We have developed a new method to estimate a Next Viewpoint (NV) which is effective for pose estimation of simple-shaped products for product display robots in retail stores. Pose estimation methods using Neural Netwo...
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ISBN:
(纸本)9798350377712;9798350377705
We have developed a new method to estimate a Next Viewpoint (NV) which is effective for pose estimation of simple-shaped products for product display robots in retail stores. Pose estimation methods using Neural Networks (NN) based on an RGBD camera are highly accurate, but their accuracy significantly decreases when the camera acquires few texture and shape features at a current view point. However, it is difficult for previous mathematical model-based methods to estimate effective NV which is because the simple shaped objects have few shape features. Therefore, we focus on the relationship between the pose estimation and NV estimation. When the pose estimation is more accurate, the NV estimation is more accurate. Therefore, we develop a new pose estimation NN that estimates NV simultaneously. Experimental results showed that our NV estimation realized a pose estimation success rate 77.3%, which was 7.4pt higher than the mathematical model-based NV calculation did. Moreover, we verified that the robot using our method displayed 84.2% of products.
This research study presents a comprehensive survey of Natural Language Processing (NLP) research, tracing its historical evolution from its inception to the present. The survey explores the key milestones and advance...
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A new control paradigm using angular momentum and foot placement as state variables in the linear inverted pendulum model has expanded the realm of possibilities for the control of bipedal robots. This new paradigm, k...
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ISBN:
(纸本)9781665491907
A new control paradigm using angular momentum and foot placement as state variables in the linear inverted pendulum model has expanded the realm of possibilities for the control of bipedal robots. This new paradigm, known as the ALIP model, has shown effectiveness in cases where a robot's center of mass height can be assumed to be constant or near constant as well as in cases where there are no non-kinematic restrictions on foot placement. Walking up and down stairs violates both of these assumptions, where center of mass height varies significantly within a step and the geometry of the stairs restrict the effectiveness of foot placement. In this paper, we explore a variation of the ALIP model that allows the length of the virtual pendulum formed by the robot's stance foot and center of mass to follow smooth trajectories during a step. We couple this model with a control strategy constructed from a novel combination of virtual constraint-based control and a model predictive control algorithm to stabilize a stair climbing gait that does not soley rely on foot placement. Simulations on a 20-degree of freedom model of the Cassie biped in the SimMechanics simulation environment show that the controller is able to achieve periodic gait.
This paper introduces a modular IoT-based framework for the monitoring and control of critical infrastructure, addressing challenges such as system resilience, adaptability, and low-energy operation. The proposed modu...
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Global redundancy resolution (GRR) roadmap is a novel concept in robotics that facilitates the mapping from task space paths to configuration space paths in a legible, predictable, and repeatable way. Such roadmaps co...
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ISBN:
(纸本)9798350377712;9798350377705
Global redundancy resolution (GRR) roadmap is a novel concept in robotics that facilitates the mapping from task space paths to configuration space paths in a legible, predictable, and repeatable way. Such roadmaps could find widespread utility in applications such as safe teleoperation, consistent path planning, and motion primitives generation. However, previous methods to compute GRR roadmaps often necessitate a lengthy computation time and produce non-smooth paths, limiting their practical efficacy. To address this challenge, we introduce a novel method EXPANSION- GRR that leverages efficient configuration space projections and enables a rapid generation of smooth roadmaps that satisfy the task constraints. Additionally, we propose a simple multi-seed strategy that further enhances the final quality. We conducted experiments in simulation with a 5-link planar manipulator and a Kinova arm. We were able to generate the GRR roadmaps up to 2 orders of magnitude faster while achieving higher smoothness. We also demonstrate the utility of the GRR roadmaps in teleoperation tasks where our method outperformed prior methods and reactive IK solvers in terms of success rate and solution quality.
This article is concerned with data-driven analysis and controller design for continuous-time sampled-data systems. The linear system considered in this paper is controlled under the periodic event-triggering transmis...
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This article is concerned with data-driven analysis and controller design for continuous-time sampled-data systems. The linear system considered in this paper is controlled under the periodic event-triggering transmission mechanism. Firstly, the periodic event-triggered control (PETC) systems are modeled and analyzed by the time-delay approach. And model-based stability conditions are presented by invoking the Lyapunov stability approach. Secondly, based on the model-based conditions and a popular data-based representation, data-based stability criteria are deduced by using only noisy data. The stability criteria guarantee the stability properties robustly for all unknown systems consistent with the measured data. The data-driven estimation of the maximum detecting interval (MDI) is also obtained directly without model knowledge. Beyond that, the data-based method for the controller design as well as computing a possibly large MDI under various triggering parameters is put forth. Finally, the effectiveness of the proposed methods is demonstrated by the numerical simulation and the hardware-in-the-loop (HIL) experiment.
作者：
Karri, Varun KumarPrasath, N.School of Computing
Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Chennai Tamilnadu Kattankulathur India
This research study presents the design of an IoT-based system that detects pesticide residues in fruits and vegetables. Enhancements in food safety status decrease the health hazards attributed to chemicals used in a...
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Visual-Inertial Odometry (VIO) has been widely used by autonomous drones as an onboard navigation method. However, it suffers from drifts especially in scenarios where the environments have few texture features such a...
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ISBN:
(纸本)9798350377712;9798350377705
Visual-Inertial Odometry (VIO) has been widely used by autonomous drones as an onboard navigation method. However, it suffers from drifts especially in scenarios where the environments have few texture features such as an empty room with solid color walls. Optical flow sensors are another type of onboard sensor used by drones that face downward and measure the velocity by detecting changes in pixels between consecutive images, which don't introduce accumulative error. In this work, we present an efficient tight-coupled estimator to improve the accuracy of VIO by fusing the measurements of a downward-facing optical flow sensor into the VIO framework consistently. We further analyze the observability of the estimators and prove that there are four unobservable directions in the ideal case and then we utilize OC-EKF to maintain the consistency of the estimator. Furthermore, we extend an adaptive weighting algorithm to the proposed method, which can better adapt to the scenes where feature tracking is less accurate. Finally, both simulation and real-world experiments demonstrate the feasibility of the proposed method.
In tele-health services, the security of patient information becomes an essential consideration during the exchange of information. In this digital age, digital image watermarking becomes a crucial tool for digital au...
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The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-c...
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ISBN:
(纸本)9798350377712;9798350377705
The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot's kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.
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