Research On Technologies Of Deployment And Self-Localization For Mobile Sensor Networks

As the cost reduced and the performance improved, using autonomous mobile robots as nodes of sensor network has become feasible. This kind of mobile sensor network has all functions that wireless distributed sensor network has. Its mobility increases the coverage range and upgrades the sensing ability of sensor network. Mobile sensor network can be extensively used in planetary exploration, military reconnaissance and disaster rescue. Multi-robots have such advantages as parallelism, flexibility, fault tolerance and data redundancy. Multi-robot coordination can extend the capability of the single robot.Supported by the national high-tech research and development plan of China,"Collaboration and Competition Mechanism for Distributed Multi-robots and its Application Techniques"and the NSF project,"Indoor Navigation Techniques of Mobile Robot based on Imprecise Map", this dissertation aims to exploiting on the application domains of the mobile robotics and systemically studies several key technologies existing in mobile sensor network by theroretical and simulated way. Several novel algorithms are proposed for mobile sensor nodes including coverage, deployment, self-localization and multi-target tracking.The features and present research for multi-robot system are introduced firstly. The concept, classification and existing problems for multi-robot coordination are briefly presented. The concepts, system architecture, species of topology structure and evaluation criterions of system for mobile sensor network composed of multi-robot are elicited. And the application domains, present research state and key problems required to resolve are introduced.Mobile sensor networks are constructed by mobile robots. The robots have the mobility to increase the coverage for sensed region. Several common dynamic coverage algorithms are introduced firstly. These algorithms are used in regulation regions. An algorithm based on coverage error is proposed to control the motion of mobile nodes to overcome the limitations of abnormity for sensed regions. This algorithm is based on the computation for the grads of coverage error to control the speed and direction for robots and the avoid collision between nodes are considered at the same time. The control rules are analysed by theoretically and verified by the simulations. The algorithm can be used in disaster rescue.Nodes deployment is an importmant subject for mobile sensor network domain. This paper introduces several common deployment algorithms for mobile sensor networks. And the mobile sensor nodes deployment is proposed based on local virtual force. The algorithm utilizes not only the distance informations among nodes but also the position informations through communication between nodes. This distributed control way for nodes moving to achieve the goal of nodes uniformly deployment and maximal coverage in the sensed area. This algorithm need not know the map information of environment previously. It can be used for battle field due to the distributed control effectness and self-adaptability.Sensor nodes are required to have the function of self-localization because the sensed targets usually associate with geographical information in sensor network. Self-localization algorithms based on range and range-free are introduced firstly. This paper presents a hybrid hops Monte Carlo self-localization algorithm (HMCL) based on Monte Carlo Localization. The algorithm may select proper localization strategy to compute the coordinates of nodes according to the numbers of anchor nodes in sensor network. The algorithm is self-adaptable and higher accuracy compared with other similar algorithms.The most extensive application domain is to observe and track dynamic targets by mobile sensor network composed of multi-robot. The Local Robot Target Density Alogrithm (LRTDA) is proposed based on the Artificial Potential Fields. Based on the local density of robots and targets, the control parameter of virtual force is adjusted for achieving to track multi-target. The algorithm improves the observation for multi-target and coverage for sensor network. Simulation shows the validity and feasibility for this algorithm. This algorithm can be used for these domains, such as reconnaissance in battle field and rescue in disaster area.