Abstract
This paper presents a motion planning and control system architecture development for autonomous earthmoving operations in excavating machines such as loading a dump truck. The motion planning system is imitation learning based, which is a general approach for learning motor skills from human demonstration. This scheme of supervised learning is based on a dynamical movement primitives (DMP) as control policies (CP). The DMP is a non-linear differential equation that encode movements, which are used to learn tasks in backhoe machines. A general architecture to achieve autonomous truck loading operations is described. Also, the effectiveness of our approach for truck loading task is demonstrated, where the machine can adapt to different operating scenarios.
| Original language | English |
|---|---|
| Title of host publication | Adaptive Mobile Robotics |
| Subtitle of host publication | Proceedings of the 15th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines |
| Publisher | World Scientific Publishing |
| Pages | 821-830 |
| Number of pages | 10 |
| ISBN (Electronic) | 9789814415965 |
| ISBN (Print) | 9789814415941 |
| DOIs | |
| Publication status | Published - Sept 2012 |
| Event | 15th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines 2012 - Baltimore, MD, United States Duration: 23 Jul 2012 → 26 Jul 2012 |
Conference
| Conference | 15th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines 2012 |
|---|---|
| Abbreviated title | CLAWAR 2012 |
| Country/Territory | United States |
| City | Baltimore, MD |
| Period | 23/07/12 → 26/07/12 |
Keywords
- Backhoe machine
- Dynamical movement primitives
- Excavating robots
- Imitation learning
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction