The proposed study presents an original concept for the design of a walking robot with a minimum number of motors. The robot has a simple design and control system, successfully moves by walking, avoids or overcomes obstacles using only two independently controlled motors. Described are basic geometric and kinematic dependencies related to its movement. It is proposed optimization of basic dimensions of the robot in order to reduce energy losses when moving on flat terrain. Developed and produced is a 3-D printed prototype of the robot. Simulation and experiments for overcoming an obstacle are presented. Trajectories and instantaneous velocities centers of links from the robot are experimentally determined. The phases of walking and the stages of overcoming an obstacle are described. The theoretical and experimental results are compared. The suggested dimensional optimization approaches to reduce energy loss and experimental determination of the instant center of rotation are also applicable to other walking robots.

This paper represents the development of a prototype robotic system for automatic bone drilling in orthopaedic surgery. A new design of the mechanical structure is proposed, answering the surgical requirements for the robot dimensions and weight to be as small as possible. The new structure design is based on idea of parallelism of the axis of the linear actuator and the axis of the rotary one. The control system is improved by using a new micro-controller. A new type of linear drive and a new force sensor (load cell) are built in, so that the quality characteristics of the “Orthopaedic bone Drilling Robot” (ODRO) are not only the same as previous its two versions, but becomes better. An experimental setup is arranged and experiments are executed to test and to verify the robot quality features and functional capabilities.

The work defines in a new way the different types of solutions of the inverse kinematics (IK) problem for planar robots with a serial topology and presents an algorithm for solving it. The developed algorithm allows the finding of solutions for a wide range of robots by using a geometric approach, representing points in a polar coordinate system. Inverse kinematics, which is one of the most important, most studied and challenging problems in robotics, aims to calculate the values of the joint variables, given the desired position and orientation of the robot’s end effector. Configuration space is defined by joint angles and is the basis of most motion planning algorithms. Areas in the working and configuration space are generated that are reachable with different types of solutions. Programs are created that use the proposed algorithm for robots with two and three rotational degrees of freedom, and graphically present the results in the workspace and configuration space. The possibility of transitioning from one type of solution to another by passing through a singular configuration is discussed. The results are important for planning motions in the workspace and configuration space, as well as for the design and kinematic analysis of robots.

Researchers in neuroscience computing experience difficulties when they try to carry out neuroanalysis in practice or when they need to design an explainable brain‐computer interface (BCI) with quick setup and minimal training phase. There is a need of interpretable computational intelligence techniques and new brain states decoding for more understandable interpretation of the sensory, cognitive, and motor brain processing. We propose a general‐purpose fuzzy software system shell for developing a custom EEG BCI system. It relies on the bursts of the ongoing EEG frequency power synchronization/desynchronization at scalp level and supports quick BCI setup by linguistic features, ad hoc fuzzy membership construction, explainable IF‐THEN rules, and the concept of the Internet of Things (IoT), which makes the BCI system device and service independent. It has a potential for designing both passive and event‐related BCIs with options for visual representation at scalp‐source level in response to time. The feasibility of the proposed system has been proven by real experiments and bursts for β and γ frequency power have been detected in real time in response to evoked visuospatial selective attention. The presence of the proposed new brain state decoding can be used as a feasible metric for interpretation of the spatiotemporal dynamics of the passive or evoked neural oscillations.

Acquiring adequate sensory information and using it to provide motor control are important issues in the process of creating walking robots. The objective of this article is to present control algorithms for the optimization of the walking cycle of an innovative walking robot named “Big Foot”. The construction of the robot is based on minimalist design principles—only two motors are used, with which Big Foot can walk and even overcome obstacles. It is equipped with different types of sensors, with some of them providing information necessary for the realization of an optimized walk cycle. We examine two laws of motion—sinusoidal and polynomial—where we compare the results with constant angular velocity motion. Both proposed laws try to find balance between minimizing shock loads and maximizing walking speed for a given motor power. Experimental results are derived with the help of a 3D-printed working prototype of the robot, with the correct realization of the laws of motion being ensured by the use of a PD controller receiving data from motor encoders and tactile sensors. The experimental results validate the proposed laws of motion and the results can be applied to other walking robots with similar construction.