Antonio Grano - Academia.edu (original) (raw)

Papers by Antonio Grano

2019 IEEE 15th International Conference on Control and Automation (ICCA), 2019

We compare the effectiveness of two widely used filters for nonlinear systems, i.e., the Extended... more We compare the effectiveness of two widely used filters for nonlinear systems, i.e., the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF), in reconstructing the unknown environment where a mobile robot moves. The reconstruction is obtained by a novel cells-covering algorithm that only uses the distance measurements taken from the robot’s on-board sonar sensors. We show that, despite the superior theoretical properties of the UKF, both filters perform comparably well, and that the proposed algorithm provides good localization performance and a reliable environment reconstruction.

2015 European Control Conference (ECC), 2015

Journal of Civil Structural Health Monitoring, 2015

2013 16th International Conference on Advanced Robotics (ICAR), 2013

In this work the Simultaneous Localization And Mapping (SLAM) problem for a mobile robot placed i... more In this work the Simultaneous Localization And Mapping (SLAM) problem for a mobile robot placed in an unknown indoor environment is faced. The environment is modeled as a set of polynomials used as SLAM landmarks. A polynomial based mapping algorithm is proposed and used along with an Extended Kalman filter to yield a solution to the SLAM problem. The filter updates the robot position and orientation estimation and the environment mapping using sparse measurements taken from a set of on board ultrasonic sensors. The proposed algorithm has been evaluated in both numerical and experimental tests obtaining very good estimation and mapping results.

2013 10th IEEE International Conference on Control and Automation (ICCA), 2013

In this work the localization of a mobile robot in an unknown environment is faced. A new version... more In this work the localization of a mobile robot in an unknown environment is faced. A new version of the Extended Kalman Filter (EKF) is presented. The proposed EKF uses both measurements provided by robot on board and out of board sensors in order to emphasize the qualities and overcome the defects of such sensors. Moreover assuming a polynomial model for the robot surrounding environment bounds, an online algorithm able to build a map of this environment is presented. The proposed algorithms are tested in a numerical way contrasting them with a classical Extended Kalman Filter based only on the out of board sensors and with a fusing algorithm related only on the on board sensors.

2013 10th IEEE International Conference on Control and Automation (ICCA), 2013

In this work the mobile robot localization problem in an unknown environment is faced. To solve t... more In this work the mobile robot localization problem in an unknown environment is faced. To solve this problem, an Extended Kalman Filter, based on measurements taken from ultrasonic sensors and only on local data, with no assumption on robot's working environment, is proposed. A new model for the ultrasonic sensors is proposed and validated through experimental tests. A switching sensors activation policy, based on the physical characteristics of ultrasonic sensors, is devised. Such policy allows power saving still achieving good estimation performance. Experimental tests, using the robot Khepera III, show the effectiveness of the proposed sensors switching policy in solving the mobile robot localization problem.

2013 16th International Conference on Advanced Robotics (ICAR), 2013

In this work the mobile robot localization problem in an unknown environment is faced and a new v... more In this work the mobile robot localization problem in an unknown environment is faced and a new version of the Extended Kalman filter is proposed to estimate the robot position and orientation. This new filter uses a convex combination of two filters estimating the same state variables. The first filter is based on measurements provided by robot on board distance sensors while the second one uses out of board distance sensors measurements. The resulting "Mixed" Kalman filter is designed to emphasize the qualities and overcome the defects of each used sensor. The proposed fusing technique has been tested in a real experimental framework using the robot Khepera III. The algorithm has been contrasted with other Extended Kalman filters, based on the on board and on the out of board sensors measurements, yielding to encouraging results.

Proceedings of the 19th IFAC World Congress, 2014

In this paper a novel solution to the Simultaneous Localization and Mapping (SLAM) problem for a ... more In this paper a novel solution to the Simultaneous Localization and Mapping (SLAM) problem for a team of mobile robots is proposed. The algorithm aims at approximating the robots surrounding environment by a set of polynomials, ensuring high mapping performance and low communication cost. To this sake, once two robots meet, only the acquired polynomials data are exchanged. Also, the algorithm has been developed trying to minimize each robot computational requirements so that it can be implemented in a decentralized way. Numerical simulations are reported to show the effectiveness of the proposed solution.

53rd IEEE Conference on Decision and Control, 2014

In this work a novel solution to the Simultaneous Localization and Mapping (SLAM) problem for a m... more In this work a novel solution to the Simultaneous Localization and Mapping (SLAM) problem for a mobile robot moving in an unknown indoor environment is proposed. The algorithm uses an Extended Kalman filter and a set of polynomials to map the robot surrounding environment boundaries. The main idea behind the proposed SLAM solution is to use the SLAM landmark extraction process to map the environment boundaries shape and the Kalman filter to estimate boundaries position. The algorithm uses measurements taken from a set of distance sensors placed on the robot. The proposed method has been evaluated in both numerical and experimental tests obtaining satisfactory estimation and mapping results.

