MPC-based Coarse-to-Fine Motion Planning for Robotic Object Transportation in Cluttered Environments
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916844644859904 |
|---|---|
| author | Cai, Chen Saraiva, Ernesto Dickel Pan, Ya-jun Liu, Steven |
| author_facet | Cai, Chen Saraiva, Ernesto Dickel Pan, Ya-jun Liu, Steven |
| contents | This letter presents a novel coarse-to-fine motion planning framework for robotic manipulation in cluttered, unmodeled environments. The system integrates a dual-camera perception setup with a B-spline-based model predictive control (MPC) scheme. Initially, the planner generates feasible global trajectories from partial and uncertain observations. As new visual data are incrementally fused, both the environment model and motion planning are progressively refined. A vision-based cost function promotes target-driven exploration, while a refined kernel-perceptron collision detector enables efficient constraint updates for real-time planning. The framework accommodates closed-chain kinematics and supports dynamic replanning. Experiments on a multi-arm platform validate its robustness and adaptability under uncertainties and clutter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MPC-based Coarse-to-Fine Motion Planning for Robotic Object Transportation in Cluttered Environments Cai, Chen Saraiva, Ernesto Dickel Pan, Ya-jun Liu, Steven Robotics Systems and Control This letter presents a novel coarse-to-fine motion planning framework for robotic manipulation in cluttered, unmodeled environments. The system integrates a dual-camera perception setup with a B-spline-based model predictive control (MPC) scheme. Initially, the planner generates feasible global trajectories from partial and uncertain observations. As new visual data are incrementally fused, both the environment model and motion planning are progressively refined. A vision-based cost function promotes target-driven exploration, while a refined kernel-perceptron collision detector enables efficient constraint updates for real-time planning. The framework accommodates closed-chain kinematics and supports dynamic replanning. Experiments on a multi-arm platform validate its robustness and adaptability under uncertainties and clutter. |
| title | MPC-based Coarse-to-Fine Motion Planning for Robotic Object Transportation in Cluttered Environments |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2507.11211 |