No abstract

Human motion models are finding an increasing number of novel applications in many different fields, such as building design, computer graphics and robot motion planning. The Social Force Model is one of the most popular alternatives to describe the motion of pedestrians. By resorting to a physical analogy, individuals are assimilated to point-wise particles subject to social forces which drive their dynamics. Such a model implicitly assumes that humans move isotropically. On the contrary, empirical evidence shows that people do have a preferred direction of motion, walking forward most of the time. Lateral motions are observed only in specific circumstances, such as when navigating in overcrowded environments or avoiding unexpected obstacles. In this paper, the Headed Social Force Model is introduced in order to improve the realism of the trajectories generated by the classical Social Force Model. The key feature of the proposed approach is the inclusion of the pedestrians’ heading into the dynamic model used to describe the motion of each individual. The force and torque representing the model inputs are computed as suitable functions of the force terms resulting from the traditional Social Force Model. Moreover, a new force contribution is introduced in order to model the behavior of people walking together as a single group. The proposed model features high versatility, being able to reproduce both the unicycle-like trajectories typical of people moving in open spaces and the point-wise motion patterns occurring in high density scenarios. Extensive numerical simulations show an increased regularity of the resulting trajectories and confirm a general improvement of the model realism.

No abstract

-One of the most accurate phasor estimation procedures recently proposed in the literature is the so-called Taylor Weighted Least Squares (TWLS) algorithm, which relies on a dynamic phasor model of an electrical waveform at nominal frequency. In this paper an extension of the TWLS algorithm (called Generalized TWLS, or GTWLS, algorithm)

Many techniques for robot localization rely on the assumption that both process and measurement noises are uncorrelated, white and normally distributed. However, if this assumption does not hold, these techniques are no longer optimal and, in addition, the maximum estimation errors can be hardly kept under control. In this paper, this problem is addressed by means of a tailored Extended H∞ filter (EHF) fusing odometry and gyroscope data with position and heading measurements based on Quick Response (QR) code landmark recognition. In particular, it is shown that, by properly tuning EHF parameters and by using an adaptive mechanism to avoid finite escape time phenomena, it is possible to achieve a higher localization accuracy than using other dynamic estimators even if QR codes are detected sporadically. Also, the proposed approach ensures a good trade-off in terms of computational burden, convergence time and deployment complexity.

No abstract

In this paper we consider the mobile robot parking problem, i.e., the stabilization of a wheeled vehicle to a given position and orientation, using only visual feedback from low-cost cameras. We take into account the practically most relevant problem of keeping the tracked features in sight of the camera while maneuvering to park the vehicle. This constraint, often neglected in the literature, combines with the non-holonomic nature of the vehicle kinematics in a challenging controller design problem. We provide an effective solution to such a problem by using a combination of previous results on non-smooth control synthesis and recently developed hybrid control techniques. Simulations and experimental results on a laboratory vehicle are reported, showing the practicality of the proposed approach.

No abstract