International audience

One important problem in autonomous robot navigation is the effective following of an unknown path traced in the environment in compliance with the kinematic limits of the vehicle, i.e., bounded linear and angular velocities and accelerations. In this case, the motion planning must be implemented in real-time and must be robust with respect to the geometric characteristics of the unknown path, namely curvature and sharpness. To achieve good tracking capability, this paper proposes a path following approach based on a fuzzy-logic set of rules which emulates the human driving behavior. The input to the fuzzy system is represented by approximate information concerning the next bend ahead the vehicle; the corresponding output is the cruise velocity that the vehicle needs to attain in order to safely drive on the path. To validate the proposed algorithm two completely different experiments have been run: in the first experiment, the vehicle has to perform a lane-following task acquiring lane information in real-time using an onboard camera; in the second, the motion of the vehicle is obtained assigning in real-time a given time law. The obtained results show the effectiveness of the proposed method

The application of the generalized minimum variance approach to the design of an implicit self-tuning with pole-assignment nodal voltage regulator in electrical power systems is presented. The regulator embeds a difference filter to counteract the effects of a constant disturbance term present in the proposed power system model. The difference filter and the disturbance term generate equivalent terms in the generalized pseudo-output which must be accounted for into the estimation algorithm. Implementation aspect and simulation results are finally discussed and reported

For a mobile robot, odometry calibration consists of the identification of a set of kinematic parameters that allow reconstructing the vehicle's absolute position and orientation starting from the wheels' encoder measurements. This paper develops a systematic method for odometry calibration of differential-drive mobile robots. As a first step, the kinematic equations are written so as to underline linearity in a suitable set of unknown parameters; thus, the least-squares method can be applied to estimate them. A major advantage of the adopted formulation is that it provides a quantitative measure of the optimality of a test motion; this can be exploited to drive guidelines on the choice of the test trajectories and to evaluate accuracy of a solution. The proposed technique has been experimentally validated on two different mobile robots and, in one case, compared with other existing approaches; the obtained results confirm the effectiveness of the proposed calibration method.

The presence of joint velocity and acceleration limits must be taken into account by the inverse kinematics of robot manipulators, so as to avoid incorrect task execution when these are violated. To solve this problem, a novel algorithmic approach to kinematic control is presented in this paper, which guarantees that the joint variables do not overtake their limits. The proposed technique is based on a new second-order inverse kinematics algorithm, which enables the handling of velocity and acceleration constraints while tracking the desired end-effector path. The goal is achieved by suitably slowing down the task-space trajectory via a time warp when joints limits are encountered. The proposed method is designed for online applications, i.e., the desired trajectory is not known in advance, and requires a light computational burden. The application of the proposed approach is finally illustrated in experiments implemented on a six-degree-of-freedom industrial robot manipulator.

This paper presents an experimental study concerning the application of a real-time motion planning algorithm to a unicycle-like mobile robot. The desired trajectory to be followed by the mobile robot in presence of bounds on the linear/angular velocities and accelerations is available online. Moreover, with the respect to the kinematic constraints, the desired path has to be kept as long as possible. The implemented algorithm is based on a discrete-time kinematic control which implements a warping of the time law based on the definition of a virtual time. In addition, a fuzzy inference system handles the additional information given by the difference between the virtual and real time in order to exploit the knowledge in advance of the desired path. The experimental results confirm the effectiveness of the adopted algorithm.

An adaptive control law for autonomous underwater vehicles (AUVs) is presented. Usually, the control laws are designed with respect to a mathematical model expressed either in the Earth-fixed frame or in the vehicle-fixed frame. These two approaches, however, do not take into account the different origin of the effects that can affect the steady state errors: namely, the restoring generalized forces and the ocean current. With the use of a suitable adaptive action those effects can be properly taken into account as is shown in the paper.