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In this paper, both the local minimum and the goal non-reachable with obstacles nearby (GNRON) problems have been solved on the artificial potential field (APF) for mobile robot path planning in a bounded environment. The GNRON problem occurs when the goal’s position is inside the circle of influence of the obstacle. This phenomenon gives rise to a larger repulsive force with regard to the attractive force. Therefore, a stronger attractive function is proposed to ensure that the robot reaches the target successfully. The local minimum problem occurs when the robot, the goal and the centre of the obstacle are aligned. This situation engenders a total potential force equal to zero before reaching the goal position. To overcome this problem, a novel of repulsive potential function is proposed by activating a virtual escaping force when a local minimum is detected. This force behaves as a rotational force allowing the robot to escape from the deadlock positions and turn smoothly away from obstacles in the direction of the target. The combination of the new attractive force and the novel repulsive force could solve the local minimum and the GNRON problems. Finally, some methodic simulations, based on a comparative study with other methods from the literature, are carried out to validate and demonstrate the effectiveness of the advanced potential field method to handle the local minimum and GNRON problems.

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We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.

This article propounds addressing the design of a sliding mode controller with adaptive gains for trajectory tracking of unicycle mobile robots. The dynamics of this class of robots are strong, nonlinear, and subject to external disturbance. To compensate the effect of the unknown upper bounded external disturbances, a robust sliding mode controller based on an integral adaptive law is proposed. The salient feature of the developed controller resides in taking into account that the system is MIMO and the upper bound of disturbances is not priori known. Therefore, we relied on an estimation of each perturbation separately for each subsystem. Hence, the proposed controller provides a minimum acceptable errors and bounded adaptive laws with minimum of chattering problem. To complete the goal of the trajectory tracking, we apply a kinematic controller that takes into account the nonholonomic constraint of the robot. The stability and convergence properties of the proposed tracking dynamic and kinematic controllers are analytically proved using Lyapunov stability theory. Simulation results based on a comparative study show that the proposed controllers ensure better performances in terms of good robustness against disturbances, accuracy, minimum tracking errors, boundness of the adaptive gains, and minimum chattering effects.

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