The control of underactuated mechanical systems represents one of the most active areas of research in robotics and control system engineering, with a strong practical interest. Recently, methods based on differential...
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ISBN:
(数字)9798350378115
ISBN:
(纸本)9798350378122
The control of underactuated mechanical systems represents one of the most active areas of research in robotics and control system engineering, with a strong practical interest. Recently, methods based on differential flatness theory have been found to be useful in nonlinear control design for marine vehicles. However, to the best of the authors’ knowledge, existing works based on differential flatness theory have not addressed the impact of external non-zero mean disturbances or parametric uncertainties in the controller design for non-flat underactuated marine vehicles. Within this context, this work explores a scenario of trajectory tracking for a non-flat and nonlinear model of an underactuated surface ship under the influence of external disturbances and parametric uncertainties. By exploiting the Liouvillian and flat properties of the linearized system, we propose a tracking controller with integral action. Numerical simulation results show the proposed controller’s capability to overcome common problems of real-world systems, which may facilitate its implementation in practical applications.
This paper studies robotic manipulation of deformable, one-dimensional objects (DOOs) like ropes or cables, which has important potential applications in manufacturing, agriculture, and surgery. In such environments, ...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
This paper studies robotic manipulation of deformable, one-dimensional objects (DOOs) like ropes or cables, which has important potential applications in manufacturing, agriculture, and surgery. In such environments, the task may involve threading through or avoiding becoming tangled with other objects. Grasping with multiple grippers can create closed loops between the robot and DOO, and if an obstacle lies within this loop, it may be impossible to reach the goal. However, prior work has only considered the topology of the DOO in isolation, ignoring the arms that are manipulating it. Searching over possible grasps to accomplish the task without considering such topological information is very inefficient, as many grasps will not lead to progress on the task due to topological constraints. Therefore, we propose the ${{\mathcal{G}}_L} - {\text{signature}}$ which categorizes the topology of these grasp loops and show how it can be used to guide planning. We perform experiments in simulation on two DOO manipulation tasks to show that using the ${{\mathcal{G}}_L} - {\text{signature}}$ is faster and more successful than methods that rely on local geometry or additional finite-horizon planning. Finally, we demonstrate using the ${{\mathcal{G}}_L} - {\text{signature}}$ in a real-world dual-arm cable manipulation task.
Effective capture of multi-scale features is crucial for improving performance in 3D point cloud semantic segmentation tasks. This paper introduces a novel framework that enhances the extraction of semantic informatio...
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ISBN:
(数字)9798350389807
ISBN:
(纸本)9798350389814
Effective capture of multi-scale features is crucial for improving performance in 3D point cloud semantic segmentation tasks. This paper introduces a novel framework that enhances the extraction of semantic information from complex objects in 3D point clouds using multi-resolution techniques. By utilizing varying voxel resolutions and convolutional kernel sizes, we integrate high-resolution voxels to capture fine details and low-resolution voxels to extract global features, achieving robust feature fusion. Experimental results demonstrate the effectiveness of our proposed network validated on the ScanNet v2 dataset, particularly excelling in the semantic segmentation task for small objects and complex scenes. This study highlights the significance of multi-resolution strategies in 3D scene understanding, providing new insights for future research in the field.
This paper presents a decentralized cooperative motion planning approach for surface inspection of 3D structures which includes uncertainties like size, number, shape, position, using multi-robot systems (MRS). Given ...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
This paper presents a decentralized cooperative motion planning approach for surface inspection of 3D structures which includes uncertainties like size, number, shape, position, using multi-robot systems (MRS). Given that most of existing works mainly focus on surface inspection of single and fully known 3D structures, our motivation is two-fold: first, 3D structures separately distributed in 3D environments are complex, therefore the use of MRS intuitively can facilitate an inspection by fully taking advantage of sensors with different capabilities. Second, performing the aforementioned tasks when considering uncertainties is a complicated and time-consuming process because we need to explore, figure out the size and shape of 3D structures and then plan surface-inspection path. To overcome these challenges, we present a meta-learning approach that provides a decentralized planner for each robot to improve the exploration and surface inspection capabilities. The experimental results demonstrate our method can outperform other methods by approximately 10.5%-27% on success rate and 70%-75% on inspection speed.
