Abstract
In this work is developed an architecture which consists of four main components: perception system, tasks planning, motion planner, and control systems that allow autonomous operations in backhoe machines. In the first part is described the architecture of control system. A set of techniques for collision mapping of the scene is described and implemented. Moreover, a motion planning system based on Learning from Demonstration using Dynamic Movement Primitives as control policy is proposed, which allows backhoe machines to perform operations in autonomous manner. A statement of reasons is presented, wherein we justified the implementation of such motion system versus planners like \(\text {A}^*\), Probabilistic RoadMap (PRM), Rapidly-exploring Random Tree (RRT), etc. In addition, we present the performance of the architecture in a simulation environment.
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Notes
- 1.
The dynamic of hydraulic actuators have not been consider in this work.
- 2.
The codification space of imitation learning approach \(\begin{bmatrix} x_e&y_e&z_e&\psi _e\end{bmatrix}^T\).
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Mastalli, C., Fernández-López, G. (2016). A Proposed Architecture for Autonomous Operations in Backhoe Machines. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_111
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