Research On Path Planning And Trajectory Tracking Of Autonomous Mobile Robot

Mobile robot system, as an integrative subject with updated research results in mechanical, electronic, computer, automatic control, and artificial intelligence, represents tremendous success of mechatronics. In recent years, mobile robot system has become an important research area in automation, computer, and artificial intelligence. Comparing with traditional industrial robot, the mobile robot with self-perception, decision making, and performance function has vaster application prospect. Mobile robot has huge advantages over human being in the fields of national defense, industrial and agriculture manufacture, and hazard. This dissertation studies the path planning and trajectory tracking of an autonomous mobile robot, it mainly contains:1. The development history of robot and the characteristic of mobile robot are briefly narrated. The technology of the architecture of mobile robot, multi-sensor information fusion, self-localization, path planning and trajectory tracking are summarized in the round.2. A self-localization method in the presence of measurement noise and model disturbance for autonomous mobile robot based-on heterogeneous sensor information fusion is proposed. First, the motion model of the autonomous mobile robot and observed model of CCD vidicon are established. The optimal state estimation is derived, random noises are overcome, and self-localization is realized by extended Kalman filter. Then, observed model of the ultrasonic sensor is established. The data from CCD vidicon and ultrasonic sensor are fused by BP neural network. The mutual aided of the tow kinds of sensors is realized. The simulation results show that the self-localization precision of the autonomous mobile robot is obviously improved by heterogeneous sensor information fusion. 3. Under the specific environments, a novel variable-length decimal encoding mechanism for the paths of the mobile robot is proposed, some genetic operators corresponding to the encoding mechanism are designed at the same time, and genetic algorithm is utilized to program an optimal path of mobile robot in the static environment, simulation results demonstrate the effectiveness of the algorithm. Principle of the basic ant colony algorithm is briefly described, and path planning for mobile robot based on the self-adaptive ant colony algorithm is studied.4. Combining the neural network, particle swarm optimization algorithm is utilized to program an optimal path of mobile robot in the dynamic environment. The dynamic environmental information in the workspace for a robot is described by a neural network model, using this model, the relationship between dynamic obstacle avoidance and the output of the model is established, then the two-dimensional coding for the planned path is simplify to one-dimensional one, and the particle swarm optimization is introduced to get an optimized collision-free path. Then, the introduction of the basic principle of distance-propagation algorithm is given, aiming at pursuing the locomotive target, by utilizing the distance-propagation algorithm, we can program the optimal path of mobile robot in the dynamic environment quickly.5. The optimal state feedback control for trajectory tracking control of a four-wheel mobile robot is studied in the presence of measure noise, model noise and input signal noise as well. At first, the kinematic and dynamic model and trajectory generation of the omnidirectional vehicle have been established. Then an optimal state feedback controller based on Lyapunov stability using the Kalman filter state estimation technique is derived. This is followed by an adaptive control algorithm to compensate for the effects of input signal noise. Simulated results are presented in the paper to highlight the effectiveness of the proposed control algorithm.