This paper proposes an informative trajectory planning approach, namely, adaptive particle filter tree with sigma point-based mutual information reward approximation (ASPIRe), for mobile target search and tracking (SA...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
This paper proposes an informative trajectory planning approach, namely, adaptive particle filter tree with sigma point-based mutual information reward approximation (ASPIRe), for mobile target search and tracking (SAT) in cluttered environments with limited sensing field of view. We develop a novel sigma point-based approximation to accurately estimate mutual information (MI) for general, non-Gaussian distributions utilizing particle representation of the belief state, while simultaneously maintaining high computational efficiency. Building upon the MI approximation, we develop the Adaptive Particle Filter Tree (APFT) approach with MI as the reward, which features belief state tree nodes for informative trajectory planning in continuous state and measurement spaces. An adaptive criterion is proposed in APFT to adjust the planning horizon based on the expected information gain. Simulations and physical experiments demonstrate that ASPIRe achieves real-time computation and outperforms benchmark methods in terms of both search efficiency and estimation accuracy.
Nowadays, most business processes of modern enterprises with different degree of automation are realized by means of information systems (Online Transaction Processing, OLTP). To increase the share of automatic operat...
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Assessing the size of fish in their natural habitat could provide crucial information for managing and monitoring these resources, thereby enabling the development of sustainable fishing and resource conservation stra...
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This project presents a comprehensive approach to address the challenges of handwritten data structure and algorithm (DSA) problems through the integration of computer vision, Optical Character Recognition (OCR), and ...
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ISBN:
(数字)9798350350593
ISBN:
(纸本)9798350350609
This project presents a comprehensive approach to address the challenges of handwritten data structure and algorithm (DSA) problems through the integration of computer vision, Optical Character Recognition (OCR), and algorithmic analysis. Our methodology entails the creation of meticulously annotated datasets of handwritten graph images, crucial for training our models effectively. Leveraging state-of-the-art techniques, we employ Detectron2 and YOLO for robust detection of handwritten elements, including nodes and edges in DSA problem graphs. Additionally, the Microsoft Azure OCR API facilitates the accurate extraction of textual content from handwritten images, providing essential data for algorithmic analysis. Through experimentation and evaluation, our approach demonstrates remarkable accuracy and efficiency gains compared to conventional methods, with accuracy rates about 95% in adjacency matrix generation and 81% in weighted matrix generation. These results underscore the efficacy of our methodology in streamlining algorithmic problem-solving processes, significantly reducing manual effort, and enhancing productivity. The implications of our findings extend beyond mere efficiency gains, as the automation of DSA problem analysis holds promise for accelerating learning and education in the field. Future work will focus on refining our models, expanding to tackle diverse DSA problems, and exploring educational applications to empower learners and educators alike in the realm of data structures and algorithms.
We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high accuracy and robustness. LVI-SAM is bui...
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ISBN:
(纸本)9781728190778
We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high accuracy and robustness. LVI-SAM is built atop a factor graph and is composed of two sub-systems: a visual-inertial system (VIS) and a lidar-inertial system (LIS). The two sub-systems are designed in a tightly-coupled manner, in which the VIS leverages LIS estimation to facilitate initialization. The accuracy of the VIS is improved by extracting depth information for visual features using lidar measurements. In turn, the LIS utilizes VIS estimation for initial guesses to support scan-matching. Loop closures are first identified by the VIS and further refined by the LIS. LVI-SAM can also function when one of the two sub-systems fails, which increases its robustness in both texture-less and feature-less environments. LVI-SAM is extensively evaluated on datasets gathered from several platforms over a variety of scales and environments. Our implementation is available at https://***/lvi-sam.
Cross-pose estimation between rigid objects is a fundamental building block for robotic applications. In this paper, we propose a new cross-pose estimation method that predicts correspondences on a set level as oppose...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Cross-pose estimation between rigid objects is a fundamental building block for robotic applications. In this paper, we propose a new cross-pose estimation method that predicts correspondences on a set level as opposed to a point level. This contrasts methods that predict cross-pose from per-point correspondences, which can encounter optimization problems for objects with symmetries, since each point may have multiple valid correspondences. Our method, SCAlign, consists of a Set Correspondence Network (SCN) which predicts these sets and their correspondences, and an alignment module to compute their relative cross-pose. Taking point clouds of two objects as input, SCN predicts a set label for each point such that such that points that share a set label form a cross object correspondence. The alignment module then computes the cross-pose as the SE(3) transformation that aligns these set correspondences. We compare SCAlign against other cross-pose estimation baselines on a synthetically generated dataset, SynWidth, which contains randomly generated width-mate objects with symmetric or near-symmetric intercepts. SCAlign significantly outperforms the baselines on this challenging dataset. Additionally, we show that set correspondences can be leveraged to distinguish positive and negative matches between pegs and holes. Robot experiments further validate the practical application of this approach.
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