Research On Map Building And Autonomous Exploration Strategies For Mobile Robots

Mobile robots have been widely used on industry, agriculture, military, hospital, healthcare and et al to expand human's ability and release them from hard and dirty work. As the complexity of the environment robot applied increasing and robotics technology improving, human expect more intelligent and autonomous mobile robots. Especially in unknown large-scale extreme or hard environment, autonomous robots must posses the ability to explore their environments, build representations of those environments, and then use those environments to navigate effectively in those environments. Consequently in the last two decades, the problems associated with autonomous exploration of mobile robots operating in unknown environments have attracted the attention of many researchers. In this thesis, the challenging problems of map building, exploration of mobile robots are addressed and some solutions are provided. The findings are verified through simulation and real world experimental trails on Pioneer 2-DXE mobile robot. The main studies undertaken in this thesis are listed as following.Firstly, the definition and development of mobile robot is surveyed, and some typical series intelligent mobile robot and general mobile robot platforms for research are presented; then, the character and principle of some popular sensors for mobile robot are discussed; subsequently, key technologies and hot topics in robotics are addressed. Based on those research backgrounds, the necessity and feasibility of research work about this thesis are introduced.In the second chapter, the configuration and design of Pioneer 2-DXE's hardware and sensors equipped are described in detail; then the control scheme and software development tools for Pioneer robots are presented; finally the motion control math model and accumulative position error model of Pioneer robot are comprehensively addressed.Due to its efficiency, one of the most popular and successful map representations is occupancy grid. Occupancy grids could be built based on laser range-finders, stereo vision, and sonar sensors. Sonar sensors are commonly used due to operation simplicity, robustness, and low price. However sonar readings are prone to several measuring errors due to various phenomena (e.g., multiple reflections, wide radiation cone, and low angular resolution). This paper presents an improved neural network model for sonar readings interpretation to build occupancy grids of mobile robot. The proposed model interprets sensor readings in the context of their space neighbors and relevant successive history readings simultaneously. Therefore, if current readings are produced by multiple reflections or specular reflection, this neural network model could depend on relevant history readings to obtain the correct occupancy values. Consequently the presented method can greatly weaken the effects by multiple reflections or specular reflection. The output of the above neural network is the vector about probabilities of three possible statuses (empty, occupancy, uncertainty) for the cell. As for sensor readings integration, three probabilities of cell's status are updated by the Bayesian update formula respectively, and the final status of cell is defined by Max-Min principle. Therefore, it integrates the sonar readings more accurately while keeping appropriate computation cost. Experiments results performed in lab environment has shown occupancy map built by proposed approach is more consistent, accurate and robust than traditional method while it still could be conducted in real time.One of the main concerns of modern robotics is efficient exploration and map representation of large unknown environment. Based on the deeply analysis of currently autonomous exploration and corresponding map model, this thesis introduces an efficient hybrid map representation that integrates the metric and topological paradigms. In this model, global environment is representing in topological model. Each topological node is first derived by improve quartree algorithm from local grids in proper interval, and then fusion of homogeneous neighbor nodes is applied to achieve consist and compact global topological node. The metric gird information of node is stored in corresponding parent node according to quartree structure. As the mobile robot explores more and more unknown area, new local grids is continuously created and the global topological map is updated and expanded until the exploration is finished. This hybrid hierarchical map model produces accurate environment representations while avoiding suffer from huge data volume and computing burden. In addition, the topological and metric information preserving simultaneously could meet different level requirement about exploration, navigation and path-planning and so on.On the basis of above proposed hybrid hierarchical map model, this thesis introduces an autonomous exploration algorithm for large scale unknown environments. Exploration planning is performed at two levels: global planning is performed at topological level and local planning is performed at metric level. As for local planning, exploration utility function is defined by the length of exploration frontier border line and travel distance from current robot position to frontier, where exploration frontier border line is calculated by improved fuzzy c-means clustering algorithm and travel distance is derived by modified Distance Transform method. When the switch conditions from local planning to global planning is satisfied, global topological map is updated according to the proposed hybrid hierarchical map model. As for global planning, the next best exploration node is selected from frontier node set according to the global exploration utility function. Once the nest exploration node is chosen, the new local planning is created on the basis of the metric information corresponding to the nest exploration node. The global exploration utility function is chosen with a multiplicative form, which prefers exploring more new unexplored areas, shorter travel distance, straighter path, and smaller proportion of neighbor obstacle areas and so on. Local planning and global planning is alternately conducted until the whole environment is known. This exploration algorithm can be performed in a fast and efficient way as the local exploration can be conducted in real time and global planning assures the robot escaping from local trap. The method has been successfully tested for Pioneer 2-DXE mobile robot in simulated environments.In order to fast and accurate path tracking or navigate mobile robot to target position without collision, this thesis proposed a fast navigation and obstacle avoiding algorithm based on fuzzy reasoning and behavior control. At first, possible behaviors encountered during robot navigation are defined. Consequently, the fuzzy navigation controller is designed. Based on this fuzzy controller, the mobile robot's behaviors are switched and scheduled according to sonar readings collected, target position, robot position and et al. This algorithm has been tested on five navigation tasks under two different environments. Experiments results have been shown that the introduced method has good tracking accuracy and navigation performance, and is reliable to different environments.Simultaneous localization and mapping (SLAM) algorithm for mobile robots is a key problem in the field of robotics. This paper surveyed the latest progress of SLAM algorithms and described the key techniques adopted by popular different SLAM modes. In addition, those methods were analyzed and compared in detail according to map-building model, computation complexity, and robustness and so on. Finally, the key problems and future research trend of SLAM approaches were presented.