## Article
### 1 Introduction
This work confirms that haptic feedbacksincrease the operator's accuracy by reducing the errors in the desired trajectory of the end effector, decreasing the collisions with the environment, and reducing the operation time. In ref. [10], a haptic system based on distances measured using a stereo camera, is evaluated on a robotic arm used to transport an object without colliding with obstacles. In ref. [11], a 3D virtual environment is recreated by a Kinect sensor used to calculate haptic forces based on potential fields. The paper evaluates the path generated when operating a robot arm with 7 degrees of freedom. This paper is organized as follows. In Section 2, the proposed haptic tele-operation of impact hammers is described. Section 3 presents results, both in simulation and in reality, which are discussed in Section 4. Finally, in Section 5, conclusions of this work are drawn. 2. 2. During the system's initialization, the environment model is generated by successively integrating point clouds from the environment, without considering the ones corresponding to material or the impact hammer. This results in a very dense point cloud that is first filtered for deleting isolated points that could be generated by noise on the sensors. Once the noise has been eliminated, the cloud is voxelized in 3D square voxels, reducing the number of point and maintaining the occupancy information of the environment in the space. Finally, this cloud is saved as the environment model. Figure 5 shows the main modules of the described process. Sensors 1 and 2 correspond to the 3D LIDARs used to capture the model of the environment through point clouds. The _Point Cloud Integration_ module corresponds to the process of joining the point clouds of both sensors and performing the task of integrating these successive point clouds over time. The _Noise Filtering_ module is responsible for removing noise. The _Voxelization & Occupancy Grid_ module is in charge of voxelizing the point cloud in order to eliminate repeated points and to show the occupancy of the space. And finally, the _Save Model in Memory_ module stores the resulting point cloud in memory for later use. During operation, the environment model is used for determining the points that belong to the material model and hammer model. In each iteration, each new point cloud is voxelized and contrasted with the point cloud of the environment model. This comparison allows to subtract the points belonging to the environment. Once subtracted from the environment points, the resulting point cloud contain the hammer-points and material-points. Hence, to obtain the material-points, a new filtering process should be performed to subtract the hammer-points. This is done by first estimating the spatial region where the hammer is located, which is obtained using the current state of the hammer's joints and the kinematic model of the hammer. This way, all those points within this space will be classified as belonging to the hammer and therefore eliminated. Figure 6 shows the process used to obtain the point cloud of the material. Sensors 1 and 2 correspond to the 3D LIDARs used to capture the working environment. The _Point Cloud Concatenation_ module is in charge of joining the point clouds coming from both sensors. The _Voxelization_ module is in charge of voxelizing the point cloud resulting from the union of both sensors, thus eliminating repeated points. The _Environment Model Points_ corresponds to the point cloud stored in memory, which only contains points corresponding to the grizzly. The _Subtraction of Environment Points_ module compares the current point cloud with the point cloud stored in memory, and removes the points that are found in both models. Finally, the _Material Points_ module is in charge of publishing the points resulting from the previous module, which correspond to the material on the grizzly. Figure 5: Processes to obtain the point cloud of the environment.