Reconstructing 3-D Graphical Model Using an Under-Constrained Cable-Driven Parallel Robot

Zare, Soroush; Ghanatian, Mohammad; Yazdi, Mohammad Reza Hairi; Masouleh, Mehdi Tale

doi:10.1109/icspis51611.2020.9349584

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Robotics has emerged as a cornerstone in our contemporary society, exerting a profound influence on diverse facets of modern life. Researchers are actively conducting numerous studies in this field, harnessing robots for object tracking [1], pervasive health applications [2], reconstructing graphical models [3], and exploring other cutting-edge domains [4]. In modern robotics, camera-based visual input plays a vital role in enabling robots to navigate and interact effectively within their environments.…”

Section: Introductionmentioning

confidence: 99%

Advancing Robot Perception in Non-Spiral Environments through Camera-based Image Processing

Hoorfar,

Bagheri

2023

EAI Endorsed Trans AI Robotics

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Robot perception heavily relies on camera-based visual input for navigating and interacting in its environment. As robots become integral parts of various applications, the need to efficiently compute their visibility regions in complex environments has grown. The key challenge addressed in this paper is to devise an innovative solution that not only accurately computes the visibility region V of a robot operating in a polynomial environment but also optimizes memory utilization to ensure real-time performance and scalability.The main objective of this research is to propose an algorithm that achieves optimal-time complexity and significantly reduces memory requirements for visibility region computation. By focusing on sub-linear memory utilization, we aim to enhance the robot's ability to perceive its surroundings effectively and efficiently.Previous approaches have provided solutions for visibility region computation in non-spiral environments, but most were not tailored to memory limitations. In contrast, the proposed algorithm is designed to achieve optimal time complexity that is O(n) while reducing memory usage to O(c/log n) variables, where c < n represents the number of critical corners in the environment. Leveraging the constant-memory model and memory-constrained algorithm, we aim to strike a balance between computational efficiency and memory usage.The algorithm's performance is rigorously evaluated through extensive simulations and practical experiments. The results demonstrate its linear-time complexity and substantial reduction in memory usage without compromising the accuracy of the visibility region computation. By efficiently handling memory constraints, the robot gains a cost-effective and reliable perception mechanism, making it well-suited for a wide range of real-world applications.The constant-memory model and memory-constrained algorithm presented in this paper offer a significant advancement in robot perception capabilities. By optimizing the visibility region computation in polynomial environments, our approach contributes to the efficient operation of robots, enhancing their performance and applicability in complex real-world scenarios. The results of this research hold promising potential for future developments in robotics, computer vision, and related fields.

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Section: Introductionmentioning

confidence: 99%

Advancing Robot Perception in Non-Spiral Environments through Camera-based Image Processing

Hoorfar,

Bagheri

2023

EAI Endorsed Trans AI Robotics

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“…Cable-driven parallel robot (CDPR) is a parallel robot in which rigid links are substituted by cables in order to control the end-effector (EE) position and orientation, called pose. This substitution leads to advantages such as reconfigurability [1], high payload to weight ratio, and large workspace besides ease of assembly, which are very useful for capturing videos or images in large zones with high precision [2]. A CDPR is completely constrained when the pose of its EE can be entirely determined if actuators are locked, and thus, all cable lengths are known.…”

Section: Introductionmentioning

confidence: 99%

Experimental study on the control of a suspended cable-driven parallel robot for object tracking purpose

et al. 2022

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In this paper, control of a suspended cable-driven parallel robot has been experimentally investigated based on the dynamic model of the robot for object tracking purpose. In order to improve the tracking ability of the robot, three control approaches, namely kinematic PID, dynamic PD, and a kinematic sliding mode control (SMC), have been implemented, both on the Simscape and on the robot constructed at the Human and Robot Interaction Laboratory. Neural network controller and dynamic SMC have been implemented on the Simscape model. Afterward, the effectiveness of each approach has been investigated by employing the root mean square error (RMSE) index. Simulation and experimental results reveal the ability of each controller for precise and smooth control. For precise and real-time object tracking, YOLOv5-s and YOLOv4-tiny model are trained. By comparing the obtained index values, the kinematic PID demonstrates the best performance with the maximum RMSE value of 0.018 compared to other methods.

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Experimental Study on Reducing the Oscillations of a Cable-Suspended Parallel Robot for Video Capturing Purposes using Simulated Annealing and Path Planning

Ghanatian

Yazdi

Masouleh

2021

2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

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Reconstructing 3-D Graphical Model Using an Under-Constrained Cable-Driven Parallel Robot

Cited by 3 publications

References 21 publications

Advancing Robot Perception in Non-Spiral Environments through Camera-based Image Processing

Advancing Robot Perception in Non-Spiral Environments through Camera-based Image Processing

Experimental study on the control of a suspended cable-driven parallel robot for object tracking purpose

Experimental Study on Reducing the Oscillations of a Cable-Suspended Parallel Robot for Video Capturing Purposes using Simulated Annealing and Path Planning

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