Abstract / Description of output
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 |
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Title of host publication | Adaptive Mobile Robotics |
Subtitle of host publication | Proceedings of the Fifteenth International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines |
Publisher | World Scientific |
Pages | 821-830 |
Number of pages | 10 |
ISBN (Electronic) | 978-981-4414-96-5 |
ISBN (Print) | 978-981-4415-94-1 |
DOIs | |
Publication status | Published - 30 Sept 2012 |
Event | 15th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines - Baltimore, 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 |
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Abbreviated title | CLAWAR 2012 |
Country/Territory | United States |
City | Baltimore |
Period | 23/07/12 → 26/07/12 |
Keywords / Materials (for Non-textual outputs)
- Imitation Learning
- Dynamical Movement Primitives
- ExcavatingRobots
- Backhoe Machine