A planning framework for robotic insertion tasks via hydroelastic contact model
Robotic contact-rich insertion tasks present a significant challenge for motion planning due to the complex force interaction between robots and objects. Although many learning-based methods have shown success in contact tasks, most methods need sampling or exploring to gather sufficient experimenta...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/171062 |
| _version_ | 1826111052002099200 |
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| author | Yang, Lin Mohammad Zaidi Ariffin Lou, Baichuan Lv, Chen Campolo, Domenico |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Yang, Lin Mohammad Zaidi Ariffin Lou, Baichuan Lv, Chen Campolo, Domenico |
| author_sort | Yang, Lin |
| collection | NTU |
| description | Robotic contact-rich insertion tasks present a significant challenge for motion planning due to the complex force interaction between robots and objects. Although many learning-based methods have shown success in contact tasks, most methods need sampling or exploring to gather sufficient experimental data. However, it is both time-consuming and expensive to conduct real-world experiments repeatedly. On the other hand, while the virtual world enables low cost and fast computations by simulators, there still exists a huge sim-to-real gap due to the inaccurate point contact model. Although finite element analysis might generate accurate results for contact tasks, it is computationally expensive. As such, this study proposes a motion planning framework with bilevel optimization to leverage relatively accurate force information with fast computation time. This framework consists of Dynamic Movement Primitives (DMPs) used to parameterize motion trajectories, Black-Box Optimization (BBO), a derivative-free approach, integrated to improve contact-rich insertion policy with hydroelastic contact model, and simulated variability to account for visual uncertainty in the real world. The accuracy of the simulated model is then validated by comparing our contact results with a benchmark Peg-in-Hole task. Using these integrated DMPs and BBO with hydroelastic contact model, the motion trajectory generated in planning is capable of guiding the robot towards successful insertion with iterative refinement. |
| first_indexed | 2024-10-01T02:44:18Z |
| format | Journal Article |
| id | ntu-10356/171062 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:44:18Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1710622023-10-14T16:47:57Z A planning framework for robotic insertion tasks via hydroelastic contact model Yang, Lin Mohammad Zaidi Ariffin Lou, Baichuan Lv, Chen Campolo, Domenico School of Mechanical and Aerospace Engineering Robotics Research Center Engineering::Mechanical engineering Peg-in-Hole Assembly Motion Planning Robotic contact-rich insertion tasks present a significant challenge for motion planning due to the complex force interaction between robots and objects. Although many learning-based methods have shown success in contact tasks, most methods need sampling or exploring to gather sufficient experimental data. However, it is both time-consuming and expensive to conduct real-world experiments repeatedly. On the other hand, while the virtual world enables low cost and fast computations by simulators, there still exists a huge sim-to-real gap due to the inaccurate point contact model. Although finite element analysis might generate accurate results for contact tasks, it is computationally expensive. As such, this study proposes a motion planning framework with bilevel optimization to leverage relatively accurate force information with fast computation time. This framework consists of Dynamic Movement Primitives (DMPs) used to parameterize motion trajectories, Black-Box Optimization (BBO), a derivative-free approach, integrated to improve contact-rich insertion policy with hydroelastic contact model, and simulated variability to account for visual uncertainty in the real world. The accuracy of the simulated model is then validated by comparing our contact results with a benchmark Peg-in-Hole task. Using these integrated DMPs and BBO with hydroelastic contact model, the motion trajectory generated in planning is capable of guiding the robot towards successful insertion with iterative refinement. National Research Foundation (NRF) Published version This research is supported by the National Research Foundation, Singapore, under the NRF Medium Sized Centre scheme (CARTIN). 2023-10-11T01:34:50Z 2023-10-11T01:34:50Z 2023 Journal Article Yang, L., Mohammad Zaidi Ariffin, Lou, B., Lv, C. & Campolo, D. (2023). A planning framework for robotic insertion tasks via hydroelastic contact model. Machines, 11(7), 741-. https://dx.doi.org/10.3390/machines11070741 2075-1702 https://hdl.handle.net/10356/171062 10.3390/machines11070741 2-s2.0-85166270558 7 11 741 en Machines © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
| spellingShingle | Engineering::Mechanical engineering Peg-in-Hole Assembly Motion Planning Yang, Lin Mohammad Zaidi Ariffin Lou, Baichuan Lv, Chen Campolo, Domenico A planning framework for robotic insertion tasks via hydroelastic contact model |
| title | A planning framework for robotic insertion tasks via hydroelastic contact model |
| title_full | A planning framework for robotic insertion tasks via hydroelastic contact model |
| title_fullStr | A planning framework for robotic insertion tasks via hydroelastic contact model |
| title_full_unstemmed | A planning framework for robotic insertion tasks via hydroelastic contact model |
| title_short | A planning framework for robotic insertion tasks via hydroelastic contact model |
| title_sort | planning framework for robotic insertion tasks via hydroelastic contact model |
| topic | Engineering::Mechanical engineering Peg-in-Hole Assembly Motion Planning |
| url | https://hdl.handle.net/10356/171062 |
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