Virtual Potential Field-Based Motion Planning for Human-Robot Collaboration via Kinesthetically Guided Teleoperation

The advent of collaborative and soft robotics has reduced the mandatory adoption of safety barriers, pushing human–robot interaction to previously unreachable levels. Due to their reciprocal advantages, integrating these technologies can maximize a device’s performance. However, simplifying assumptions or elementary geometries are often required due to non-linear factors that identify analytical models for designing soft pneumatic actuators for collaborative and soft robotics. Over time, various approaches have been employed to overcome these issues, including finite element analysis, response surface methodology (RSM), and machine learning (ML) algorithms. Based on the latter, in this study, the bending behavior of an externally reinforced soft pneumatic actuator was characterized by the changing geometric and functional parameters, realizing a Bend dataset. This was used to train 14 regression algorithms, and the Bilayered neural network (BNN) was the best. Three different external reinforcements, excluded for the realization of the dataset, were tested by comparing the predicted and experimental bending angles. The BNN demonstrated significantly lower error than that obtained by RSM, validating the methodology and highlighting how ML techniques can advance the prediction and mechanical design of soft pneumatic actuators.

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Study of Robot Manipulator Control via Remote Method

Tuan Nguyen

2023

EAI Endorsed Trans Context Aware Syst App

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INTRODUCTION: The study introduces a novel approach to the design and management of industrial robots using virtual reality technology, enabling humans to observe a wide range of robot behaviors across various environments.OBJECTIVES: Through a simulation program, the robot's movements can be reviewed, and a program for real-world task execution can be generated. Furthermore, the research delves into the algorithm governing the interaction between the industrial robot and humans.METHODS: The robot utilized in this research project has been meticulously refurbished and enhanced from the previously old version robotic manipulator, which lacked an electrical cabinet derived.RESULTS: Following the mechanical and electrical upgrades, a virtual setup, incorporating a headset and two hand controllers, has been integrated into the robot's control system, enabling control via this device.CONCLUSION: This control algorithm leverages a shared control approach and artificial potential field methods to facilitate obstacle avoidance through repulsive and attractive forces. Ultimately, the study presents experimental results using the real robot model.

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Virtual Potential Field-Based Motion Planning for Human-Robot Collaboration via Kinesthetically Guided Teleoperation

Cited by 3 publications

References 33 publications

Revolutionizing Transportation: Advancements in Robot-Assisted Mobility Systems

Revolutionizing Transportation: Advancements in Robot-Assisted Mobility Systems

A Methodology for the Mechanical Design of Pneumatic Joints Using Artificial Neural Networks

Study of Robot Manipulator Control via Remote Method

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