https://github.com/ami-iit/paper_rapetti_2023_icra_ergonomic_payload_lifting
Repository associated with the paper "A Control Approach for Human-Robot Ergonomic Payload Lifting", published in IEEE ICRA 2023.
https://github.com/ami-iit/paper_rapetti_2023_icra_ergonomic_payload_lifting
Science Score: 49.0%
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Repository
Repository associated with the paper "A Control Approach for Human-Robot Ergonomic Payload Lifting", published in IEEE ICRA 2023.
Basic Info
- Host: GitHub
- Owner: ami-iit
- Language: MATLAB
- Default Branch: main
- Size: 567 KB
Statistics
- Stars: 7
- Watchers: 3
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
A Control Approach for Human-Robot
Ergonomic Payload Lifting
https://github.com/ami-iit/paperrapetti2023icraergonomicpayloadlifting/assets/35487806/6af0b2d7-571b-46d3-bc99-665455f33f1b
Abstract
Collaborative robots can relief human operators from excessive efforts during payload lifting activities. Modelling the human partner allows the design of safe and efficient collaborative strategies. In this paper, we present a control approach for human-robot collaboration based on human monitoring through whole-body wearable sensors, and interaction modelling through coupled rigid-body dynamics. Moreover, a trajectory advancement strategy is proposed, allowing for online adaptation of the robot trajectory depending on the human motion. The resulting framework allows us to perform payload lifting tasks, taking into account the ergonomic requirements of the agents. Validation has been performed in an experimental scenario using the iCub3 humanoid robot and a human subject sensorized with the iFeel wearable system.
Reproducing the experiments
The instruction to install the required software can be found in INSTALL documentation. Following the instruction, you will be able to use: - Gazebo models for collaborative tasks - Visualizer for multi-agent interaction - Simulink controllers for human-robot collaboration
Human models can be generated using the human-model-generator python tool.
Citing this work
If you find the work useful, please consider citing:
bibtex
@INPROCEEDINGS{rapetti2023control,
author={Rapetti, Lorenzo and Sartore, Carlotta and Elobaid, Mohamed and Tirupachuri, Yeshasvi and Draicchio, Francesco and Kawakami, Tomohiro and Yoshiike, Takahide and Pucci, Daniele},
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
title={A Control Approach for Human-Robot Ergonomic Payload Lifting},
year={2023},
pages={7504-7510},
doi={10.1109/ICRA48891.2023.10161454}}
Maintainer
This repository is maintained by:
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| @lrapetti |
Owner
- Name: Artificial and Mechanical Intelligence
- Login: ami-iit
- Kind: organization
- Location: Italy
- Website: https://ami.iit.it/
- Repositories: 111
- Profile: https://github.com/ami-iit
GitHub Events
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Last Year
- Watch event: 3
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Last synced: 10 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Lorenzo Rapetti | l****i@g****m | 18 |
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