Neural Configuration Distance Function for Continuum Robot Control
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916632406786048 |
|---|---|
| author | Long, Kehan Parwana, Hardik Fainekos, Georgios Hoxha, Bardh Okamoto, Hideki Atanasov, Nikolay |
| author_facet | Long, Kehan Parwana, Hardik Fainekos, Georgios Hoxha, Bardh Okamoto, Hideki Atanasov, Nikolay |
| contents | This paper presents a novel method for modeling the shape of a continuum robot as a Neural Configuration Euclidean Distance Function (N-CEDF). By learning separate distance fields for each link and combining them through the kinematics chain, the learned N-CEDF provides an accurate and computationally efficient representation of the robot's shape. The key advantage of a distance function representation of a continuum robot is that it enables efficient collision checking for motion planning in dynamic and cluttered environments, even with point-cloud observations. We integrate the N-CEDF into a Model Predictive Path Integral (MPPI) controller to generate safe trajectories for multi-segment continuum robots. The proposed approach is validated for continuum robots with various links in several simulated environments with static and dynamic obstacles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Configuration Distance Function for Continuum Robot Control Long, Kehan Parwana, Hardik Fainekos, Georgios Hoxha, Bardh Okamoto, Hideki Atanasov, Nikolay Robotics Systems and Control This paper presents a novel method for modeling the shape of a continuum robot as a Neural Configuration Euclidean Distance Function (N-CEDF). By learning separate distance fields for each link and combining them through the kinematics chain, the learned N-CEDF provides an accurate and computationally efficient representation of the robot's shape. The key advantage of a distance function representation of a continuum robot is that it enables efficient collision checking for motion planning in dynamic and cluttered environments, even with point-cloud observations. We integrate the N-CEDF into a Model Predictive Path Integral (MPPI) controller to generate safe trajectories for multi-segment continuum robots. The proposed approach is validated for continuum robots with various links in several simulated environments with static and dynamic obstacles. |
| title | Neural Configuration Distance Function for Continuum Robot Control |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2409.13865 |