Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.

This article deals with the design of an optimal tracking controller for a wheeled mobile robot. The tracking control can be performed to track either a given or a planned trajectory. In our study, an improved linear quadratic tracker is adopted to track a path planned using an improved reactive approach that combines the dynamic window with the fuzzy logic to make the robot movement toward the target faster, smoother, and safer whatever the complexity of the environment. The fuzzy logic is used to dynamically adjust the weights of the terms included in the dynamic window objective function according to different environmental scenarios. Simulation results of the path planning and the tracking control prove that the proposed approaches are significantly superior to the conventional ones.

This paper presents a trajectory tracking control approach for a mobile robot in the presence of static obstacles on the reference trajectory. The tracking control is developed based on the fuzzy parallel distributed compensation scheme where the linear quadratic regulator is used as a state feedback controller for each subsystem. The obstacle avoidance task is accomplished by applying a linear quadratic regulator on the overall open loop Takagi-Sugeno (T-S) fuzzy system, in which the weighting matrices are adjusted dynamically using a fuzzy controller. A fuzzy fusion controller is designed to combine the tracking and the obstacle avoidance velocities in order to perform both tasks simultaneously. Simulation results illustrate the effectiveness of the proposed control strategy.