A Wearable IMU System for Flexible Teleoperation of a Collaborative Industrial Robot

Škulj, Gašper; Vrabič, Rok; Podržaj, Primož

doi:10.3390/s21175871

Cited by 24 publications

(13 citation statements)

References 32 publications

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“…However, it calls for improvements in creating personalized, real-time models, especially for individuals with unusual walking patterns. Škulj et al [20] developed a system that uses wearable IMUs placed on the user's body to remotely control collaborative robots. This approach translates the user's motion into collaborative industrial robot commands through an innovative algorithm, which is noted for its user-friendliness, adaptability, and reliability.…”

Section: Imusmentioning

confidence: 99%

Robotics Perception and Control: Key Technologies and Applications

Luo,

Zhou,

Zeng

et al. 2024

Micromachines

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The integration of advanced sensor technologies has significantly propelled the dynamic development of robotics, thus inaugurating a new era in automation and artificial intelligence. Given the rapid advancements in robotics technology, its core area—robot control technology—has attracted increasing attention. Notably, sensors and sensor fusion technologies, which are considered essential for enhancing robot control technologies, have been widely and successfully applied in the field of robotics. Therefore, the integration of sensors and sensor fusion techniques with robot control technologies, which enables adaptation to various tasks in new situations, is emerging as a promising approach. This review seeks to delineate how sensors and sensor fusion technologies are combined with robot control technologies. It presents nine types of sensors used in robot control, discusses representative control methods, and summarizes their applications across various domains. Finally, this survey discusses existing challenges and potential future directions.

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Section: Imusmentioning

confidence: 99%

Robotics Perception and Control: Key Technologies and Applications

Luo,

Zhou,

Zeng

et al. 2024

Micromachines

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…Teleoperation enables the manipulation of a robotic device by a human operator distant from the robot's workspace (Niemeyer et al, 2016;Škulj et al, 2021). Traditional methods of robotic arm teleoperation focus on using the upper limb movements of humans including a joystick, haptic device, and inertial measurement unit (IMU) sensors (Zhao et al, 2017;Si et al, 2021;Škulj et al, 2021). Even though these methods enable precise control of various robotic arms in real-time, hands-free teleoperation can be useful in cases where human movement is limited which is already explained in the relevant literature.…”

Section: Introductionmentioning

confidence: 99%

“…Even though these methods enable precise control of various robotic arms in real-time, hands-free teleoperation can be useful in cases where human movement is limited which is already explained in the relevant literature. For instance, workers doing physical tasks with both hands in the industrial workspace would need the capability of hands-free teleoperation to conduct additional tasks simultaneously (Liu et al, 2021;Škulj et al, 2021). In addition to industrial purposes, it can be used to assist patients suffering from motor disabilities in an upper limb due to stroke or spinal cord injury (Meng et al, 2016;Chen et al, 2018;Quiles et al, 2022;Zhou et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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EEG-controlled tele-grasping for undefined objects

Kim,

Choi,

Jang

et al. 2023

Front. Neurorobot.

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This paper presents a teleoperation system of robot grasping for undefined objects based on a real-time EEG (Electroencephalography) measurement and shared autonomy. When grasping an undefined object in an unstructured environment, real-time human decision is necessary since fully autonomous grasping may not handle uncertain situations. The proposed system allows involvement of a wide range of human decisions throughout the entire grasping procedure, including 3D movement of the gripper, selecting proper grasping posture, and adjusting the amount of grip force. These multiple decision-making procedures of the human operator have been implemented with six flickering blocks for steady-state visually evoked potentials (SSVEP) by dividing the grasping task into predefined substeps. Each substep consists of approaching the object, selecting posture and grip force, grasping, transporting to the desired position, and releasing. The graphical user interface (GUI) displays the current substep and simple symbols beside each flickering block for quick understanding. The tele-grasping of various objects by using real-time human decisions of selecting among four possible postures and three levels of grip force has been demonstrated. This system can be adapted to other sequential EEG-controlled teleoperation tasks that require complex human decisions.

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“…Modern palletizing robots acquire trajectory parameters by human–computer interaction [ 9 , 10 , 11 ]. Moreover, different from traditional methods in optimizing objectives [ 12 ], modern methods combine sensors to optimize energy sources directly [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

Deng

Zhou

Zhao

et al. 2022

Sensors

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Energy saving in palletizing robot is a fundamental problem in the field of industrial robots. However, the palletizing robot often suffers from the problems of high energy consumption and lacking flexibility. In this work, we introduce a novel differential evolution algorithm to address the adverse effects caused by the instability of the initial trajectory parameters while reducing the energy. Specially, a simplified analytical model of the palletizing robot is firstly developed. Then, the simplified analytical model and the differential evolutionary algorithm are combined to form a planner with the goal of reducing energy consumption. The energy saving planner optimizes the initial parameters of the trajectories collected by the bionic demonstration system, which in turn enables a reduction in the operating power consumption of the palletizing robot. The major novelty of this article is the use of a differential evolutionary algorithm that can save the energy consumption as well as boosting its flexibility. Comparing with the traditional algorithms, the proposed method can achieve the state-of-the-art performance. Simulated and actual experimental results illustrate that the optimized trajectory parameters can effectively reduce the energy consumption of palletizing robot by 16%.

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A Wearable IMU System for Flexible Teleoperation of a Collaborative Industrial Robot

Cited by 24 publications

References 32 publications

Robotics Perception and Control: Key Technologies and Applications

Robotics Perception and Control: Key Technologies and Applications

EEG-controlled tele-grasping for undefined objects

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

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