Wallbounce : Push wall to navigate with Contact-Implicit MPC
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866909376188514304 |
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| author | Liu, Xiaohan Dai, Cunxi Zhang, John Z. Bishop, Arun Manchester, Zachary Hollis, Ralph |
| author_facet | Liu, Xiaohan Dai, Cunxi Zhang, John Z. Bishop, Arun Manchester, Zachary Hollis, Ralph |
| contents | In this work, we introduce a framework that enables highly maneuverable locomotion using non-periodic contacts. This task is challenging for traditional optimization and planning methods to handle due to difficulties in specifying contact mode sequences in real-time. To address this, we use a bi-level contact-implicit planner and hybrid model predictive controller to draft and execute a motion plan. We investigate how this method allows us to plan arm contact events on the shmoobot, a smaller ballbot, which uses an inverse mouse-ball drive to achieve dynamic balancing with a low number of actuators. Through multiple experiments we show how the arms allow for acceleration, deceleration and dynamic obstacle avoidance that are not achievable with the mouse-ball drive alone. This demonstrates how a holistic approach to locomotion can increase the control authority of unique robot morpohologies without additional hardware by leveraging robot arms that are typically used only for manipulation. Project website: https://cmushmoobot.github.io/Wallbounce |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01387 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Wallbounce : Push wall to navigate with Contact-Implicit MPC Liu, Xiaohan Dai, Cunxi Zhang, John Z. Bishop, Arun Manchester, Zachary Hollis, Ralph Robotics In this work, we introduce a framework that enables highly maneuverable locomotion using non-periodic contacts. This task is challenging for traditional optimization and planning methods to handle due to difficulties in specifying contact mode sequences in real-time. To address this, we use a bi-level contact-implicit planner and hybrid model predictive controller to draft and execute a motion plan. We investigate how this method allows us to plan arm contact events on the shmoobot, a smaller ballbot, which uses an inverse mouse-ball drive to achieve dynamic balancing with a low number of actuators. Through multiple experiments we show how the arms allow for acceleration, deceleration and dynamic obstacle avoidance that are not achievable with the mouse-ball drive alone. This demonstrates how a holistic approach to locomotion can increase the control authority of unique robot morpohologies without additional hardware by leveraging robot arms that are typically used only for manipulation. Project website: https://cmushmoobot.github.io/Wallbounce |
| title | Wallbounce : Push wall to navigate with Contact-Implicit MPC |
| topic | Robotics |
| url | https://arxiv.org/abs/2411.01387 |