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. specifically, it discusses matters concerning 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 tough terrains, for this reason, tracked robotics constitutes a huge examine box with many functions (e.g. agriculture, mining, seek and rescue operations, army activities). the categorical themes of this monograph are: old standpoint of tracked automobiles and tracked robots; trajectory-tracking version considering slip influence; visual-odometry-based localization concepts; and complicated slip-compensation movement controllers making sure effective real-time execution. actual experiments with a true tracked robotic are offered displaying the higher functionality of the instructed novel methods to identified options.

key terms: longitudinal slip, visible odometry, slip-compensation regulate, strong predictive keep an eye on, trajectory tracking.

similar matters: Robotics – Mechanical Engineering – Mechanics – laptop technological know-how – man made Intelligence - Applications

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

Example text

Squared Trajectory In this experiment, the robot was manually driven on a sunlit illuminated gravel terrain following a squared trajectory. The lighting conditions did not produce any significant shadows during the experiment in this case. 7 shows two frames employed by the vision-based localization technique during this experiment. 5). (a) Panoramic view (b) Ground view Fig. 8a shows the resulting trajectories. 3 Results 51 particularly, odometry fails at turns. The trajectory obtained using the image sequence from the camera pointing at the ground (referred to as “VO”) to estimate orientation (see Remark 5) is also plotted, and it has a similar result to that obtained using the approach combining information from both cameras (visual odometry with visual compass).

Notice that in order to validate the trajectories obtained with the EKM and the CKM, the global position (Latitude/Longitude) has been converted to Universal Transverse Mercator (UTM) grid system [51], and hence to relative position from the starting point. 70 [s] for the Doppler radar readings. 7a. 7b. 7c shows a comparison of the trajectories obtained using the kinematic models. In this case, the trajectory obtained using the EKM closely follows to the ground-truth obtained from DGPS. 78 [m].

Where images with few high gradients are grabbed. On the other hand, the template matching approach works properly in low texture scenarios, since a larger pattern (template) is employed, and, therefore, the probability of a successful matching is increased [73]. Previous discussion motivates why the template matching method has been selected in this book. However, it is important to remark that if the matching process fails (false matches), the robot motion estimate can become degraded. In order to minimize this shortcoming, especially undesirable estimating robot orientation, a second camera is added.

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