In this paper we propose a kinematic approach for tracked mobile robots in order to improve motion control and pose estimation. Complex dynamics due to slippage and track-soil interactions make it difficult to predict the exact motion of the vehicle on the basis of track velocities. Nevertheless, real-time computations for autonomous navigation require an effective kinematics approximation without introducing dynamics in the loop. The proposed solution is based on the fact that the instantaneous centers of rotation (ICRs) of treads on the motion plane with respect to the vehicle are dynamics-dependent, but they lie within a bounded area. Thus, optimizing constant ICR positions for a particular terrain results in an approximate kinematic model for tracked mobile robots. Two different approaches are presented for off-line estimation of kinematic parameters: (i) simulation of the stationary response of the dynamic model for the whole velocity range of the vehicle; (ii) introduction of an experimental setup so that a genetic algorithm can produce the model from actual sensor readings. These methods have been evaluated for on-line odometric computations and low-level motion control with the Auriga-α mobile robot on a hard-surface flat soil at moderate speeds.

Power consumption is a key element in outdoor mobile robot autonomy. This issue is very relevant in skid-steer tracked vehicles on account of their large ground contact area. In this paper, the power losses due to dynamic friction have been modeled from two different perspectives: 1) the power drawn by the rigid terrain and 2) the power supplied by the motors. Comparison of both approaches has provided new insight on skid steering on hard flat terrains at walking speeds. Experimental power models, which also include traction resistance and other power losses, have been obtained for two different track widths over marble flooring and asphalt with Auriga-β, which is a full-size mobile robot. To this end, various internal probes have been set at different points of the power stream. Furthermore, new energy implications for navigation of these kinds of vehicles have been deduced and tested.

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Abslr4cf -The paper proposes a ldnematic approach for tracked vehicles in order to improve motion control and pose estimation. Complex dynamics due to slippage and soil shearing make it difficult to predict the exact motion of the vehicle from the velocity of the two backs. Nevertheless, reliable geomeMc approxlmations are necessary to perform onboard real-time computations for autonomous navigation. The presented solution is based on the ldnematic similarities between tracked vehicles and wheeled differential drive vehicles. Particularly, the approximate position of wheel contact paints for am equivalent vehicle c m be opiimlzed for a particular terrain at moderate speeds. This is achieved off-line by feeding a genetic algorithm with raw trajectoIy data and reliable localization estimations based on external senson. The method bas been successfully tested for online odometric computations m d low-level motion control with the Aurigaa mobile robot Moreover, tbe identilied parameters are similar to those obtained from tbe simulated stationary response of a complex dynamic model of tbh vehlcle.

In this paper, a low computational cost method for terrestrial mobile robots that uses a laser scanner far following mobile objects and uvoiding obstacles is presented. In particular, the technique has been successfully implemented in the outdoor mobile robot Auriga-a. The measurement data from the laser scanner and the vehicle position are used as input variables. The outputs are the new curvature and velocity of the robot in order to follow a mobile object, or track U previously recorded path and avoid any possible obstucle in its way.