The design of a rover for a specific environment is a complex procedure which requires modeling a chassis and evaluating it with specific criteria. This is the aim of the Performance Optimization Tool (POT)
-Navigating in rough terrain is a complex task that requires the robot to be considered as a holistic system. Algorithms, which don't consider the physical dimensions and capabilities of the mobile robot lead to inefficient motion and suffer from a lack of robustness. A physical model of the robot is necessary for trajectory control. In this paper, quasi-static modeling of a six-wheeled robot with a passive suspension mechanism is presented together with a method for selecting the optimal torques considering the system constraints: maximal and minimal torques, positive normal forces. The aim of this method is to limit wheel slip and to improve climbing capabilities. The modeling and the optimization are applied to the Shrimp rover.
Owing to the fundamental nature of all-terrain exploration, autonomous rovers are confronted with unknown environments. This is especially apparent regarding soil interactions, as the nature of the soil is typically unknown. This work aims at establishing a framework from which the rover can learn from its interaction with the terrains encountered and shows the importance of such a method. We introduce a set of rover-terrain interaction (RTI) and remote data metrics that are expressed in different subspaces. In practice, the information characterizing the terrains, obtained from remote sensors (e.g., a camera) and local sensors (e.g., an inertial measurement unit) is used to characterize the respective remote data and RTI model. In each subspace, which can be described as a feature space encompassing either a remote data measurement or an RTI, similar features are grouped to form classes, and the probability distribution function over the features is learned for each one of those classes. Subsequently, data acquired on the same terrain are used to associate the corresponding models in each subspace and to build an inference model. Based on the remote sensor data measured, the RTI model is predicted using the inference model. This process corresponds to a near-to-far approach and provides the most probable RTI metrics of the terrain lying ahead of the rover. The predicted RTI metrics are then used to plan an optimal path with respect to the RTI model and therefore influence the rover trajectory. The CRAB rover is used in this work for the implementation and testing of the approach, which we call rover-terrain interactions learned from experiments (RTILE). This article presents RTILE, describes its implementation, and concludes with results from field tests that validate the approach. C 2009 Wiley Periodicals, Inc.• First, the performance depends on the robot's physical and mechanical properties, corresponding to its structure, suspension mechanism, actuators, and sensors.• Second, the performance is related to the control of the robotic platform, in a very generic sense. This includes research in fields such as control, obstacle avoidance, path planning, pose estimation, and so forth.As mentioned in the latter point, the robot's performance is related to the interaction of the robot with its surroundings and its capability to sense and represent the environment. A natural environment, which is usually the operating place for all-terrain rovers, involves a great diversity in terrain, soil and obstacle types, shapes, and appearances. This diversity is difficult to model and hence implies additional uncertainty that the rover must cope with.
Summary. The terrain classification is a very important subject to the all-terrain robotics community. The knowledge of the type of terrain allows a rover to deal with its environment more efficiently. The work presented in this paper shows that it is possible to differentiate terrains based on their aspects, using exteroceptive sensors, as well as based on their influence on the rover's behavior, using proprioceptive sensors. Using a boosting method (AdaBoost), these two sets of classifiers are trained and applied independently. The resulting dual algorithm identifies offline the nature of the terrain on which the vehicle is virtually driving and classifies it according to categories previously labeled, such as sand or grass. Due to the good results obtained for the classification based solely on each type of sensor, this paper concludes that the correlation between data from proprioceptive and exteroceptive sensors could be used for further applications. This paper is a summarized version of the one presented at the ISER conference.
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