Abstract-This paper presents a new way for mobile robots' path planning which is based on the Evolutionary Artificial Potential Fields(EAPF) approach. The APF theory is a traditional method to plan path for a robot. The Evolutionary APF aims at helping a robot jump out of the local minimum point. Using a virtual goal to produce extra force and fixing the direction of the repulsive force are combined to prompt the robot to escape from the obstacle in different situations. The simulation result shows that the evolutionary method is effective for solving the local minimum problem.
Localization is one of the most important issues in mobile robotics, especially when an autonomous mobile robot performs a navigation task. The current and popular occupancy grid map, based on 2D LiDar simultaneous localization and mapping (SLAM), is suitable and easy for path planning, and the adaptive Monte Carlo localization (AMCL) method can realize localization in most of the rooms in indoor environments. However, the conventional method fails to locate the robot when there are similar and repeated geometric structures, like long corridors. To solve this problem, we present Text-MCL, a new method for robot localization based on text information and laser scan data. A coarse-to-fine localization paradigm is used for localization: firstly, we find the coarse place for global localization by finding text-level semantic information, and then get the fine local localization using the Monte Carlo localization (MCL) method based on laser data. Extensive experiments demonstrate that our approach improves the global localization speed and success rate to 96.2% with few particles. In addition, the mobile robot using our proposed approach can recover from robot kidnapping after a short movement, while conventional MCL methods converge to the wrong position.
Localization for estimating the position and orientation of a robot in an asymmetrical environment has been solved by using various 2D laser rangefinder simultaneous localization and mapping (SLAM) approaches. Laser-based SLAM generates an occupancy grid map, then the most popular Monte Carlo Localization (MCL) method spreads particles on the map and calculates the position of the robot by a probabilistic algorithm. However, this can be difficult, especially in symmetrical environments, because landmarks or features may not be sufficient to determine the robot’s orientation. Sometimes the position is not unique if a robot does not stay at the geometric center. This paper presents a novel approach to solving the robot localization problem in a symmetrical environment using the visual features-assisted method. Laser range measurements are used to estimate the robot position, while visual features determine its orientation. Firstly, we convert laser range scans raw data into coordinate data and calculate the geometric center. Secondly, we calculate the new distance from the geometric center point to all end points and find the longest distances. Then, we compare those distances, fit lines, extract corner points, and calculate the distance between adjacent corner points to determine whether the environment is symmetrical. Finally, if the environment is symmetrical, visual features based on the ORB keypoint detector and descriptor will be added to the system to determine the orientation of the robot. The experimental results show that our approach can successfully determine the position of the robot in a symmetrical environment, while ordinary MCL and its extension localization method always fail.
A potential limitation of motor imagery (MI) based brain-computer interface (BCI) (MI-BCI) is that it usually requires a relatively long time to record sufficient electroencephalogram (EEG) data for robust feature extraction and classification. Moreover, due to the non-stationarities in EEG signals, the offline training model has poor adaptability and classification ability in cross-session or sample-wise online testing. Methods: To address the problems, we propose a model updating scheme with adaptive and fast operation. Based on the Common Spatial Pattern (CSP), we propose an online and fast generalized eigendecomposition method by Recursive Least Squares updates of the CSP filter coefficients (RLS-CSP), which allows incremental training for CSP spatial filters. Additionally, we present an Incremental Self-training Classification algorithm based on Density Clustering (ISCDC) to select high-confidence samples to update spatial filters and classifier, and classify at the same time. Results: We conducted extensive experiments to validate the efficiency of the proposed adaptive CSP and classifier on the BCI III_IVa and BCI III_V data sets. Experimental results demonstrate that RLS-CSP outperforms significantly in a small sample setting (SSS), and ISCDC has great adaptability in cross-session and non-stationary EEG signals. The results indicate that our proposed methods are feasible to improve the real-time performance of online BCI system.
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