| Robots are used more and more widely and deeply with the development of artificial intelligence in recent years. Intelligent mobile robot is a kind of robot systems can completing the work function through the sensor to sense the environment and its own state to realize autonomous motion target oriented in the obstacle environment. Robot in practical application are facing more unknown environment. Research on control theory and method in the navigation of mobile robot navigation for the certainty of environment has made a lot of research on the application of the results. Navigation control for unknown environment also has some research and method, but has not yet formed a unified and perfect system. Mobile robots in unknown environments with only less prior knowledge, its navigation relates to perceive the environment, optimization of the navigation decision, and the knowledge representation and acquisition etc..Running in the unknown environment, need the contradiction between real-time control of large amount of calculation and intelligent robot required for reasonable solutions to environmental modeling and overall planning. Because of the lag of the environment model is established, and the environment is often dynamic, using a global map in a large-scale environment navigation is often difficult to achieve in the actual operation. For a work area is not too big environment, regional planning may have equivalent to global planning objectives to be achieved. The existing algorithms as can be applicable to complex environment, but they are based on the premise of environment known or most of it known, in the event of unknown obstacles originally established heuristic distance field distribution will be changed, it needs to recalculate the potential distribution. In the unknown environment, the robot can only observe part of the surroundings through limited sensor, and explored the information through the accumulation of information retained, but often know nothing about the nearby areas of ultimate goal.In order to improve the efficiency of real-time navigation of robot, we use the LMS511 laser rangefinder of SICK as sensor, our anti riot robot collects -5?185 degree of unknown environment map information, then translate the laser range finder polar coordinate information into Cartesian coordinate information to build the environment map model of dynamic grid method to occupy.We use grid map to combine the obstacle distance information of laser range finder and robot pose information of dead reckoning.Then we combine the fuzzy logic method and Maximum likelihood estimation method, and use the method of map matching to improve the performance of mobile robot self localization method.Then we use C++ as the programming language and use the improved D* algorithm in reverse which including improvements in three aspects:weighted Manhattan distance, obstacle density and the target density, to realize real-time path planning and navigation of intelligent mobile robot in the VS2010 platform based on MFC and realize convenient and easy operation of the PC software interface which can real-time dynamic display the robot scan to roads, the current path planning and state of the robot.This paper uses the improved D* algorithm in reverse and multithreading to realize real-time composite navigation of robots in unknown environment.For along the planned path of motion,we use the combination of tracking control law design method based on backstepping and controller synthesis based on fuzzy sliding mode variable structure.So as to improve the motion performance of the robot and realize the free interface with robot host computer software.And because of the use of multithreading, the following three aspects such as environment modeling, real-time navigation of mobile robot, motion control is interrelated and mutually independent, which greatly improves the system operation efficiency, so as to enhance the mobile robot with robustness and fault tolerance in unknown environment. |
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