Hongwei Tang

The methods of task assignment and path planning have been reported by many researchers, but they are mainly focused on environments with prior information. In unknown dynamic environments, in which the real-time acquisition of the location information of obstacles is required, an integrated multi-robot dynamic task assignment and cooperative search method is proposed by combining an improved self-organizing map (SOM) neural network and the adaptive dynamic window approach (DWA). To avoid the robot oscillation and hovering issue that occurs with the SOM-based algorithm, an SOM neural network with a locking mechanism is developed to better realize task assignment. Then, in order to solve the obstacle avoidance problem and the speed jump problem, the weights of the winner of the SOM are updated by using an adaptive DWA. In addition, the proposed method can search dynamic multi-target in unknown dynamic environment, it can reassign tasks and re-plan searching paths in real time when the location of the targets and obstacle changes. The simulation results and comparative testing demonstrate the effectiveness and efficiency of the proposed method.

Due to the advent and pervasive deployment of wireless local area networks, WiFi-based indoor localization systems have received increasing attention in the last few years. However, their localization accuracy has always been a challenging issue. In addition, because of diverse interference such as multipath effects, the block of signals, an unstable or weak signal in itself, etc., not all the Access Points (AP) are informative for the localization. Faced with these problems, we propose a WiFi-based localization model by Modifying the large localization errors and Enhancing the Gaussian Process Regression (MEGPR). (1) To select the AP subsets that contribute more to the localization and further reduce the computational load, the Access Point Discrimination Criterion (APDC) is introduced to quantify the discernibility of the APs detected in the workspace and filter out the APs with low discrimination. (2) Second, to enhance the localization model, the localization residual is fed and learnt by the model. (3) Furthermore, the large localization errors are mitigated by the Location Modification Method (LMM). Experiments were conducted in a real environment with an area of more than 1200 m2 and the results show that compared with other existing localization models, the average localization error of the proposed MEGPR model is minimum, which further verifies the effectiveness of the proposed MEGPR localization model.

WiFi-Fingerprint is extensively utilized for indoor localization with the advent of the high-density wireless networks deployment and research on ubiquitous intelligence. Nevertheless, establishing an elaborate radio map for localization is a highly time-consuming task. Aiming to alleviate this problem and enhance WiFi-based indoor localization accuracy, this paper has done the following contributions. First, we present gaussian process regression (GPR) models to predict the spatial distribution of signal strength in the uncalibrated domain with limited known labeled fingerprints in reference points (RPs). As a result, deployment effort for radio mapping can be greatly reduced. Second, in order to acquire fingerprints data with higher accuracy, the compound kernels for received signal strength (RSS) prediction models are presented. Finally, the definite position is determined with the weighted Similarity K-Nearest Neighbor (Similarity-WKNN) localization algorithm when new observation RSS is collected. The experiments show that compared with the original reference fingerprints localization system, the proposed localization system explicitly reduces the localization error. The results further demonstrate that our method can augment the fingerprints and improve the accuracy of fingerprint-based indoor localization without extra manual calibration or adding dedicated infrastructure.