Francesco Capezio

GPS-based localization for a surveillance UGV in outdoor areas

The ANSER project (airport night surveillance expert robot) is described, exploiting a mobile robot for autonomous surveillance in civilian airports and similar wide outdoor areas. The paper focuses on the localization subsystem of the patrol robot: in contrast with most approaches in literature, we show how a positioning subsystem composed exclusively of a non-differential GPS unit (i.e., without inertial sensors) is enough to ensure accurate estimates of the robots position and orientation, under the assumption of nonholonomic kinematics.

Multi-Robot Uniform Frequency Coverage of Significant Locations in the Environment

The article shows that the Random Walk and Edge Counting algorithms allow to solve – under some constraints – the Multi-Robot Uniform Frequency Coverage problem, i.e., the problem of repeatedly visiting a set of locations in the environment with uniform frequency. Both algorithms have extremely low requirements in terms of computational power and memory storage, and do not require inter-robot communication. Their properties are described in details and formally demonstrated.

The ANSER project: Airport nonstop surveillance expert robot

This paper describes ANSER, a system designed to perform surveillance in civilian airports and similar wide outdoor areas. Whereas an intelligent system - possibly controlled by a human supervisor - is able to integrate the information originating from different sources (i.e., fixed devices and sensors distributed throughout the environment) and to coordinate their behaviors in case of anomalies, the mobile robot is a significant part of the overall system: its main subsystems, i.e., autonomous surveillance, localization (performed using only a non-differential GPS and a laser rangefinder) and navigation are investigated in depth. Experimental results validate the robustness and reliability of the approach.

A minimalist approach to path following among unknown obstacles

The article proposes a feedback control system for path following in presence of obstacles that is an extension of previous work and is made of two components: (i) a sensor-based, real-time model that generates and periodically updates the path on-line in order to avoid both known and unforeseen obstacles, and (ii) a feedback-control model that is capable of driving a unicycle vehicle along the collision free path. The system has some unique characteristics, among which it requires very few computational resources as a consequence of its extreme simplicity.

FAST POSITION TRACKING OF AN AUTONOMOUS VEHICLE IN CLUTTERED AND DYNAMIC INDOOR ENVIRONMENTS

Abstract This paper deals with map-based self-localization in a dynamic environment. In order to detect localization features, laser range finders traditionally scan a horizontal plane: we hypothesize the existence of a “low frequency cross-section” (informally, over people heads), where even highly dynamic environments become more “static” and “regular”. We show that, by accurately choosing the scanning plane, problems related to moving objects, occluded features, etc. are simplified. Experimental results showing hours of continuous work in a real-world scenario, with an accuracy of about 2.5cm and a speed of 0.6m/sec, demonstrate the reliability of the approach.

An augmented state vector approach to GPS-based localization

The ANSER project (Airport Night Surveillance Expert Robot) is described, exploiting a mobile robot for autonomous surveillance in civilian airports and similar wide outdoor areas. The paper focuses on the localization subsystem of the patrolling robot, composed of a non-differential GPS unit and a laser rangefinder for map-based localization (inertial sensors are absent). Moreover, it shows that an augmented state vector approach and an Extended Kalman filter can be successfully employed to estimate the colored components in GPS noise, thus getting closer to the conditions for the EKF to be applicable.

ANSER: Airport night surveillance expert robot

Abstract The paper describes the ANSER project, focusing on mobile robots for autonomous surveillance in civilian airports and similar wide outdoor areas. The robot is able to navigate autonomously both outdoor and indoor, with a localization sub-system composed only of a single Non-Differential GPS and a laser scanner (inertial sensors are absents). The paper shows that an augmented state vector approach and an Extended Kalman Filter can be successfully employed to estimate the colored components in GPS noise, thus getting closer to the conditions for the EKF to be applicable. The overall system capabilities are described with respect to a typical surveillance scenario, where ANSER is requested to recognize unexpected events through a laser rangefinder for outdoor security, and to consequently alert a human supervisor.