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This paper presents a comprehensive wheel model that can quantitatively evaluate traction performance of flexible/rigid wheelsdriving on deformable terrain. The proposed model exploits a terramechanics-based approach with taking account of pressures generated by wheel elasticity as well as terrain stiffness. Deflection of a flexible wheel typically depends on a relative pressure between thewheel and terrain: the wheel will be significantly deformed on rigid terrain whereas it will be hardly deformed on soft terrain. Therefore, the wheel-terrain interaction in the proposed model is divided into three contact sections: wheel front section, wheel deflected(flat) section, and wheel rear section. The traction force of the wheel is obtained as an integral of normal and shear stresses generatedat each section. Simulation studies with varied wheel pressures, such as flexible, semi-flexible, and rigid wheels, are conducted tovalidate the proposed model. Also, traction performances of flexible/rigid wheels are compared based on a metric called tractiveefficiency. The comparison implies an optimal wheel pressure of flexible wheel for better traction performance.

This paper introduces a novel method for slip angle estimation based on visually observing the traces produced by the wheels of a robot on soft, deformable terrain. The proposed algorithm uses a robust Hough transform enhanced by fuzzy reasoning to estimate the angle of inclination of the wheel trace with respect to the vehicle reference frame. Any deviation of the wheel track from the planned path of the robot suggests occurrence of sideslip that can be detected and, more interestingly, measured. In turn, the knowledge of the slip angle allows encoder readings affected by wheel slip to be adjusted and the accuracy of the position estimation system to be improved, based on an integrated longitudinal and lateral wheel–terrain slip model. The description of the visual algorithm and the odometry correction method is presented, and a comprehensive set of experimental results is included to validate this approach.

For a mobile robot it is critical to detect and compensate for slippage, especially when driving in rough terrain environments. Due to its highly unpredictable nature, drift largely affects the accuracy of localization and control systems, even leading, in extreme cases, to the danger of vehicle entrapment with consequent mission failure. This paper presents a novel method for lateral slip estimation based on visually observing the trace produced by the wheels of the robot, during traverse of soft, deformable terrain, as that expected for lunar and planetary rovers. The proposed algorithm uses a robust Hough transform enhanced by fuzzy reasoning to estimate the angle of inclination of the wheel trace with respect to the vehicle reference frame. Any deviation of the wheel trace from the planned path of the robot suggests occurrence of sideslip that can be detected, and more interestingly, measured. This allows one to estimate the actual heading angle of the robot, usually referred to as the slip angle. The details of the various steps of the visual algorithm are presented and the results of experimental tests performed in the field with an all- terrain rover are shown, proving the method to be effective and robust.

In this paper, we propose an action planning algorithm and its evaluation method based on dynamic simulation for a novel type of hybrid leg-wheel rover for planetary exploration. Hybrid leg-wheel robots are recently receiving a growing interest from the space community to explore planets, since they offer an appropriate solution to gain improved speed and mobility on unstructured terrain. However, in order to fully reach the hybrid mechanismpsilas potential, it is necessary to establish an optimal way to define when to use one over the other locomotion mode, depending on the soil conditions and topology. Even though this step is crucial, little attention has been devoted to this topic by the robotic community. The switching of motion mode, that is either wheel or leg are the actions to be planned, that we are considering in this paper. We aim at generating the safest and the least energy demanding path to reach a point of scientific interest. In order to define the optimal path with the set of switching actions required for the robot to follow it, the authors developed an action planning algorithm and a path evaluation method based on a four steps approach. First, an optimal candidate path on a rough terrain is generated based on topology and specificationspsila criteria functions. Then switching actions are defined along this path depending on the hybrid robotpsilas performances in each motion mode. The next step is a dynamic simulation of the robot controlled to follow the path. Finally, the path is evaluated based on the energy profile spent by the actuators and calculated by the simulation. Demonstrations for the proposed technique are addressed along with a discussion on characteristics of the candidate path and the energy profile of the robot.