Dynamic Filtered Path Tracking Control for a 3RRR Robot Using Optimal Recursive Path Planning and Vision-Based Pose Estimation

Huynh, Ba-Phuc; Kuo, Yong-Lin

doi:10.1109/access.2020.3025952

Cited by 11 publications

(4 citation statements)

References 28 publications

(32 reference statements)

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“…Zhu et al [6] employed the D435i for safety monitoring of solitary individuals. Huynh et al [40] utilized D435i for estimating robot poses. Rong et al [41] used it for recognizing oyster mushrooms in auto-harvesting.…”

Section: Experimental Validation 31 Devices Overviewmentioning

confidence: 99%

Increased plane identification precision with stereo identification

Zhao

2023

Robotica

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Stereo vision allows machines to perceive their surroundings, with plane identification serving as a crucial aspect of perception. The accuracy of identification constrains the applicability of stereo systems. Some stereo vision cameras are cost-effective, compact, and user-friendly, resulting in widespread use in engineering applications. However, identification errors limit their effectiveness in quantitative scenarios. While certain calibration methods enhance identification accuracy using camera distortion models, they rely on specific models tailored to a camera’s unique structure. This article presents a calibration method that is not dependent on any particular distortion model, capable of correcting plane position and orientation identified by any algorithm, provided that the identification error is biased. A high-precision mechanical calibration platform is designed to acquire accurate calibration data while using the same detected material in real measurement scenarios. Experimental comparisons confirm the efficacy of plane pose correction on PCL-RANSAC, with the average relative error of distance reduced by 5.4 times and the average absolute error of angle decreasing by 41.2%.

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Section: Experimental Validation 31 Devices Overviewmentioning

confidence: 99%

Increased plane identification precision with stereo identification

Zhao

2023

Robotica

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“…The advantages of parallel manipulators include load distribution among all arms, high speed and acceleration, enhanced precision, and reduced weight compared to serial robots [2,3]. However, they tend to have a smaller workspace and more complex kinematic equations, making control more challenging [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Kinematic Modelling of a 3RRR Planar Parallel Robot Using Genetic Algorithms and Neural Networks

García-Samartín,

Barrientos

2023

Machines

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Kinematic modelling of parallel manipulators poses significant challenges due to the absence of analytical solutions for the Forward Kinematics (FK) problem. This study centres on a specific parallel planar robot, specifically a 3RRR configuration, and addresses the FK problem through two distinct methodologies: Genetic Algorithms (GA) and Neural Networks (NN). Utilising the Inverse Kinematic (IK) model, which is readily obtainable, both GA and NN techniques are implemented without the need for closed-loop formulations or non-systematic mathematical tools, allowing for easy extension to other robot types. A comparative analysis against an existing numerical method demonstrates that the proposed methodologies yield comparable or superior performance in terms of accuracy and time, all while reducing development costs. Despite GA’s time consumption limitations, it excels in path planning, whereas NN delivers precise results unaffected by stochastic elements. These results underscore the feasibility of using neural networks and genetic algorithms as viable alternatives for real-time kinematic modelling of robots when closed-form solutions are unavailable.

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“…Ref. [ 33 ] uses computer vision and an unscented Kalman filter to estimate the pose of a planar 3RRR parallel robot and uses a PID control to guide the robot through a point to point path-tracking task. In [ 34 , 35 ], VS is used to control a cable-driven parallel robot, pose-based (3D and 2D) schemes are developed as well as a mix of them (2D 1/2); a conservative sufficient condition for stability is obtained.…”

Section: Introductionmentioning

confidence: 99%

Depth-Dependent Control in Vision-Sensor Space for Reconfigurable Parallel Manipulators

Franco-López

Maya

González

et al. 2023

Sensors

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In this paper, a control approach for reconfigurable parallel robots is designed. Based on it, controls in the vision-sensor, 3D and joint spaces are designed and implemented in target tracking tasks in a novel reconfigurable delta-type parallel robot. No a priori information about the target trajectory is required. Robot reconfiguration can be used to overcome some of the limitations of parallel robots like small relative workspace or multiple singularities, at the cost of increasing the complexity of the manipulator, making its control design even more challenging. No general control methodology exists for reconfigurable parallel robots. Tracking objects with unknown trajectories is a challenging task required in many applications. Sensor-based robot control has been actively used for this type of task. However, it cannot be straightforwardly extended to reconfigurable parallel manipulators. The developed vision-sensor space control is inspired by, and can be seen as an extension of, the Velocity Linear Camera Model–Camera Space Manipulation (VLCM-CSM) methodology. Several experiments were carried out on a reconfigurable delta-type parallel robot. An average positioning error of 0.6 mm was obtained for static objectives. Tracking errors of 2.5 mm, 3.9 mm and 11.5 mm were obtained for targets moving along a linear trajectory at speeds of 6.5, 9.3 and 12.7 cm/s, respectively. The control cycle time was 16 ms. These results validate the proposed approach and improve upon previous works for non-reconfigurable robots.

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Dynamic Filtered Path Tracking Control for a 3RRR Robot Using Optimal Recursive Path Planning and Vision-Based Pose Estimation

Cited by 11 publications

References 28 publications

Increased plane identification precision with stereo identification

Increased plane identification precision with stereo identification

Kinematic Modelling of a 3RRR Planar Parallel Robot Using Genetic Algorithms and Neural Networks

Depth-Dependent Control in Vision-Sensor Space for Reconfigurable Parallel Manipulators

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