Abstract-In this paper, we present a Covariance Intersection (CI)-based algorithm for reducing the processing and communication complexity of multi-robot Cooperative Localization (CL). Specifically, for a team of N robots, our proposed approximate CI-based CL approach has processing and communication complexity only linear, O(N), in the number of robots. Moreover, and in contrast to alternative approximate methods, our approach is provably consistent, can handle asynchronous communication, and does not place any restriction on the robots' motion. We test the performance of our proposed approach in both simulations and experimentally, and show that it outperforms the existing linear-complexity split CI-based CL method.

This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.

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In this paper, we consider the problem of autonomous exploration of unknown environments with single and multiple robots. This is a challenging task, with several potential applications. We propose a simple yet effective approach that combines a behavior-based navigation with an efficient data structure to store previously visited regions. This allows robots to safely navigate, disperse and efficiently explore the environment. A series of experiments performed using a realistic robotic simulator and a real testbed scenario demonstrate that our technique effectively distributes the robots over the environment and allows them to quickly accomplish their mission in large open spaces, narrow cluttered environments, dead-end corridors, as well as rooms with minimum exits.

Humanoid biped robots are typically complex in design, having numerous Degrees‐of‐Freedom (DOF) due to the ambitious goal of mimicking the human gait. The paper proposes a new architecture for a biped robot with seven DOF per each leg and one DOF corresponding to the toe joint. Furthermore, this work presents close equations for the forward and inverse kinematics by dividing the walking gait into the Sagittal and Frontal planes. This paper explains the mathematical model of the dynamics equations for the legs into the Sagittal and Frontal planes by further applying the principle of Lagrangian dynamics. Finally, a control approach using a PD control law with gravity compensation was recurred in order to control the desired trajectories and finding the required torque by the joints. The paper contains several simulations and numerical examples to prove the analytical results, using SimMechanics of MATLAB toolbox and SolidWorks to verify the analytical results