Abstract. Nowadays there are some path planning algorithms for mobile robot which have been documented and explained individually in detail such as A*, LPA*, D* and D* Lite. However, there is still a lack of a comparative analysis of these algorithms. Therefore, in this paper a research of comparing A* and D* Lite algorithm for AGV's path planning is conducted by using simulation and experiment. The goal is to compare the characteristic of each algorithm when they are applied in a real differential drive AGV and give the reader a guide in choosing algorithms for their own planning domains. The emphasis of this comparison is on the computation time of generating trajectory and the distance of the generated trajectory. The simulation and experimental results show that generally D* Lite can plan the shorter path with faster computation time than A*. However, there are some cases when D* Lite is less effective than A*. It means which of the algorithms should be chosen depends on the requirement of the system.
A three-linked manipulator mounted on a two-wheeled mobile platform is used to weld a long curved welding path. A welding torch mounted at the end of a manipulator of the welding mobile manipulator (WMM) must be controlled for tracking a welding path with constant velocity and constant welding angle of torch. In this paper, a decentralized control method is applied to control the WMM considered as two separate subsystems such as a mobile platform and a manipulator. Two decentralized motion controllers are designed to control two subsystems of WMM, respectively. Firstly, based on a tracking error vector of the manipulator and a feedback motion of the mobile platform, a kinematic controller is designed for manipulator. Secondly, based on an another tracking error vector of the mobile platform and a feedback angular velocities of revolution joints of three-link, a sliding mode controller is designed for the mobile platform. These controllers are obtained based on the Lyapunov's function and its stability condition to ensure for the tracking error vectors to be asymptotically stable. Furthermore, simulation and experimental results are presented to illustrate the effectiveness of the proposed algorithm.
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