Teleoperation control of Baxter robot using Kalman filter-based sensor fusion

Li, Chunxu; Yang, Chenguang; Wan, Jian; Annamalai, Andy; Cangelosi, Angelo

doi:10.1080/21642583.2017.1300109

Cited by 26 publications

(26 citation statements)

References 21 publications

(33 reference statements)

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“…We assume that every single joint of the human arm is taken into account separately and not depending on each other, which shows that all the KF parameters are in first order. According to our previous work [34], this is the main reason behind the de-coupling of the different position coordinates within the KF, thus we have A = B = G = H =1. In addition, since the measurement errors are not correlated in the x direction, the y direction and the z direction, the filtering can be performed separately in three directions.…”

Section: Kalman Filtering Based Sensor Fusionmentioning

confidence: 95%

An Augmented Reality Based Human-Robot Interaction Interface Using Kalman Filter Sensor Fusion

Fahmy

Sienz

2019

Sensors

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In this paper, the application of Augmented Reality (AR) for the control and adjustment of robots has been developed, with the aim of making interaction and adjustment of robots easier and more accurate from a remote location. A LeapMotion sensor based controller has been investigated to track the movement of the operator hands. The data from the controller allows gestures and the position of the hand palm’s central point to be detected and tracked. A Kinect V2 camera is able to measure the corresponding motion velocities in x, y, z directions after our investigated post-processing algorithm is fulfilled. Unreal Engine 4 is used to create an AR environment for the user to monitor the control process immersively. Kalman filtering (KF) algorithm is employed to fuse the position signals from the LeapMotion sensor with the velocity signals from the Kinect camera sensor, respectively. The fused/optimal data are sent to teleoperate a Baxter robot in real-time by User Datagram Protocol (UDP). Several experiments have been conducted to test the validation of the proposed method.

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Section: Kalman Filtering Based Sensor Fusionmentioning

confidence: 95%

An Augmented Reality Based Human-Robot Interaction Interface Using Kalman Filter Sensor Fusion

Fahmy

Sienz

2019

Sensors

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“…A kinematics based skeletal tracking system using Microsoft Kinect sensor with an upper limb virtual reality rehabilitation system has been investigated (Tao et al. 2013). In Raheja et al.…”

Section: Introductionmentioning

confidence: 99%

An enhanced teaching interface for a robot using DMP and GMR

Yang

2018

Int J Intell Robot Appl

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This paper develops an enhanced teaching interface tested on both a Baxter robot and a KUKA iiwa robot. Movements are collected from a human demonstrator by using a Kinect v2 sensor, and then the data is sent to a remote PC for the teleoperation with Baxter. Meanwhile, data is saved locally for the playback process of the Baxter. The dynamic movement primitive (DMP) is used to model and generalize the movements. In order to learn from multiple demonstrations accurately, dynamic time warping (DTW), is used to pretreat the data recorded by the robot platform and Gaussian mixture model (GMM), aiming to generate multiple patterns after the teaching process, are employed for the calculation of the DMP. Then the Gaussian mixture regression (GMR) algorithm is applied to generate a synthesized trajectory with smaller position errors in 3D space. This proposed approach is tested by performing two tasks on a KUKA iiwa and a Baxter robot.

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“…It has many advantages, compared to the conventional programming methods, such as: (i) TbD does not require a human instructor with expert skills/knowledge; (ii) human-to-robot skill transfer is achieved in a convenient and efficient manner; and (iii) most significantly, it takes human characteristics into account such as flexibility and compliance (Yang, Zeng, Cong, Wang, & Wang, 2018). Thereby, these benefits facilitate the performance of task accomplishment (Li, Yang, Wan, Annamalai, & Cangelosi, 2017). The intelligent learning based on TbD technology with trajectory matching is a research topic in imitation learning that has received considerable attention in the past few decades.…”

Section: Introductionmentioning

confidence: 99%

Development of writing task recombination technology based on DMP segmentation via verbal command for Baxter robot

Yang

Xu³

et al. 2018

Systems Science & Control Engineering

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This paper developed a character recombination technology based on dynamic movement primitive (DMP) segmentation using verbal command on a Baxter robot platform. Movements are recorded from a human demonstrator. The operator physically guides the Baxter robot to perform the movements for five times. This training data set is also utilized for playback process. Subsequently, the dynamic time warping is employed to pre-treat the data. The DMP is used to model and generalize every single movement. Gaussian mixture model is used to generate multiple patterns after the teaching process. Then the Gaussian mixture regression algorithm is applied to reduce the position errors in 3D space after the generation of a synthesized trajectory. A remote PC is used to control the command of Baxter to record or playback any trajectories via user datagram protocol (UDP) by typing commands in a text file. In addition, Dragon NaturalSpeaking software is used to transfer the voice data to text data. This proposed approach is tested by performing a Chinese character writing task with a Baxter robot, where different Chinese characters are written by teaching only one character.

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Teleoperation control of Baxter robot using Kalman filter-based sensor fusion

Cited by 26 publications

References 21 publications

An Augmented Reality Based Human-Robot Interaction Interface Using Kalman Filter Sensor Fusion

An Augmented Reality Based Human-Robot Interaction Interface Using Kalman Filter Sensor Fusion

An enhanced teaching interface for a robot using DMP and GMR

Development of writing task recombination technology based on DMP segmentation via verbal command for Baxter robot

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