A novel shared control algorithm for industrial robots

Ramacciotti, Marco; Milazzo, Mario; Leoni, Fabio; Roccella, S.; Stefanini, Cesare

doi:10.1177/1729881416682701

Cited by 13 publications

(5 citation statements)

References 33 publications

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“…Considering how to share autonomy between a human and robot is an important aspect to ensuring effective HRI [156]. It can help to reduce an operator's workload, allow both inexperienced and professional operators to control the system [86,156].…”

Section: Sharing Autonomy Between Users and Expressive Robotsmentioning

confidence: 99%

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Pourebadi

Riek

2022

ACM Trans. Comput. Healthcare

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Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.

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Section: Sharing Autonomy Between Users and Expressive Robotsmentioning

confidence: 99%

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Pourebadi

Riek

2022

ACM Trans. Comput. Healthcare

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“…Several papers explore sub-aspects of the stated problem. For example, in a recent paper Ramacciotti et al (Ramacciotti et al, 2016) explore shared control for motion speed and trajectory adaptation. However, to the best of our knowledge, a complete approach that fulfills the given problem statement, has not been proposed yet.…”

Section: Problem Statementmentioning

confidence: 99%

Human robot cooperation with compliance adaptation along the motion trajectory

et al. 2017

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In this paper we propose a novel approach for intuitive and natural physical human-robot interaction in cooperative tasks. Through initial learning by demonstration, robot behavior naturally evolves into a cooperative task, where the human co-worker is allowed to modify both the spatial course of motion as well as the speed of execution at any stage. The main feature of the proposed adaptation scheme is that the robot adjusts its stiffness in path operational space, defined with a Frenet-Serret frame. Furthermore, the required dynamic capabilities of the robot are obtained by decoupling the robot dynamics in operational space, which is attached to the desired trajectory. Speed-scaled dynamic motion primitives are applied for the underlying task representation. The combination allows a human co-worker in a cooperative task to be less precise in parts of the task that require high precision, as the precision aspect is learned and provided by the robot. The user can also freely change the speed and/or the trajectory by simply applying force to the robot. The proposed scheme was experimentally validated on three illustrative tasks. The first task demonstrates novel twostage learning by demonstration, where the spatial part of the trajectory is demonstrated independently from the velocity part. The second task shows how parts of the trajectory can be rapidly and significantly changed in one execution. The final experiment shows two Kuka LWR-4 robots in a bi-manual setting cooperating with a human while carrying an object.

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“…Based on a demonstration, Pérez-del-Pulgar et al ( 2016 ) introduced a forced control method to provide guidance and feedback during a peg-in-hole insertion task. Considering the improved complexity in human management, Ramacciotti et al ( 2016 ) presented a shared control method to compensate and couple human intelligence for industrial robots. In human–robot cooperation, the robots cannot ignore the influence of an obstacle and communication delay.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Shared Control Scheme Based on Intention Recognition for Flexible Assembly Manufacturing

Zhou

Luo

et al. 2022

Front. Neurorobot.

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Human–robot collaboration (HRC) has been widely utilized in industrial manufacturing and requires a human to cooperate with a robot at the same workspace. However, as HRC focuses on workspace sharing along with independent work, it is not a real collaboration between a human and a robot and, thus, cannot guarantee a smooth cooperation and synchronous operation. To this end, a cooperative shared control scheme based on intention recognition is proposed in this study by sharing workspace and time. In the proposed method, a classification algorithm based on three-dimensional (3D) point cloud is utilized to recognize the human operation intention. Then, the robot can select a suitable tool to match the human's operation. A robot motion control algorithm is developed to detect the obstacles in the HRC process. A cooperative control strategy is introduced to achieve synchronous operation. A simple assembly task is also performed to demonstrate the proposed scheme's effectiveness. The proposed HRC method with shared control can be extended to more complicated and delicate flexible tasks in assembly manufacturing.

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A novel shared control algorithm for industrial robots

Cited by 13 publications

References 33 publications

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Human robot cooperation with compliance adaptation along the motion trajectory

A Cooperative Shared Control Scheme Based on Intention Recognition for Flexible Assembly Manufacturing

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