2019 IEEE 15th International Conference on Control and Automation (ICCA), 2019

We compare the effectiveness of two widely used filters for nonlinear systems, i.e., the Extended... more We compare the effectiveness of two widely used filters for nonlinear systems, i.e., the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF), in reconstructing the unknown environment where a mobile robot moves. The reconstruction is obtained by a novel cells-covering algorithm that only uses the distance measurements taken from the robot’s on-board sonar sensors. We show that, despite the superior theoretical properties of the UKF, both filters perform comparably well, and that the proposed algorithm provides good localization performance and a reliable environment reconstruction.

2015 European Control Conference (ECC), 2015

Journal of Civil Structural Health Monitoring, 2015

2013 16th International Conference on Advanced Robotics (ICAR), 2013

In this work the Simultaneous Localization And Mapping (SLAM) problem for a mobile robot placed i... more In this work the Simultaneous Localization And Mapping (SLAM) problem for a mobile robot placed in an unknown indoor environment is faced. The environment is modeled as a set of polynomials used as SLAM landmarks. A polynomial based mapping algorithm is proposed and used along with an Extended Kalman filter to yield a solution to the SLAM problem. The filter updates the robot position and orientation estimation and the environment mapping using sparse measurements taken from a set of on board ultrasonic sensors. The proposed algorithm has been evaluated in both numerical and experimental tests obtaining very good estimation and mapping results.

2013 10th IEEE International Conference on Control and Automation (ICCA), 2013

In this work the localization of a mobile robot in an unknown environment is faced. A new version... more In this work the localization of a mobile robot in an unknown environment is faced. A new version of the Extended Kalman Filter (EKF) is presented. The proposed EKF uses both measurements provided by robot on board and out of board sensors in order to emphasize the qualities and overcome the defects of such sensors. Moreover assuming a polynomial model for the robot surrounding environment bounds, an online algorithm able to build a map of this environment is presented. The proposed algorithms are tested in a numerical way contrasting them with a classical Extended Kalman Filter based only on the out of board sensors and with a fusing algorithm related only on the on board sensors.

2013 10th IEEE International Conference on Control and Automation (ICCA), 2013

In this work the mobile robot localization problem in an unknown environment is faced. To solve t... more In this work the mobile robot localization problem in an unknown environment is faced. To solve this problem, an Extended Kalman Filter, based on measurements taken from ultrasonic sensors and only on local data, with no assumption on robot's working environment, is proposed. A new model for the ultrasonic sensors is proposed and validated through experimental tests. A switching sensors activation policy, based on the physical characteristics of ultrasonic sensors, is devised. Such policy allows power saving still achieving good estimation performance. Experimental tests, using the robot Khepera III, show the effectiveness of the proposed sensors switching policy in solving the mobile robot localization problem.

2013 16th International Conference on Advanced Robotics (ICAR), 2013

In this work the mobile robot localization problem in an unknown environment is faced and a new v... more In this work the mobile robot localization problem in an unknown environment is faced and a new version of the Extended Kalman filter is proposed to estimate the robot position and orientation. This new filter uses a convex combination of two filters estimating the same state variables. The first filter is based on measurements provided by robot on board distance sensors while the second one uses out of board distance sensors measurements. The resulting "Mixed" Kalman filter is designed to emphasize the qualities and overcome the defects of each used sensor. The proposed fusing technique has been tested in a real experimental framework using the robot Khepera III. The algorithm has been contrasted with other Extended Kalman filters, based on the on board and on the out of board sensors measurements, yielding to encouraging results.

Proceedings of the 19th IFAC World Congress, 2014

In this paper a novel solution to the Simultaneous Localization and Mapping (SLAM) problem for a ... more In this paper a novel solution to the Simultaneous Localization and Mapping (SLAM) problem for a team of mobile robots is proposed. The algorithm aims at approximating the robots surrounding environment by a set of polynomials, ensuring high mapping performance and low communication cost. To this sake, once two robots meet, only the acquired polynomials data are exchanged. Also, the algorithm has been developed trying to minimize each robot computational requirements so that it can be implemented in a decentralized way. Numerical simulations are reported to show the effectiveness of the proposed solution.

53rd IEEE Conference on Decision and Control, 2014

In this work a novel solution to the Simultaneous Localization and Mapping (SLAM) problem for a m... more In this work a novel solution to the Simultaneous Localization and Mapping (SLAM) problem for a mobile robot moving in an unknown indoor environment is proposed. The algorithm uses an Extended Kalman filter and a set of polynomials to map the robot surrounding environment boundaries. The main idea behind the proposed SLAM solution is to use the SLAM landmark extraction process to map the environment boundaries shape and the Kalman filter to estimate boundaries position. The algorithm uses measurements taken from a set of distance sensors placed on the robot. The proposed method has been evaluated in both numerical and experimental tests obtaining satisfactory estimation and mapping results.