPGA-Based Resilient Embedded Systems in Harsh Environments
Full text linkReal-time embedded systems nowadays get employed in harsh environments such as space, nuclear sites to carry out critical operations. Along with the traditional software based (CPU) execution, FPGAs are now also emerging as a bright prospect to accomplish such routines. However, these platforms are often get plagued by faults generated due to the high radiations in such environments. As a result, the real-time applications running on the platform could also get jeopardized. Thus, efficient execution of a set of hard real-time applications on reconfigurable systems with anomaly detection and recovery mechanism is inevitable. This work aims at tackling such problem with a “healing” approach for extreme environments. Initially, the applications are intelligently partitioned for hardware and software execution, then attempts have been made to schedule hardware applications with intermittent preemption point. Upon detecting any abnormality on such distinct points, our approach orchestrates a healing mechanism to remediate the scenario without hampering the pre-determined schedule. Experimental validation of our proposed method reveals its effectiveness
Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks
Full text linkWeather conditions often disrupt the proper functioning of transportation systems. Present systems either deploy an array of sensors or use an in-vehicle camera to predict weather conditions. These solutions have resulted in incremental cost and limited scope. To ensure smooth operation of all transportation services in all-weather conditions, a reliable detection system is necessary to classify weather in wild. The challenges involved in solving this problem is that weather conditions are diverse in nature and there is an absence of discriminate features among various weather conditions. The existing works to solve this problem have been scene specific and have targeted classification of two categories of weather. In this paper, we have created a new open source dataset consisting of images depicting three classes of weather i.e rain, snow and fog called RFS Dataset. A novel algorithm has also been proposed which has used super pixel delimiting masks as a form of data augmentation, leading to reasonable results with respect to ten Convolutional Neural Network architectures
Gamma-induced Degradation Analysis of Commercial off-the-shelf Camera Sensors
Full text linkIn this work, we investigated the degradation of commercial camera sensors exposed to 100 Gy of γ dose at dose rates of 0.55 Gy/min and 1.34 Gy/min respectively. The results show that the degradation is strongly dependent on the dose rate but doesnt vary much with the accumulation of dose at constant dose rate. Furthermore, cameras with in-built processing electronics are more susceptible to gamma radiations as compared to the cameras with sensing unit only
A spatial fuzzy clustering algorithm with kernel metric based on immune clone for SAR image segmentation
No full textThe fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. However, FCM exhibits poor robustness to noise, often leading to unsatisfactory segmentations on noisy images. Additionally, the FCM algorithm is sensitive to the choice of initial cluster centers. In order to solve these problems, this paper proposes clone kernel spatial FCM (CKS_FCM), which improves segmentation performance in several ways. First, in CKS_FCM, an immune clone algorithm is used to generate the initial cluster centers, which helps prevent the algorithm from converging on local optima. Second, CKS_FCM improves the robustness to noise by incorporating spatial information into the objective function of FCM. Third, CKS_FCM uses a non-Euclidean distance based on a kernels metric, instead of the Euclidean distance conventionally used in FCM, to enhance the segmentation accuracy (SA). We present experimental results on both real and synthetic SAR images, which suggest that the proposed method can generate higher accuracy, and obtain more robustness to noise, as compared against six state-of-the-art methods from the literatures
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