Dynamic Path Tracking of Industrial Robots With High Accuracy Using Photogrammetry Sensor

Shu, Tingting; Gharaaty, Sepehr; Xie, Wenfang; Joubair, Ahmed; Bonev, Ilian A.

doi:10.1109/tmech.2018.2821600

Cited by 72 publications

(27 citation statements)

References 20 publications

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“…This approach is able to improve the position accuracy of low cost systems as well as of high quality industrial robots (Hefele, Brenner, 2001, Durovic et al, 2017. For smooth pose estimation the usage of a Kalmann filter is proposed in (Shu et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Development of a Multi Camera Calibration for an Analysis and Manipulation System of Biological Samples in Petri Dishes

Hardner

Schneider

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Cameras can provide accurate 3D positioning for a robotic system. Calibration of interior orientation as well as the relative orientation when using multiple cameras is a necessary step. Integration of the automatic calibration process into the robot system is useful to provide a simple system for the user. The paper presents a method for calibrating a multi camera system using the open source Ceres solver and compares it to a commercial software. The motivation is a measurement system for laboratory automation where biological samples in Petri dishes will be manipulated with different tools. For this application we will examine the camera setup, show first results of the calibration and the possible accuracy. Lastly we will draw conclusions for the final system prototype and necessary improvements to be implemented in order to provide accurate measurements.

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Section: Related Workmentioning

confidence: 99%

Development of a Multi Camera Calibration for an Analysis and Manipulation System of Biological Samples in Petri Dishes

Hardner

Schneider

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…However, the vision system often has errors due to noises during its operation. Therefore, some studies used the extended Kalman filter (EKF) [24] or adaptive Kalman filter (AKF) [25], [26] to optimize the pose estimation. In this study, the pose estimation of the 3RRR planar parallel robot is performed based on the vision system using the 3D Intel RealSense camera D435i.…”

Section: Introductionmentioning

confidence: 99%

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

Huynh

Kuo

2020

IEEE Access

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This study proposes a dynamic filtered path tracking control schema for a 3RRR planar parallel robot to reduce the positioning errors caused by various sources such as backlash in the mechanical system, system nonlinearities, uncertainties, and unknown disturbances. The optimal recursive algorithm is implemented absolutely in path planning based on the optimization problem. Compared to other methods, this algorithm not only helps the robot avoid singularities but also ensures the shortest movement distance of links thanks to the optimization in path planning. The vision system using the 3D Intel RealSense camera D435i is proposed to provide the visual measurement as feedback for the pose estimation of the 3RRR planar parallel robot. The unscented Kalman filter is designed to reduce the errors of the pose estimation due to the camera's intrinsic parameters, the vibration of the mechanical system, and unknown noises. The establishment of important equations and parameters of the unscented Kalman filter is detailed in this study. The simulation, experiment, and comparison are conducted to verify the effectiveness and feasibility of the proposed approaches. INDEX TERMS 3RRR planar parallel robot, dynamic filtered path tracking control, optimal recursive path planning, vision-based pose estimation, unscented Kalman filter.

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“…Visual servoing system is an intelligent system that controls robots to complete tasks by visual signals so as to enhance the extensibility and adaptivity of robots [1]. According to different structures, visual servoing methods can be classified into position‐based visual servo [2], image‐based visual servo (IBVS) [3–6] and hybrid visual servo [7]. Due to the small computational burden of IBVS, this method has rapidly implemented for the last decade.…”

Section: Introductionmentioning

confidence: 99%

Non‐linear model predictive control for visual servoing systems incorporating iterative linear quadratic Gaussian

Jin

Liu

2020

IET Control Theory & Appl

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Dynamic Path Tracking of Industrial Robots With High Accuracy Using Photogrammetry Sensor

Cited by 72 publications

References 20 publications

Development of a Multi Camera Calibration for an Analysis and Manipulation System of Biological Samples in Petri Dishes

Development of a Multi Camera Calibration for an Analysis and Manipulation System of Biological Samples in Petri Dishes

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

Non‐linear model predictive control for visual servoing systems incorporating iterative linear quadratic Gaussian

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