Huawei Liang

SP-NN: A novel neural network approach for path planning

In this paper, a neural network approach named shortest path neural networks (SP-NN) is proposed for real-time on-line path planning. Based on grid-based map and mapping this kind of map to neural networks, this proposed method is capable of generating the globally shortest path from the target position to the start position without collision with any obstacles. The dynamics of each neuron is distinctive to other previously presented methods by other researchers and ensures that the generated path is shortest without collision and that the state of neurons varied continuously. Extensive simulations show the efficiency of the presented method.

A route discovery method based on limited flooding in ZigBee networks

ZigBee is an emerging standard for the low cost, low power and low rate wireless networks. ZigBee standard specifies two routing protocols: a tree routing protocol and a route discovery protocol similar to AODV. The second one discovers the optimal path by flooding route quest command frame, which wastes significant amount of bandwidth and energy. This paper proposes a novel route discovery method based on limited flooding in ZigBee networks. The tree routing information is utilized to obtain the flooding radius, which limites the flooding in a bounded region of the network. Simulation results show that the proposed method can effectively reduce the communication overhead.

Particle filtering for range-based localization in Wireless Sensor Networks

The paper proposed a range-based particle filtering localization method for static WSN (Wireless Sensor Network). In the method, every blind node in WSN localizes itself through local distance measurements. So the method is parallel. With the development of hardware, RSSI (Received Signal Strength Indicator) measurements were already hardwired in some chips, and then almost no extra hardware was required when the measurement stratagem was adopted. Firstly, the state-space function of localization in a static WSN was proposed in the paper. And the measurement noise model was particularly studied. Because of the non-linear and non-Gaussian model, particle filter was adopted as a suitable localization algorithm for the application. Static nodes bring some new problems to us. So important steps in a particle filter were specifically illustrated in the paper, including: initialization, prediction, sequential importance sampling and the resampling. Software simulations were done in the paper to demonstrate the effect of the proposed method. A blind node was selected as a sample to illustrate the convergence of the particle filter. And some problems emerged in the simulation were also analyzed. After that, it showed a result about localization in a 100 nodes network by the proposed method.

A Shooting Training and Instructing System Based on Image Analysis

The main shortcoming of the traditional way to train shooting athletes is that the effect is hard to be evaluated without live firing. We have designed and implemented a computer-aided shooting training and instructing system that can help coaches and athletes do more quality than quantity work. The minimum system consists of 6 components: a gun, a laser aiming device, a target, a camera, an image card and a computer. It works as follows: the camera snaps a target image, then the aiming point which is marked by the laser spot is extracted from the image and the corresponding score is calculated and printed in the screen in real time. The trace of the aiming point in a training session is also drawn in the screen. The system can prompt the best trigger moment. Based on the recorded performance data in the whole training session, the coach can make objective evaluation on the training effect and give pointed instructions to the athlete. The evaluation and instructions can be made by the expert system instead. Up to 16 targets can be processed simultaneously by one system.

A practical evaluation of radio signal strength for mobile robot localization

This paper dealt with localization of a mobile robot using received signal strength (RSS) and detailed a practical evaluation about the suitability of the RSS based localization. RSS technique is especially appealing for localization in WSN due to its simplicity such as low cost, size and power constraints, despite of the fact that RSS may bring in very noisy range estimates. We conducted numerous ranging experiments to quantify the effects of various environmental factors on RSS both in the indoor environment and in the outdoor environment. To further improve the localization performance of mobile robot, we proposed a novel improvement-mean filtering technique to reduce the effect of radio irregularity and optimized the localization results. A series of localization experiments were performed to validate the proposed methods, with achieving the localization error to 1.2m in the outdoor basketball field.

An Empirical Study of DV-Hop Localization Algorithm in Random Sensor Networks

Node localization is an important problem for location-dependent applications of wireless sensor networks. Aiming at the positioning problem of wireless sensor networks node location, an improved DV-Hop localization algorithm is proposed in this paper. The proposed method firstly recalculates the hop-size and sends different correction along different directions instead of computing a single correction to be broadcasted into the networks. Then we empirically evaluate the difference between distance estimate and actual distance through a number of simulation experiments statistical results. We find empirical parameter to improve the accuracy of distance estimate. Simulation results show that the performance of the proposed scheme outweighs classical DV-Hop algorithms, especially in lower connectivity.

Particle filtering for WSN aided SLAM

The paper proposed a particle filter based and Wireless Sensor Networks (WSN) aided Simultaneous Localization and Mapping (SLAM) strategy. The proposed method aims at solving two troublesome problems in the traditional particle filter based SLAM algorithms. The first problem is high dimension of question space; and the second one is multi-date association. Firstly, the paper analysed the model of the WSN aided SLAM problem. Then noises in the model were analysed. According to analyses, a particle filtering algorithm was developed as the kernel algorithm for data fusion. Detailed procedures of the particle filter were introduced. Besides, all key steps, including initialization, prediction, sequential importance sampling and also resampling, were especially specified. Software simulations were done to analyse and prove the validity and the efficiency of the proposed method. The simulation results supported that the proposed method can diminish dimensions of the SLAM problem and resolve the multi-data association problems. Moreover, through adopting PF algorithm and involving WSN, following advantages were also acquired: firstly, the method could locate blind nodes in WSN with high resolution while no anchor node was available; secondly, it could improve precision of localization and mapping for mobile robots and, especially, it could inhibit error accumulation of dead reckoning without requirement of close-loop.

An improvement of DV-Hop Algorithm Based on Collinearity

In order to fully consider the topology relationship among the anchor nodes and the topology relationship between the anchor nodes and unknown nodes, an Improvement of DV-Hop Algorithm Based on Collinearity is proposed. The main principle of the proposed scheme is to introduce the concept of normalized colinearity (NC) into the selection phase of beacon nodes. Based on DV-Hop, best available anchor terns are elected to accomplish more accurate localization by using NC. The experimental results show that the location accuracy of the proposed algorithm outweighs significantly the DV-Hop algorithm, especially in the cases where the connectivity is lower than 10.

An experimental system of mobile robot's self-localization based on WSN

Mobile robots self-localization is an important part of robot technology research. This paper first briefly introduces several main methods of mobile robots localization, and RSSI (Received Signal Strength Indicator) ranging principle. Then it proposes an experimental system which is used for mobile robots localization based on wireless sensor network (WSN), and describes the experimental systems structure in detail. According to outdoor experiments, it proves that the system operates perfectly. Based on the experimental results, we provide some suggestions for improving the localization algorithm, facilitating further algorithm research.

A Localization Algorithm nin Wireless Sensor Networks Using a Mobile Beacon Node

Wireless sensor networks is one of important information acquisition methods and also has been a promising technique for many applications in recent years. Node localization is one of challenging and fundamental topics in distributed WSNs research. The goal of this paper is to propose an efficient range-free localization algorithm in WSN. We use directional antennas other than omnidirectional antennas. While the mobile beacon node using several directional antennas moves, it periodically broadcasts its packet in K directions. Ordinary sensor nodes are able to estimate their position based on the messages received from only one virtual beacon. We also use discrete imprecision range measurement. It can send messages in different lever of power to obtain the approximate distance between neighbor nodes. Considering WSN is an event-based network, we propose a refinement phase in latter part of this paper. The main advantage of the algorithm is that it is both simple and economical. Our simulation has shown that the given approach is able to provide accurate and robust estimation of sensor location.