Research Of Path Planning On Mobile Robot Using Local Sensor

The Mobile Robot Path Planning Problem is one of the three basic problems on mobile robot navigation, which describes the way that mobile robot walks from the starting point to the target point while avoiding obstacles and overcoming inner constraints. In general, the path planning was based on locating. But in the actual scene, it was difficult for mobile robot to get overall and accurate position information because of the limitations of mobile robot sensors and other factors in which condition mobile robot had to use local sensors for local path planning. The local mobile robot path planning used to be considered in the opposite view of the global path planning. Nowadays, there have been many kinds of local path planning methods, and many researchers have analyzed the performance. But they did not compare and integrate these methods into theories, which was not helpful to the further research on Local Path Planning Problem.The paper mainly did the following work aiming at the Robot Obstacle Avoidance Problem:1. We reviewed the methods for local path planning of mobile robot at present, discussed the modeling method for FIRA robot obstacle avoidance scene, proposed super Bug environment model, and analyzed relative local path planning algorithms.2. Based on MRPT library, we implemented a simple simulation software system. Then, we analyzed and compared the performance of VFF algorithm, NG algorithm, basic TangentBug algorithm and improved TangentBug algorithm based on the memory movement direction in algorithm complexity, optimization capability, path length and robustness, which were displayed in the performance league table.3. As for the high complexity of improved TangentBug algorithm based on the memory movement direction, we proposed improved TangentBug algorithm based on LTG following-wall detection method, and summarized the implement approaches of TangentBug algorithm. The experiment showed the improved performance.4. Aiming at the selection problem of following-wall direction, we considered of the improved search ability using available local information especially when the perception scope was enough large. We proposed the selection method based on the free area information.5. The key issues and implementation methods based on monocular vision algorithm applied to the game environment of NAO avoidance were discussed. Especially, the method based on detection of the free area was proposed. The robot using the method did not need complex object recognition and corner detection method, which greatly improves the performance of the algorithm. What’s more, the way offered good support for all kinds of two dimensional obstacle avoidance algorithm to be applied to the obstacle avoidance situation.6. The improved TangentBug algorithm was applied to the real environment of NAO robot obstacle avoidance, which showed good results. According to the time complexity, obstacle crossing capability and robustness, the results show that the improved algorithm was indeed valid.