Numerous approaches have been proposed to generate well-balanced gaits in biped robots that show excellent performance in simulated environments. However, in general, the dynamic balance of the robots decreases dramatically when these methods are tested in physical platforms. Since humanoid robots are intended to collaborate with humans and operate in everyday environments, it is of paramount importance to test such approaches both in physical platforms and under severe conditions. In this work, the special characteristics of the Nao humanoid platform are analyzed and a control system that allows robust walking and disturbance rejection is proposed. This approach combines the zero moment point (ZMP) stability criterion with angular momentum suppression and step timing control. The proposed method is especially suitable for platforms with limited computational resources and sensory and sensory-motor capabilities.

This work presents the development of a flexible industrial guidance system used to guide Automated Guided Vehicles (AGVs) in indoor industrial environments. Typically, wireless guidance systems are composed of path‐tracking and localization methods linked to follow a certain route. This paper focuses on the localization approach that permits industrial vehicles to operate indoors with the grade of accuracy, repeatability and reliability required by industrial applications. A key point is that, apart from accuracy, the position estimates should be performed at a high sample rate in order to permit the path tracker to follow the route properly. Robustness of absolute positioning is also crucial in industrial applications. An Extended Kalman Filter (EKF) is adopted to fuse the information provided by a laser navigation system and odometry. The effectiveness of the development is tested using a custom modified commercial industrial vehicle operating in an industrial setting

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This paper presents the development of a framework based on fuzzy logic for multi-sensor fusion and localization in indoor environments. Such a framework makes use of fuzzy segments to represent uncertain location information from different sources of information. Fuzzy reasoning, based on similarity interpretation from fuzzy logic, is then used to fuse the sensory information represented as fuzzy segments. This approach makes it possible to fuse vague and imprecise information from different sensors at the feature level instead of fusing raw data directly from different sources of information. The resulting fuzzy segments are used to maintain a coherent representation of the environment around the robot. Such an uncertain representation is finally used to estimate the robot position. The proposed multi-sensor fusion localization approach has been validated with a mobile platform using different range sensors.

The Zero Moment Point (ZMP) stability criterion has been broadly employed for walking pattern generation in legged robots. However, ZMP‐based approaches usually ignore the presence of angular momentum in the system. This hinders the performance of the gait, especially against disturbances. In this work we propose an angular momentum controller that can be integrated into standard ZMP‐based gaits. The experiments on a real Nao robot demonstrate that the use of the proposed angular momentum controller improves the stability of a walking pattern generator based on the preview control of the ZMP