Advances in Robotics & Automation

Brain-Machine Interfaces (BMIs) have been one of the most influential and disruptive science fields of the past decades. Prosthetic or remotely operated robotic devices being controlled by brain signals has transitioned from science fiction to reality. Advances in the recording electrodes technology and the machine learning and signal decoding algorithms were critical in the realization of those systems. The second decade of the 21st century brings new challenges found in both frontiers; first, advancements in neuroscience are sought via high-resolution mapping of the brain for better understanding of its function and decision making processes. On the robotics frontiers, the challenge of the human controlling many robots simultaneously is of utmost importance for applications spanning from industrial and entertainment, to disaster response and military. As the swarming paradigm, deriving inspiration from the behaviour of natural swarms such as bird flocks and fish schools, offers myriad advantages to a team of robots, the way humans interact and control a robotic swarm creates new avenues of research. This article summarizes recent developments and novel methods for brain-swarm interfaces, and poses challenges for the future researchers.

We describe a unique liquid handling platform, based on a Tecan EVO, specifically designed for the preparation of analytic calibrators and quality control samples according to the requirements of the Good Laboratory Practice (GLP). The platform utilizes a combination of off-the-shelf software (Tecan Gemini 4.2) and custom-programmed SPIKE 1.0. The system convinces with robust and reproducibly quality and a very easy, intuitive user interface. All security requirements as per FDA 21CFRpart 11 were considered, when programming the software bundle.

In this paper we proposed an algorithm for handling the aliasing problem of the images. The proposed method is post processing approach which is applied after the image acquisition. The paper tries to restore the image quality which is affected by the aliased edges, and gives the image free from the effect. The resultant images will have smoother edges than the input image. The algorithm is fast as it does not use the complex mathematical structure and focused itself on the affected areas only. The result on the grayscale images have been displayed to prove the working of algorithm.

Mathematical modeling, simulation and control of an underwater robot are a very complex task due to its nonlinear dynamic structure. In this paper, the authors present kinematic and dynamic modeling of an underwater robot with two rotating thrusters. Through a virtual environment implemented in MATLAB and LabVIEW, the performance of the proposed robot under real operating conditions was demonstrated.