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A new approach is proposed for an adaptive robust three‐dimensional (3D) trajectory‐tracking controller design. The controller is modeled for actively articulated tracked vehicles (AATVs). These vehicles have active sub‐tracks, called flippers, linked to the ends of the main tracks, to extend the locomotion capabilities in hazardous environments, such as rescue scenarios. The proposed controller adapts...
We present a real time method for updating a 3D map with dynamic obstacles detection. Moving obstacles are detected through ray-casting on spherical voxelization of point clouds. We evaluate the accuracy of this method on a point cloud dataset, suitably constructed for testing ray-surface intersection under relative motion conditions. Moreover, we show the benefits of the map updating in a real robot...
The paper presents a novel framework for 3D autonomous navigation for tracked vehicles. The framework takes care of clustering and segmentation of point clouds, traversability analysis, autonomous 3D path planning, motion planning and flippers control. Results illustrated in an experiment section show that the framework is promising to face harsh terrains. Robot performance is proved in three main...
Modeling cognitive control is a major issue in robot control, and it is about deciding when a task cannot succeed and a new task need to be initiated. These decisions are induced by incoming stimuli alerting of events taking place while the robot is executing its duties. To learn cognitive control we address the human inspired mechanisms that govern cognitive control and that have been widely studied...
In this paper we propose a framework for trajectory planning and control of tracked vehicles for rescue environments, based on Augmented Reality (AR). The framework provides the human operator with an AR-based interface that facilitates both 3D path planning and obstacle negotiation. The interface converts the 3D movements of a marker pen, handheld by the operator, into trajectories feasible for the...
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