Objective. A motor-imagery-based brain-computer interface (MI-BCI) provides an alternative way for people to interface with the outside world. However, the classification accuracy of MI signals remains challenging, especially with an increased number of classes and the presence of high variations with data from multiple individual people. This work investigates electroencephalogram (EEG) signal processing techniques, aiming to enhance the classification performance of multiple MI tasks in terms of tackling the challenges caused by the vast variety of subjects. Approach. This work introduces a novel method to extract discriminative features by combining the features of functional brain networks with two other feature extraction algorithms: common spatial pattern (CSP) and local characteristic-scale decomposition (LCD). After functional brain networks are established from the MI EEG signals of the subjects, the measures of degree in the binary networks are extracted as additional features and fused with features in the frequency and spatial domains extracted by the CSP and LCD algorithms. A real-time BCI robot control system is designed and implemented with the proposed method. Subjects can control the movement of the robot through four classes of MI tasks. Both the BCI competition IV dataset 2a and real-time data acquired in our designed system are used to validate the performance of the proposed method. Main results. As for the offline data experiment results, the average classification accuracy of the proposed method reaches 79.7%, outperforming the majority of popular algorithms. Experimental results with real-time data also prove the proposed method to be highly promising in its real-time performance. Significance. The experimental results show that our proposed method is robust in extracting discriminative brain activity features when performing different MI tasks, hence improving the classification accuracy in four-class MI tasks. The high classification accuracy and low computational demand show a considerable practicality for real-time rehabilitation systems.
2020. Particle swarm optimization for cooperative multirobot task allocation: a multi-objective approach.
Abstract-The work presented in this paper is part of our investigation in the ROBOSKIN project. The project aims to develop and demonstrate a range of new robot capabilities based on the tactile feedback provided by a robotic skin. One of the project's objectives is to improve human-robot interaction capabilities in the application domain of robot-assisted play. This paper presents design challenges in augmenting a humanoid robot with tactile sensors specifically for interaction with children with autism. It reports on a preliminary study that includes requirements analysis based on a case study evaluation of interactions of children with autism with the child-sized, minimally expressive robot KASPAR. This is followed by the implementation of initial sensory capabilities on the robot that were then used in experimental investigations of tactile interaction with children with autism.
Unmanned Surface Vehicles (USVs) are increasingly used for ocean missions, which typically require long duration of operations under strict energy constraints. Consequently, there is an increased interest in energy efficient path planning for USVs. This work proposes a novel energy efficient path planning algorithm to address the challenges with the presence of spatially-temporally variant sea current and complex geographic map data, by integrating the following algorithms, namely Voronoi roadmap, Dijkstras searching, coastline expanding and genetic algorithm. The selection, crossover and mutation operators are employed as part of the GA algorithm. The dividing, smoothing and exchanging operators are proposed to improve the quality of the path and adapt to the Voronoi-Visibility roadmap. The Global Self-Consistent Hierarchical High-Resolution Shorelines dataset and historical sea current dataset are applied to demonstrate the flexibility and practicability of the proposed algorithm. To evaluate the performance, the Voronoi-GA energy efficient algorithm and Voronoi-Visibility energy efficient path re-planning algorithm are also im
In this paper, a novel Voronoi-Visibility (VV) path planning algorithm, which integrates the merits of a Voronoi diagram and a Visibility graph, is proposed for solving the Unmanned Surface Vehicle (USV) path planning problem. The VM (Voronoi shortest path refined by Minimising the number of waypoints) algorithm was applied for performance comparison. The VV and VM algorithms were compared in ten Singapore Strait missions and five Croatian missions. To test the computational time, a high-resolution, large spatial dataset was used. It was demonstrated that the proposed algorithm not only improved the quality of the Voronoi shortest path but also maintained the computational efficiency of the Voronoi diagram in dealing with different geographical scenarios, while also keeping the USV at a configurable clearance distance c from coastlines. Quantitative results were generated by comparing the Voronoi, VM and VV algorithms in 2,000 randomly generated missions using the Singapore dataset.
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