Satellites generally have flexible appendages such as solar arrays, and the vibration frequency and damping ratio of the flexible appendages are critical for satellite attitude control system design, but accurate moda...
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The proposed RMS-FlowNet is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation which can operate on point clouds of high density. For hierarchical scene flow estimation, th...
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ISBN:
(纸本)9781728196817
The proposed RMS-FlowNet is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation which can operate on point clouds of high density. For hierarchical scene flow estimation, the existing methods depend on either expensive Farthest-Point-Sampling (FPS) or structure-based scaling which decrease their ability to handle a large number of points. Unlike these methods, we base our fully supervised architecture on Random-Sampling (RS) for multiscale scene flow prediction. To this end, we propose a novel flow embedding design which can predict more robust scene flow in conjunction with RS. Exhibiting high accuracy, our RMS-FlowNet provides a faster prediction than state-of-the-art methods and works efficiently on consecutive dense point clouds of more than 250K points at once. Our comprehensive experiments verify the accuracy of RMS-FlowNet on the established FlyingThings3D data set with different point cloud densities and validate our design choices. Additionally, we show that our model presents a competitive ability to generalize towards the real-world scenes of KITTI data set without fine-tuning.
This paper tackles the problem of nonprehensile object transportation through a legged manipulator. A whole-body control architecture is devised to prevent sliding of the object placed on the tray at the manipulator&#...
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ISBN:
(纸本)9781728196817
This paper tackles the problem of nonprehensile object transportation through a legged manipulator. A whole-body control architecture is devised to prevent sliding of the object placed on the tray at the manipulator's end-effector and retain the legged robot balance during walking. The controller solves a quadratic optimization problem to realize the sought transportation task while maintaining the contact forces between the tray and the object and between the legs and the ground within their respective friction cones, also considering limits on the input torques. An extensive simulation campaign confirmed the feasibility of the approach and evaluated the control performance through a thorough statistical analysis conducted varying mass, friction, and the dimension of the transported object.
Large Language models (LLMs) have garnered significant attention for their ability to understand text and images, generate human-like text, and perform complex reasoning tasks. However, their ability to generalize thi...
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ISBN:
(纸本)9798331517519;9798331517526
Large Language models (LLMs) have garnered significant attention for their ability to understand text and images, generate human-like text, and perform complex reasoning tasks. However, their ability to generalize this advanced reasoning with a combination of natural language text for decision-making in dynamic situations requires further exploration. In this study, we investigate how well LLMs can adapt and apply a combination of arithmetic and common-sense reasoning, particularly in autonomous driving scenarios. We hypothesize that LLMs' hybrid reasoning abilities can improve autonomous driving by enabling them to analyze detected object and sensor data, understand driving regulations and physical laws, and offer additional context. This addresses complex scenarios, like decisions in low visibility (due to weather conditions), where traditional methods might fall short. We evaluated LLM based on accuracy by comparing their answers with human-generated ground truth inside CARLA. The results showed that when a combination of images (detected objects) and sensor data is fed into the LLM, it can offer precise information for brake and throttle control in autonomous vehicles across various weather conditions. This formulation and answers can assist in decision-making for autopilot systems.
The paper presents the results of modeling the dynamic behavior of a rotor system using fluid film bearings and magnetorheological squeeze dampers. A rigid rotor model is used to describe the dynamics. A linearized be...
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This paper develops an adaptive PID autotuner for multicopters, and presents simulation and experimental results. The autotuner consists of adaptive digital control laws based on retrospective cost adaptive control im...
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ISBN:
(纸本)9781728196817
This paper develops an adaptive PID autotuner for multicopters, and presents simulation and experimental results. The autotuner consists of adaptive digital control laws based on retrospective cost adaptive control implemented in the PX4 flight stack. A learning trajectory is used to optimize the autopilot during a single flight. The autotuned autopilot is then compared with the default PX4 autopilot by flying a test trajectory constructed using the second-order Hilbert curve. In order to investigate the sensitivity of the autotuner to the quadcopter dynamics, the mass of the quadcopter is varied, and the performance of the autotuned and default autopilot is compared. It is observed that the autotuned autopilot outperforms the default autopilot.
The quality of skills learned by robots from demonstrations depends on the level of demonstration by the instructor, while models such as Dynamic Motion Primitives (DMP) are extremely sensitive to the quality of demon...
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In this paper we analyze the equilibrium points of a collaborative transportation task, composed of two unmanned aerial vehicles and a payload - in this case, a bar. Moreover, centralized and decentralized linear mode...
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ISBN:
(纸本)9781728196817
In this paper we analyze the equilibrium points of a collaborative transportation task, composed of two unmanned aerial vehicles and a payload - in this case, a bar. Moreover, centralized and decentralized linear model predictive controllers are designed, where the nonlinear dynamics are linearized around the equilibrium points previously analyzed. A comparison between the centralized and decentralized formulations is provided, based on experimental results for both setups, and considering the time to solution and performance of each controller. Our findings provide new operational equilibrium points that can be paired with predictive model-based controllers for efficient operation.
This work focuses on analyzing different centralized task allocation methods for multiple quadruped systems. The goal is to assign tasks to agents by considering obstacles in the area in a way that minimizes power con...
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In the automation upgrading process of traditional labor-intensive industries, positioning method of workpiece affects the automated operation quality, efficiency, and system cost directly. Traditional workpiece posit...
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