Vision-based kinematic analysis of the Delta robot for object catching

Kansal, Sachin; Mukherjee, Sudipto

doi:10.1017/s0263574721001491

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…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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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: Introductionmentioning

confidence: 99%

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

2023

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“…In [ 29 ], a vision system is used to estimate the pose of the Hexa parallel robot, while [ 31 ] uses a computer vision system to develop an obstacle collision detection method for the same robot, no control description is included in either work. In [ 32 ], computer vision is used to identify the geometric parameters of a Delta parallel architecture. Regular PIDs are then used to control the robot joints.…”

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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“…Also, the delta-robot visual-tracking platforms have been used to provide feedback signal for 1) dynamically visual-servo tracking, employing template matching method on FPGA [37], 2) tracking the position trajectory and controlling desired end-effector force simultaneously [38], 3) optimizing controller designs with respect to both camera and robot-geometric parameters [39] and 4) computing position and velocity of tracked objected and automatically camera calibration [40]. Furthermore, kinematic parameters identification of a delta robotic system, consisted of three delta manipulators acting as an individual finger, could be accomplished using a pose estimation with marker board detection from a vision system [41].…”

Section: Introductionmentioning

confidence: 99%

Visual Servo Kinematic Control of Delta Robot using YOLOv5 Algorithm

Yamtuan,

Radomngam,

Prempraneerach

2023

Journal of Robotics and Control

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Industrial delta robots with motion-and vision-base controllers, providing high-precision and fast/flexible motion, normally come with high cost and complex software. Low-cost delta robots with vision solution could be beneficial to vast industries to increase productivity and to reduce labor cost. A delta robot using a low-cost motion controller and an open-source vision system is developed to accomplish real-time visual servoing with high motion accuracy. In the low-cost motion controller, three parallel links’ upper-arm angles, computed from inverse kinematics for a given desired target position by a high-level computer, are used as reference position commands for three AC-motor drives. A low-level Arduino microcontroller is employed to convert these links’ angles to high-frequency pulses and on-off signals for synchronously controlling three motor angles and direction. Experimental results of a point-to-point motion tracking exhibit high-precision repeatability. Synchronous pulse generation from Arduino microcontroller and structural misalignments of parallel links are major challenges for achieving high motion accuracy. For the vision-based system, the YOLOv5 algorithm is implemented along with a Python GUI Application. Then, the visual-servo performance is evaluated on localization accuracy and recognition rate of 3-color objects. However, a partial object occlusion can reduce the visual classification rate. A sorting task of 4-category medicine boxes demonstrate a high-speed pick-and-place operation using the low-cost visual-servo system of this delta robot. Therefore, integration of low-cost visual servoing with this delta robot can revolutionize various industries, like automobile, pharmaceutical, and food sectors, in separating, sorting and packing applications.

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Vision-based kinematic analysis of the Delta robot for object catching

Cited by 11 publications

References 25 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

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

Visual Servo Kinematic Control of Delta Robot using YOLOv5 Algorithm

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