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Artificial Potential Field techniques (APF) have become a popular path planning technique due to their simplicity and fast-execution nature. APF techniques have been widely known for their outstanding performance against the path quality and computational time trade-of problem for path planning techniques. However, Since APF techniques are optimization problems, they suffer from well-known problems such as the minima trap problem and the narrow passage problem. The APF planner can fail to find an obstacle-firee path to the final goal point and simply be trapped in a local minima for some map configurations. This paper proposes a new path planning technique that aims to take the advantage of the APF technique as well as solving the APF problems of local trap and narrow passage problems. The proposed technique is inspired by the fluid dynamic concepts and adopts an artificial "flushing" operation. The robot and the obstacles will be placed in an artificial pool. The robot will have negligible weight while the obstacles will be fixed in the pool. Once the "flushing" operation starts, the robot will be automatically pulled by the water to the flushing point which is the goal point. It is guaranteed that the water in the whole map will be absorbed into the flushing point and no particles will be trapped in any region. The simulation results have proven the effectiveness of the proposed technique and demonstrated its ability to overcome the challenge of local minima traps in potential field path planning. [ABSTRACT FROM AUTHOR]

Recently, there has been widespread and vital adoption of flexible manipulators due to their increased prevalence. This is attributed to the growing demand for flexibility in various tasks like refueling operations, inspections, and maintenance activities. Nevertheless, these robots are under-actuated systems characterized by a nonlinear behavior and present dynamic coupling interactions that contribute to the complexity of the control process. The main control objective is to achieve an accurate tracking of the desired position while simultaneously reducing oscillations occurring in the link. Therefore, this paper proposes integrating the tuning and adaptive control by employing fuzzy logic methodology in conjunction with internal model control (IMC). The suggested controller takes advantage of intelligent techniques, simple structure, robustness, and easy tuning of the conventional IMC. Both triangular and trapezoidal Membership Functions (MFs) are applied in this study to create a pair of Fuzzy Logic Controllers (FLCs) based on the Mamdani method. These controllers are employed to dynamically adjust the parameters of the IMC, in contrast to the fixed parameters used in the conventional IMC approach. The effectiveness of the suggested Adaptive-based Fuzzy IMC (AFIMC) is showcased through simulation and practical experimentation, in scenarios both with and without disturbances. Results indicate that this technique outperforms conventional IMC in achieving control objectives and rejecting disturbances. [ABSTRACT FROM AUTHOR]

Background: The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. Methods: We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot. Results: Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals. Discussion: The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions. [ABSTRACT FROM AUTHOR]

This paper introduces the Color Histogram Contouring (CHC) method, a new training-less approach to object detection that emphasizes the distinctive features in chrominance components. By building a chrominance-rich feature vector with a bin size of 1, the proposed CHC method exploits the precise information in chrominance features without increasing bin sizes, which can lead to false detections. This feature vector demonstrates invariance to lighting changes and is designed to mimic the opponent color axes used by the human visual system. The proposed CHC algorithm iterates over non-zero histogram bins of unique color features in the model, creating a feature vector for each, and emphasizes those matching in both the scene and model histograms. When both model and scene histograms for these unique features align, it ensures the presence of the model in the scene image. Extensive experiments across various scenarios show that the proposed CHC technique outperforms the benchmark training-less Swain and Ballard method and the algorithm of Viola and Jones. Additionally, a comparative experiment with the state-of-the-art You Only Look Once (YOLO) technique reveals that the proposed CHC technique surpasses YOLO in scenarios with limited training data, highlighting a significant advancement in training-less object detection. This approach offers a valuable addition to computer vision, providing an effective training-less solution for real-time autonomous robot localization and mapping in unknown environments. [ABSTRACT FROM AUTHOR]

This work takes advantage of synergetic control theory and fractional calculus to develop and propose fractional synergetic control (FSC) strategy for Four Degrees of Freedom (4-DOF) robot manipulator. The proposed fractional synergetic control is designed to track a joint space as well as workspace desired trajectories. Fractional calculus gives more flexibility in the design since it has a wider stability region. Added to that, as stated in the literature, compared to a similar approach such as sliding mode control, the synergetic control approach converges faster to the equilibrium point, without chattering with a fast response. This paper proposes a new control strategy that takes advantage of fractional calculus and synergetic control theory. This proposed control strategy is tested experimentally on a 4-DOF manipulator to study the performance of the proposed control scheme. The stability of the closed-loop system is proved using the Lyapunov approach. The experimental results have shown that the proposed FSC design has achieved a good tracking performance. [ABSTRACT FROM AUTHOR]

There has been a continuous interest in multi-robot formation systems in the last few years due to several significant advantages such as robustness, scalability, and efficiency. However, multi-robot formation systems suffer from well-known problems such as energy consumption, processing speed, and security. Therefore, developers are continuously researching for optimal solutions that can gather the benefits of multi-robot formation systems while overcoming the possible challenges. A backbone process required by any multi-robot system is path planning. Thus, path planning for multi-robot systems is a recent top research topic. However, the literature lacks a recent comprehensive review of path planning works designed for multi-robot systems. The aim of this review paper is to provide a comprehensive assessment and an insightful look into various path planning techniques developed in multi-robot formation systems, in addition to highlighting the basic problems involved in this field. This will allow the reader to discover the research gaps that must be solved for a better path planning experience for multi-robot formation systems. Finally, an illustrative comparative example is presented at the end of the paper to show the advantages and disadvantages of some popular path planning techniques. [ABSTRACT FROM AUTHOR]