Abstract
An approach to intelligent robot motion planning and tracking in known and static environments is presented in this paper. This complex problem is divided into several simpler problems. The first is generation of a collision free path from starting to destination point, which is solved using a particle swarm optimization (PSO) algorithm. The second is interpolation of the obtained collision-free path, which is solved using a radial basis function neural network (RBFNN), and trajectory generation, based on the interpolated path. The last is a trajectory tracking problem, which is solved using a proportional-integral (PI) controller. Due to uncertainties, obstacle avoidance is still not ensured, so an additional fuzzy controller is introduced, which corrects the control action of the PI controller. The proposed solution can be used even in dynamic environments, where obstacles change their position in time. Simulation studies were realized to validate and illustrate this approach.
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