Search

Capacitive sensing has been proven valid for locomotion mode recognition as an alternative of popular electromyography based methods in the control of powered prostheses. In this paper, we analyze the characteristics of the capacitive signals and extract suitable feature sets to improve the recognition accuracy. Then the classification results of different classifiers are compared and one optimal classifier which can offer highest accuracy within a reasonable time limit is selected. Experimental results show that the recognition accuracy of the wearable capacitive sensing system has been improved by using the selected classifier. [ABSTRACT FROM AUTHOR]

In this paper, we propose a seven-link passivity-based dynamic walking model, in order to further understand the principles of real human walking and provide guidance in building bipedal robots. The model includes an upper body, two thighs, two shanks, flat feet and compliant joints. A bio-inspired central pattern generator (CPG)-based control method is applied to the proposed model. In addition, we add adaptable joint stiffness to the motion control. To validate the effectiveness of the proposed bipedal walking model, we carried out simulations and human walking experiments. Experimental results indicate that human-like walking gaits with different speeds and walking pattern transitions can be realized in the proposed locomotor system. [ABSTRACT FROM AUTHOR]

In this article, we present a new formation control method based on collaborative localization. Due to the accurate position estimation, the proposed approach is computationally light and well-performing in switching between different formations of real-world multi-robot systems. Satisfactory experimental results of multi-robot formation control are obtained in the RoboCup environment. [ABSTRACT FROM AUTHOR]

This paper presents a novel evolutionary computation approach to optimize fast forward gaits for a quadruped robot with three motor-driven joints on each limb. Our learning approach uses Particle Swarm Optimization to search for a set of parameters automatically aiming to develop the fastest gait that an actual quadruped robot can possibly achieve, based on the concept of parameterized representation for quadruped gaits. In addition, we analyze the computational cost of Particle Swarm Optimization taking the memory requirements and processing limitation into consideration. Real robot experiments show that the evolutionary approach is effective in developing quadruped gaits. Satisfactory results are obtained in less than an hour by the autonomous learning process, which starts with randomly generated parameters instead of any hand-tuned parameters. [ABSTRACT FROM AUTHOR]

This paper presents an experience-based collaborative approach for a group of autonomous robots to localize in asymmetric, dynamic environments. To help robots play soccer under more natural conditions, we propose a Markov localization based hybrid method with integration of environment experience construction and dynamic reference object based multi-robot localization. By using this method, the robot can estimate and correct its position perception more accurately and effectively among a group of autonomous robots, taking the odometry error and other negative influence into consideration. Satisfactory results are obtained in the RoboCup Four-Legged League environment. [ABSTRACT FROM AUTHOR]