As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance,
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algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.
Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real-time epileptic seizure detection. In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close). It achieves to predict a result in every 0.006 seconds with a short detection duration (one second). By comparing with other two types deep learning approaches (DCNN and LSTM), the presented deep C-LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80%. INDEX TERMS Deep learning, C-LSTM, epileptic seizure, high-dimension electroencephalogram (EEG).
This article addresses the synthesis of static output feedback stabilization via employing event‐based control, for discrete‐time Markov jump systems subject to limited bandwidth and mismatched modes. The event‐triggered communication strategy with an extra dynamic variable is considered in the design of controller to alleviate transmission burden. Then, for the purpose of further improving system performance, a special triggering threshold constructed as a diagonal matrix is introduced. Asynchronous behavior caused by mismatched modes between the controller and controlled systems is depicted by utilizing a hidden Markov model. With the assistance of output feedback theory, an asynchronous event‐based output feedback control law is sufficiently formulated to achieve the stochastic stabilization with desired H∞$$ {H}_{\infty } $$ performance. Ultimately, an illustrative example is implemented to verify the feasibility of theoretical results.
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