The Product-of-Exponential (POE) model simplifies kinematic modeling by establishing direct relationships between kinematic and end-effector errors using base coordinate and tool coordinates. However, It contains nume...
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Mean field approximation methodology has laid the foundation of modern Continuous Random Field (CRF) based solutions for the refinement of semantic segmentation. In this paper, we propose to relax the hard constraint ...
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ISBN:
(纸本)9781728196817
Mean field approximation methodology has laid the foundation of modern Continuous Random Field (CRF) based solutions for the refinement of semantic segmentation. In this paper, we propose to relax the hard constraint of mean field approximation - minimizing the energy term of each node from probabilistic graphical model, by a global optimization with the proposed dilated sparse convolution module (DSConv). In addition, adaptive global average-pooling and adaptive global max-pooling are implemented as replacements of fully connected layers. In order to integrate DSConv, we design an end-to-end, time-efficient DilatedCRF pipeline. The unary energy term is derived either from pre-softmax and post-softmax features, or the predicted affordance map using a conventional classifier, making it easier to implement DilatedCRF for varieties of classifiers. We also present superior experimental results of proposed approach on the suction dataset comparing to other CRF-based approaches.
To address the nonlinear, underactuated, and strongly coupled dynamics of quadrotor UAV systems, this paper proposes a control strategy. The designed controller combines sliding mode control (SMC) with linear active d...
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In the modern age of industry 4.0 and state-of-the-art manufacturing, automated inspection plays an essential role. The bearing in the rotary machine is a vital and critical component as it provides support and stabil...
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Ultrasound scanning is an imaging technique that aids medical professionals in diagnostics and interventional procedures. However, a trained human-in-the-loop (HITL) with a radiologist is required to perform the scann...
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ISBN:
(纸本)9781728196817
Ultrasound scanning is an imaging technique that aids medical professionals in diagnostics and interventional procedures. However, a trained human-in-the-loop (HITL) with a radiologist is required to perform the scanning procedure. We seek to create a novel ultrasound system that can provide imaging in the absence of a trained radiologist, say for patients in the field who suffered injuries after a natural disaster. One challenge of automating ultrasound scanning involves finding the optimal area to scan and then performing the actual scan. This task requires simultaneously maintaining contact with the surface while moving along it to capture high quality images. In this work, we present an automated Robotic Ultrasound System (RUS) to tackle these challenges. Our approach introduces a Bayesian Optimization framework to guide the probe to multiple points on the unknown surface. Our proposed framework collects the ultrasound images as well as the pose information at every probed point to estimate regions with high vessel density (information map) and the surface contour. Based on the information map and the surface contour, an area of interest is selected for scanning. Furthermore, to scan the proposed region, a novel 6-axis hybrid force-position controller is presented to ensure acoustic coupling. Lastly, we provide experimental results on two different phantom models to corroborate our approach.
Coverage Path Planning is the work horse of contemporary service task automation, powering autonomous floor cleaning robots and lawn mowers in households and office sites. While steady progress has been made on indoor...
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ISBN:
(纸本)9781728196817
Coverage Path Planning is the work horse of contemporary service task automation, powering autonomous floor cleaning robots and lawn mowers in households and office sites. While steady progress has been made on indoor cleaning and outdoor mowing, these environments are small and with simple geometry compared to general urban environments such as city parking garages, highway bridges or city crossings. To pave the way for autonomous road sweeping robots to operate in such difficult and complex environments, a benchmark suite with three large-scale 3D environments representative of this task is presented. On this benchmark we evaluate a new Coverage Path Planning method in comparison with previous well performing algorithms, and demonstrate state-of-the-art performance of the proposed method. Part of the success, for all evaluated algorithms, is the usage of automated domain adaptation by in-the-loop parameter optimization using Bayesian Optimization. Apart from improving the performance, tedious and bias-prone manual tuning is made obsolete, which makes the evaluation more robust and the results even stronger.
While there are many works developing methods for modeling and calibrating robot kinematics, assessing the accuracy of those models has received little attention. However, accuracy assessment is critically important f...
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ISBN:
(纸本)9798350358513;9798350358520
While there are many works developing methods for modeling and calibrating robot kinematics, assessing the accuracy of those models has received little attention. However, accuracy assessment is critically important for applications where the robot must operate with absolute accuracy over a large region of workspace, such as in robotic machining. When the model of such a system is well calibrated, the remaining deterministic error can be quite complex, owing to complicated gearing errors, deformations, and quasi-static thermal changes. Locating the largest deterministic error requires an exploration over the workspace, but assessing the largest error is complicated by repeatability error and measurement noise. How then to assess the largest error from such a measurement set? This paper evaluates the efficacy of two conventional methods, maximum measured error and outlier rejection, and a novel method based on model invalidation that uses a hypothesis testing framework. A machining robot is used to develop a numerical study for evaluation of these methods under differing magnitude of measurement noise. A high-order kinematic model of the robot is constructed as used as the true robot kinematics, and the workspace for that system is used as the region of interest. A best-fit Denavit-Hartenberg (DH) model is used as the model whose accuracy is to be measured. The study shows that the largest deterministic error can be difficult to locate with just a few percent of points approaching the defining accuracy limit. As expected, the largest measured error provides a poor (over)estimate of the error as noise is increased, but outlier rejection can be equally as bad as rare large deterministic errors can be easily mistaken for lowprobability large random error. The novel model invalidation method, however, performs well across all noise levels.
In this work, we robustly compare the performance of popular collision avoidance approaches for Uncrewed Surface Vehicles (USVs) in the context of adhering to the international Regulations for Preventing Collisions at...
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Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Nevertheless, given the lack of unified da...
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ISBN:
(纸本)9781728196817
Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Nevertheless, given the lack of unified data sets, metrics, and platforms to evaluate the exploration approaches, we develop an autonomous robot exploration benchmark entitled Explore-Bench. The benchmark involves various exploration scenarios and presents two types of quantitative metrics to evaluate exploration efficiency and multi-robot cooperation. Explore-Bench is extremely useful as, recently, deep reinforcement learning (DRL) has been widely used for robot exploration tasks and achieved promising results. However, training DRL-based approaches requires large data sets, and additionally, current benchmarks rely on realistic simulators with a slow simulation speed, which is not appropriate for training exploration strategies. Hence, to support efficient DRL training and comprehensive evaluation, the suggested Explore-Bench designs a 3-level platform with a unified data flow and 12x speed-up that includes a grid-based simulator for fast evaluation and efficient training, a realistic Gazebo simulator, and a remotely accessible robot testbed for high-accuracy tests in physical environments. The practicality of the proposed benchmark is highlighted with the application of one DRL-based and three frontier-based exploration approaches. Furthermore, we analyze the performance differences and provide some insights about the selection and design of exploration methods. Our benchmark is available at https: //github. com/efc-robot/Explore-Bench.
Lane line recognition plays an important role in road safety and intelligent transport system. This paper focuses on the key technology of lane line recognition, which is divided into three parts: firstly, the origina...
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