Autonomous Tracked Robots in Planar Off-Road Conditions: by Ramón González, Francisco Rodríguez, José Luis Guzmán

By Ramón González, Francisco Rodríguez, José Luis Guzmán

This monograph is framed in the context of off-road cellular robotics. particularly, it discusses matters regarding modelling, localization, and movement keep watch over of tracked cellular robots operating in planar slippery stipulations. Tracked locomotion constitutes a widely known resolution for cellular systems working over different hard terrains, consequently, tracked robotics constitutes a massive examine box with many functions (e.g. agriculture, mining, seek and rescue operations, army activities). the explicit issues of this monograph are: historic viewpoint of tracked cars and tracked robots; trajectory-tracking version considering slip impact; visual-odometry-based localization ideas; and complicated slip-compensation movement controllers making sure effective real-time execution. actual experiments with a true tracked robotic are awarded displaying the higher functionality of the recommended novel techniques to recognized options.

keyword phrases: longitudinal slip, visible odometry, slip-compensation keep an eye on, powerful predictive keep watch over, trajectory tracking.

comparable matters: Robotics – Mechanical Engineering – Mechanics – laptop technological know-how – synthetic Intelligence - Applications

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Extra resources for Autonomous Tracked Robots in Planar Off-Road Conditions: Modelling, Localization, and Motion Control

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This phenomenon constitutes a difficult issue to be removed and elaborate solutions have to be considered to minimize its influence. For instance, in [55], authors formulate a learning policy as a trade-off between the localization accuracy and robot velocity. In [110], authors propose to carry out a preprocessing step before detecting features in the image. In this book, a preprocessing of the images, such as an enhancing filter, is not appropriate due to this would mean to raise the computation time assigned to the vision algorithm.

9) where [xvo y vo ]T ∈ R2 is the robot position. The estimation of the robot orientation (θvo ∈ R) is addressed in the following subsection. Remark 5. Notice that the robot orientation can be calculated using the information from the camera pointing at the ground. 2). Then, the orientation at each sampling instant is given by ˆ θˆvo (k) = θˆvo (k − 1) + Δθ(k). 11) However, the resulting orientation is extremely sensitive to systematic errors, such as inaccurate distance between the camera and the ground plane, inaccurate distance between the camera and the centre of the robot, and false matches.

The top left corner of the new image is W inqw , λq q W inh W inq (v) = W inqh − . 18) In this way, the computation time is decreased, since correlation process is carried out over a smaller image, such as shown in the following subsection. Effect of Template and Image Sizes on the Computation Time Before carrying out physical experiments, the effect of the template and image sizes on the computation time has been analyzed. 3). 5 GB RAM. 4 shows the resulting computation time of varying the template and image sizes (“Mean” is the mean computation time of the sequence of images and “Std” denotes the standard deviation).

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