Self-Organizing System Forming Strategy of the Global Behavior for Control of an Autonomous Mobile Robot

Autonomous mobile robots typically require a preconceived and very detailed navigational model (map) of their intended operating emironment. It requires the presence of a priori information. The creating world model of eironment is a difficult problem requiring a detailed description of all possible routes of the robot motion. Therefore, it is better to describe a behaviour strategy that robot can create the world model of emironment itself during exploration of an unknown territory to achieve efficiently the target from any start position in the future. This strategy assumes fuli self-organization and self-adaptation to the emironment. This paper describes an architecture of such system closely connected with neural network solving the shortest path problem. Such interconnection allows determining the global strategy of the robot behaviour parallel with local strategy formed by reactive navigational system

Discrete Globalised Dual Heuristic Dynamic Programming in Control of the Two-Wheeled Mobile Robot

Network-based control systems have been emerging technologies in the control of nonlinear systems over the past few years. This paper focuses on the implementation of the approximate dynamic programming algorithm in the network-based tracking control system of the two-wheeled mobile robot, Pioneer 2-DX. The proposed discrete tracking control system consists of the globalised dual heuristic dynamic programming algorithm, the PD controller, the supervisory term, and an additional control signal. The structure of the supervisory term derives from the stability analysis realised using the Lyapunov stability theorem. The globalised dual heuristic dynamic programming algorithm consists of two structures: the actor and the critic, realised in a form of neural networks. The actor generates the suboptimal control law, while the critic evaluates the realised control strategy by approximation of value function from the Bellman’s equation. The presented discrete tracking control system works online, the neural networks’ weights adaptation process is realised in every iteration step, and the neural networks preliminary learning procedure is not required. The performance of the proposed control system was verified by a series of computer simulations and experiments realised using the wheeled mobile robot Pioneer 2-DX