Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture

Kansal, Sachin; Kumar, Rajesh; Mukherjee, Sudipto

doi:10.1007/s11042-020-09937-9

Cited by 3 publications

(3 citation statements)

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“…A predetermined library of ArUco markers [6, 7, 15] was used to estimate the pose of an object. Using monocular vision and markers to achieve a Cadaver CVJ pose [8, 9, 16] is possible. The legs are connected to the base platform by universal (U) joints and the moving platform by spherical (S) joints.…”

Section: Methodsmentioning

confidence: 99%

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Investigating the vision-based intervertebral motion estimation of the Cadaver’s craniovertebral junction

2023

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Craniovertebral junction (CVJ) is one of the more complex parts of the spinal column. It provides mobility to the cranium and houses the spinal cord. In a healthy subject, the CVJ contributes 25% of the flexion–extension motion and 50% of the axial rotation of the neck. This work reports instrumentation development and results for evaluating implant performance in the stabilized CVJ after surgical procedures. Typically, some bony parts of the vertebrae causing compression to the spinal cord are removed and subsequently stabilized by the instrumenting implant in the CVJ. Pose estimation of the Cadaveric CVJ region is estimated using a monocular vision-based setup. The cervical spine’s first three vertebrae surround the CVJ area, where most cervical spine mobility originates. We aim to evaluate the performance of vision-based intervertebral motion estimation of the Cadaver’s CVJ in the Indian population, particularly in older people. A series of tests were performed on the Cadaver’s CVJ to evaluate the vision system-based motion estimation performance.

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Section: Methodsmentioning

confidence: 99%

“…The marker image distortion must be compared to the library's distortion to calculate the pose at the target position. The marker pattern improves accuracy [7][8][9][10][11][12]. The authors describe the normal CVJ anatomy and numerous CVJ disorders [10].…”

Section: Introductionmentioning

confidence: 99%

Investigating the vision-based intervertebral motion estimation of the Cadaver’s craniovertebral junction

2023

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“…The image of the identified marker and the marker image stored in the library is used for pose estimation. The transformation for perspective correction and damped least square method is used iteratively to minimize the projection error [20][21][22][23] and yields the pose of the marker (target position) in the camera frame. The process of determining the pose of the top platform of the 6-UScS manipulator with respect to the base platform frame is explained as follows:…”

Section: Pose Estimationmentioning

confidence: 99%

Vision-based pose estimation of craniocervical region: experimental setup and saw bone-based study

2021

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Summary This article discusses the intervertebral motion present in the craniovertebral junction (CVJ) region. The CVJ region is bounded by the first three vertebras from the spinal column. It helps in bringing most of the neck motion. Intervention in this region requires surgery in which an implant is placed to stabilize the whole system. The various available implants need to undergo performance evaluation as their performance varies from region and anatomical diversity. For the Indian population, we are targeting to evaluate the performance of such an implant, testing it into a cadaver. The region of interest will be loaded as per the loading condition of an average human. Motion in these regions is evaluated using the camera. A preliminary test was done on a saw bone model of CVJ to assess the performance of segmentation methods. Multiple such ArUco markers are used to increase pose accuracy further, and the pose of the entire board of multiple tags provides us with reliable pose estimation. The absolute error ranged from a minimum of 0.1 mm to a maximum of 16 mm. At the same time, the mean and median absolute errors were 3.8961 mm and 3.35 mm. By considering the absolute lengths, the percentage error showed the following trends. The percentage error was between 3.9168% and 0.0230%.

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An Obstacle Avoidance Control Strategy for DELTA Parallel Manipulator via Improved Genetic Algorithm

Zheng

et al. 2022

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC)

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Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture

Cited by 3 publications

References 15 publications

Investigating the vision-based intervertebral motion estimation of the Cadaver’s craniovertebral junction

Investigating the vision-based intervertebral motion estimation of the Cadaver’s craniovertebral junction

Vision-based pose estimation of craniocervical region: experimental setup and saw bone-based study

An Obstacle Avoidance Control Strategy for DELTA Parallel Manipulator via Improved Genetic Algorithm

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