Search Results (2)

Multi-source information fusion technology is a kind of information processing technology which comprehensively processes and utilizes multi-source uncertain information. It is an effective scheme to solve complex pattern recognition and improve classification performance. This study aims to improve the accuracy and robustness of exoskeleton gait pattern transition recognition in complex environments. Based on the theory of multi-source information fusion, this paper explored a multi-source information fusion model for exoskeleton gait pattern transition recognition in terms of two aspects of multi-source information fusion strategy and multi-classifier fusion. For eight common gait pattern transitions (between level and stair walking and between level and ramp walking), we proposed a hybrid fusion strategy of multi-source information at the feature level and decision level. We first selected an optimal feature subset through correlation feature extraction and feature selection algorithm, followed by the feature fusion through the classifier. We then studied the construction of a multi-classifier fusion model with a focus on the selection of base classifier and multi-classifier fusion algorithm. By analyzing the classification performance and robustness of the multi-classifier fusion model integrating multiple classifier combinations with a number of multi-classifier fusion algorithms, we finally constructed a multi-classifier fusion model based on D-S evidence theory and the combination of three SVM classifiers with different kernel functions (linear, RBF, polynomial). Such multi-source information fusion model improved the anti-interference and fault tolerance of the model through the hybrid fusion strategy of feature level and decision level and had higher accuracy and robustness in the gait pattern transition recognition, whose average recognition accuracy for eight gait pattern transitions reached 99.70%, which increased by 0.15% compared with the highest average recognition accuracy of the single classifier. Moreover, the average recognition accuracy in the absence of different feature data reached 97.47% with good robustness. Full article

In some hazardous or inaccessible applications, such as earthquake rescue, as a substitute for mankind, robots are expected to perform missions reliably. Unfortunately, the failure of components is difficult to avoid due to the complexity of robot composition and the interference of the environment. Thus, improving the reliability of robots is a crucial problem. The hexapod robot has redundant degrees of freedom due to its multiple joints, making it possible to tolerate the failure of one leg. In this paper, the Fault-Tolerant Tripod (F-TT) gait dealing with the failure of one leg is researched. The Denavit–Hartenberg (D-H) method is exploited to establish a kinematic model for the hexapod robot, the Jacobian matrix is analyzed, and it is proved that the body can be controlled when three legs are supported. Then, an F-TT gait phase sequence planning method based on a stability margin is established, and a method to improve stability is proposed. The trajectory for the center of gravity (COG) and foot is studied. Finally, a simulation model and prototype robot experiments are developed, and the effectiveness of the proposed method is verified. Full article