Jianjun Ni

A FUZZY-LOGIC BASED CHAOS GA FOR COOPERATIVE FORAGING OF MULTI-ROBOTS IN UNKNOWN ENVIRONMENTS

This paper investigates the foraging of multiple robots in completely unknown environments. The onboard robot sensor information and expert knowledge of foraging are used to forage the targets. The foraging problem in this paper is defined as a searching task, where the robots cooperate to find and reach all the targets in an efficient way. A novel fuzzy-logic based chaos genetic algorithm (FCGA) is proposed for target foraging in unknown environments. The fuzzy logic is used to avoid the disorder of the robot movement and reduce the search time when there is no information about the targets or the information density around the robots is the same. The chaos genetic algorithm enables the robots find the targets efficiently. In the proposed approach, the robot motion can be dynamically adjusted to guarantee that all the targets can be found, even in some difficult situations such as targets are at some locations difficult to find or obstacles are linked together. The proposed approach is capable of dealing with uncertainties, e.g., some robots break down. In comparison to the pure chaos genetic algorithm (PCGA) and the random-search approach, experimental results show that the proposed approach is more efficient in foraging all the targets.

An Improved Spinal Neural System-Based Approach for Heterogeneous AUVs Cooperative Hunting

AbstractCooperative hunting by a multi-AUV system in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To conduct this task, each AUV needs to move quickly without obstacle collisions and cooperate with other AUVs considering the overall interests. In this paper, the heterogeneous AUVs cooperative hunting problem is studied, including two main tasks, namely the search and pursuit of targets, and a novel spinal neural system-based approach is proposed. In the search stage, a partition and column parallel search strategy is used in this paper, and a search formation control algorithm based on an improved spinal neural system is proposed. The presented search algorithm not only accomplishes the search task but also maintains a stable formation without obstacle collisions. In the cooperative pursuit stage, a dynamic alliance method based on bidirectional negotiation strategy and a pursuit direction assignment method based on improved genetic algorithm are presented, which can realize the pursuit task efficiently. Finally, some simulations are conducted and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the hunting tasks in unknown 3D underwater environment efficiently.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

An improved shuffled frog leaping algorithm for robot path planning

Path planning is one of the most significant and challenging subjects in robot control field. In this paper, a path planning method based on an improved shuffled frog leaping algorithm is proposed. In the proposed approach, a novel updating mechanism based on the median strategy is used to avoid local optimal solution problem in the general shuffled frog leaping algorithm. Furthermore, the fitness function is modified to make the path generated by the shuffled frog leaping algorithm smoother. In each iteration, the globally best frog is obtained and its position is used to lead the movement of the robot. Finally, some simulation experiments are carried out. The experimental results show the feasibility and effectiveness of the proposed algorithm in path planning for mobile robots.

A Single-Way Ranging Localization of AUVs Based on PSO of Outliers Elimination

Localization of autonomous underwater vehicles (AUVs) is a very important and challenging task for the AUVs applications. In long baseline underwater acoustic localization networks, the accuracy of single-way range measurements is the key factor for the precision of localization of AUVs, whether it is based on the way of time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA). The single-way range measurements do not depend on water quality and can be taken from long distances; however, there are some limitations which exist in these measurements, such as the disturbance of the unknown current velocity and the outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on particle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA) localization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers during the localization process. Some simulation experiments are carried out to illustrate the performance of the proposed method compared with another localization algorithm.

Positioning technology of mobile vehicle using self-repairing heterogeneous sensor networks

Abstract Mobile vehicle positioning can provide the reference to navigation, tracking and multi vehicles collaboration. Applying spatiotemporal distribution characteristics of positioning errors between strap-down inertial navigation system (SINS) and wireless sensor network (WSN) approaches, a mobile vehicle positioning is proposed as a component of heterogeneous sensor networks (HSN). The attitude, velocity and position equations of mobile vehicle are derived based on the kinematics parameter constraints and inertial parameter errors. Meanwhile, WSN approach can provide position estimation using inaccurate anchor nodes. However, SINS is known for its cumulative errors over long time, while WSN approach can have large positioning errors in certain areas. As an effort to overcome the limitations of pure SINS or WSN approach, an integrated SINS and WSN approach is proposed to form a self-repairing HSN approach, which can provide sound position and attitude for mobile vehicle. Then, multi-parameter interaction and cooperative correction strategy are explored when SINS or WSN measurement is abnormal. Finally, a comprehensive set of experiments of position and attitude estimations for mobile vehicle are performed on the actual environment platform.

Robot path planning based on an improved genetic algorithm with variable length chromosome

In order to improve the adaptability of mobile robot path planning algorithm, a solution for robotic path planning method using improved genetic algorithm is proposed. In this method, the chromosome with variable length is introduced in the genetic algorithm. In addition, a new fitness function and three improved genetic operators are proposed in this study, including simplification operator, revision operator and substitution operator. The results of experiment show that the proposed algorithm is much more adaptive and feasible.

An evidence fusion approach for characterization of heterogeneous images under complex environment

Characterization, recognition under complex environment is a challenging task. The measured signal will be submerged by noise in complex environment, which makes it difficult to characterize targets, especially when the targets share the similar characteristics. Multi-sensor information fusion will improve characterization significantly and DS evidence theory is an effective approach in heterogeneous information fusion. However, evidence from multi-sensor information is always affected by subjective factors in the process of evidence fusion. In this paper, a new evidence fusion approach for improving characterization under complex environment is proposed. To characterize the heterogeneous images better, a concept of comprehensive credibility is introduced into the proposed approach and a new update rule of evidence is designed. Some experimental results show the efficiency and effectiveness of the proposed approach.

A Multi-agent Model of Lake Water Environment System Evolution and Its Simulation

Aimed at the shortcoming of the traditional theory and method in the analysis of lake water environment evolution mechanism, a method based on multi-agent was introduced. The lake water environment system is regarded as complex system, which is composed of many relatively self-governed agents. These agents behaviors were modeled and simulated to analyze the macroevolution process of lake water environment system. The feasibility of multi-agent method was discussed in emphases, and a lake water environment multi-agent model was proposed. Finally, a simulation experiment to analyze the evolutionary game behavior of the agents in our model was implemented, to prove the effectiveness of this model. The results of simulation verified the rationality of this method.