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.
In this paper, an adaptive high-order sliding mode controller-observer is proposed for multi input-multi output uncertain systems with application to robotic manipulators. Firstly, a type-2 Takagi-Sugeno fuzzy system is used to model the original system. Next, with the unavailability of velocity measurement, a fuzzy high-order sliding mode observer is designed to estimate both the joint velocities and uncertainties. Moreover, based on a super twisting second-order sliding mode, the proposed robust controller generates smooth control and guarantees finite-time convergence to the third-order sliding set with respect to the sliding variable by keeping second-order sliding mode constraint. The controller gains are generated based on an adaptive estimation scheme without overestimation. The finite-time stability of the suggested controller is proved by using an homogeneous and strict Lyapunov function. Finally, the obtained simulation results for two-link robot manipulators demonstrate the effectiveness of the proposed controller.
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.
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