Abstract-Future planetary exploration missions will require wheeled mobile robots ("rovers") to traverse very rough terrain with limited human supervision. Wheel-terrain interaction plays a critical role in rough-terrain mobility. In this paper, an online estimation method that identifies key terrain parameters using on-board robot sensors is presented. These parameters can be used for traversability prediction or in a traction control algorithm to improve robot mobility and to plan safe action plans for autonomous systems. Terrain parameters are also valuable indicators of planetary surface soil composition. The algorithm relies on a simplified form of classical terramechanics equations and uses a linear-least squares method to compute terrain parameters in real time. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for various soil types.Index Terms-Mobile robots, planetary rovers, rough terrain, wheel-terrain interaction.

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In this paper, we propose a frequency domaill(FD) MMSE equalizer for M-ary bi-orthogonal keying direct sequence UWB (MBOK DS-UWB) systems considered as a PHY proposal for high-speed wireless communication in IEEE 802.15.3a. The conventional FD MMSE equalization scheme has a structural limit due to insertion of the cyclic prefix (CP) in all transmit packets, but the proposed scheme is able to equalize the channel effect without CP. In order to overcome channel estimation error by multipath delay, we introduce a moving FFT and a moving average scheme. Compared with the traditional time-domain (TD) MMSE-RAKE receiver, the proposed FD MMSE equalizer has a better BER performance and we demonstrate this result by computer simulation.

Several unmanned retail stores have been introduced with the development of sensors, wireless communication, and computer vision technologies. A vision-based kiosk that is only equipped with a vision sensor has significant advantages such as compactness and low implementation cost. Using convolutional neural network (CNN)-based object detectors, the kiosk recognizes an object when a customer picks up a product. In retail object recognition, the key challenge is the limited number of detections and high interclass similarity. In this study, these challenges are addressed by utilizing the "view-specific" feature of an object; specifically, an object class is divided into multiple "view-based" subclasses, and the object detectors are trained using these data. Further, the "view-aware feature" is defined by aggregating subclass detection results from multiple cameras. A superclass classifier predicts a superclass by utilizing an informative subclass detection result that distinguishes the target object from other similar-looking objects. To verify the effectiveness of the proposed approach, a prototype of the vision-based unmanned kiosk system is implemented. Experimental results indicate that the proposed method outperforms the conventional method, even on a state-of-the-art detection network. The dataset used in this study has been subsequently provided in the IEEE DataPort for reproducibility.