Abstract
In this paper, robots equipped with two complementary typologies of redundant sensors are considered: one typology provides sharp measures of some geometrical entity related to the robot pose (e.g., distance or angle) but is not univocally associated with this quantity; the other typology is univocal but is characterized by a low level of precision. A technique is proposed to properly combine these two kinds of measurement both in a stochastic and in a deterministic context. This framework may occur in robotics, for example, when the distance from a known landmark is detected by two different sensors, one based on the signal strength or time of flight of the signal, while the other one measures the phase-shift of the signal, which has a sharp but periodical dependence on the robot-landmark distance. In the stochastic case, an effective solution is a two-stage extended Kalman filter (EKF) which exploits the precise periodic signal only when the estimate of the robot position is sufficiently precise. In the deterministic setting, an approach based on a switching hybrid observer is proposed, and results are analyzed via simulation examples.
Highlights
Recent technology advances allow to create robots equip‐ ped with several kinds of sensors, often providing meas‐ ures related to the same physical quantity but characterized by quite different and complementary characteristics
An illustrative example in this direction which can be used to introduce the subject is a RFID-based robot localization problem where a reader, installed on the robot, can measure some quantities depending on the distance from a set of RFID tags located in the environment, which act as known landmarks
RFID data used for locali‐ zation vary from a binary information, e.g., [1, 2, 3, 4, 5], to a more complete set of information, like received signal strength indication (RSSI), e.g., [6, 7, 8], or phase shift of the signal, e.g., [9, 10]
Summary
Recent technology advances allow to create robots equip‐ ped with several kinds of sensors, often providing meas‐ ures related to the same physical quantity but characterized by quite different and complementary characteristics. The phase shift, on the contrary, is very sharp (typically one degree corresponds to displacements of the robot in the order of 1 mm) but presents a cycle ambiguity, since an unknown number of full wavelengths is contained in the tag-reader distance Both the measures are related to the tagreader distance but present different and, to some extent, complementary characteristics, which should be properly combined to obtain an effective estimation of the robot state. Alternative approaches to face the estimation problem addressed in this paper can be developed, resorting to general purpose filtering techniques, e.g., particle filters [12] or multi-hypothesis Gaussian filters (e.g., [13]) These methods may handle the multivariate nature of the measurements considered in this paper.
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