Daniele Fontanelli

Shortest Paths for a Robot With Nonholonomic and Field-of-View Constraints

This paper presents a complete characterization of shortest paths to a goal position for a robot with unicycle kinematics and an on-board camera with limited field-of-view (FOV), which must keep a given feature in sight. Previous work on this subject has shown that the search for a shortest path can be limited to simple families of trajectories. In this paper, we provide a complete optimal synthesis for the problem, i.e., a language of optimal control words, and a global partition of the motion plane induced by shortest paths, such that a word in the optimal language is univocally associated with a region and completely describes the shortest path from any starting point in that region to the goal point. An efficient algorithm to determine the region in which the robot is at any time is also provided.

Flexible Indoor Localization and Tracking Based on a Wearable Platform and Sensor Data Fusion

Indoor localization and tracking of moving human targets is a task of recognized importance and difficulty. In this paper, we describe a position measurement technique based on the fusion of various sensor data collected using a wearable embedded platform. Since the accumulated measurement uncertainty affecting inertial data (especially due to the on-board accelerometer) usually makes the measured position values drift away quickly, a heuristic approach is used to keep velocity estimation uncertainty in the order of a few percent. As a result, unlike other solutions proposed in the literature, localization accuracy is good when the wearable platform is worn at the waist. Unbounded uncertainty growth is prevented by injecting the position values collected at a very low rate from the nodes of an external fixed infrastructure (e.g., based on cameras) into an extended Kalman filter. If the adjustment rate is in the order of several seconds and if such corrections are performed only when the user is detected to be in movement, the infrastructure remains idle most of time with evident benefits in terms of scalability. In fact, multiple platforms could work simultaneously in the same environment without saturating the communication channels.

A Frequency-Domain Algorithm for Dynamic Synchrophasor and Frequency Estimation

Next-generation phasor measurement units (PMUs) are expected to play a key role for monitoring the behavior of future smart grids. While most of the PMUs used nowadays in transmission networks rely on static phasor models, more sophisticated representations and stricter accuracy requirements are needed to track amplitude, phase, and frequency changes of power waveforms in strongly dynamic scenarios as those expected in future distribution systems. In this paper, a discrete Fourier transform (DFT)-based algorithm based on a dynamic phasor model (referred to as interpolated dynamic DFT-based synchrophasor estimator) is used to estimate not only amplitude and phase of the collected waveforms, but also their frequency and rate of change of frequency. The performances of the proposed method are evaluated through multiple simulations in different steady-state and transient conditions described in the Standard IEEE C37.118.1-2011.

A Hybrid-Control Approach to the Parking Problem of a Wheeled Vehicle Using Limited View-Angle Visual Feedback

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.

A Data Fusion Technique for Wireless Ranging Performance Improvement

The increasing diffusion of mobile and portable devices provided with wireless connectivity makes the problem of distance measurement based on radio-frequency technologies increasingly important for the development of next-generation nomadic applications. In this paper, the performance limitations of two classic wireless ranging techniques based on received signal strength (RSS) and two-way time-of-flight (ToF) measurements, respectively, are analyzed and compared in detail. On the basis of this study, a data fusion algorithm is proposed to combine both techniques in order to improve ranging accuracy. The algorithm has been implemented and tested on the field using a dedicated embedded prototype made with commercial off-the-shelf components. Several experimental results prove that the combination of both techniques can significantly reduce measurement uncertainty. The results obtained with the developed prototype are not accurate enough for fine-grained position tracking in Ambient Assisted Living applications. However, the platform can be successfully used for reliable indoor zoning, e.g., for omnidirectional and adjustable hazard proximity detection. Most importantly, the proposed solution is absolutely general, and it is quite simple and light from the computational point of view. Accuracy could be further improved by using a more isotropic antenna and by integrating the ToF measurement technique at the lowest possible level on the same radio chip used for communication. Usually, this feature is not available in typical low-cost short-range wireless modules, e.g., for wireless sensor networks. Thus, the results of this research suggest that combining RSS with ToF measurements could be a viable solution for chip manufacturers interested in adding ranging capabilities to their radio modules.

An Analytical Bound for Probabilistic Deadlines

The application of a resource reservation scheduler to soft real -- time systems requires effective means to compute the probability of a deadline miss given a particular choice for the scheduling parameters. This is a challenging research problem, for which only numeric solutions, complex and difficult to manage, are currently available. In this paper, we adopt an analytical approach. By using an approximate and conservative model for the evolution of a periodic task scheduled through a reservation, we construct a closed form lower bound for the probability of a deadline miss. Our experiments reveal that the bound remains reasonably close to the experimental probability for many real -- time applications of interest.

Design of Embedded Controllers Based on Anytime Computing

In this paper, we present a methodology for designing embedded controllers based on the so-called anytime control paradigm. A control law is split into a sequence of subroutine calls, each one fulfilling a control goal and refining the result produced by the previous one. We propose a design methodology to define a feedback controller structured in accordance with this paradigm and show how a switching policy of selecting the controller subroutines can be designed that provides stability guarantees for the closed-loop system. The cornerstone of this construction is a stochastic model describing the probability of executing, in each activation of the controller, the different subroutines. We show how this model can be constructed for realistic real-time task sets and provide an experimental validation of the approach.

Motion planning in crowds using statistical model checking to enhance the social force model

Crowded environments pose a challenge to the comfort and safety of those with impaired ability. To address this challenge we have developed an efficient algorithm that may be embedded in a portable device. The algorithm anticipates undesirable circumstances in real time, by verifying simulation traces of local crowd dynamics against temporal logical formulae. The model incorporates the objectives of the user, pre-existing knowledge of the environment and real time sensor data. The algorithm is thus able to suggest a course of action to achieve the users changing goals, while minimising the probability of problems for the user and others in the environment. To demonstrate our algorithm we have implemented it in an autonomous computing device that we show is able to negotiate complex virtual environments. The performance of our implementation demonstrates that our technology can be successfully applied in a portable device or robot.

Accurate time synchronization in PTP-based industrial networks with long linear paths

Assuring very accurate time synchronization across wide area industrial networks is still an open issue, which even the second version of the Precision Time Protocol (PTPv2) has not been able to solve completely. This is due to the accumulation of many uncertainty contributions when PTP event messages are routed from the master clock to the slave one through multiple network nodes. Peer-to-peer transparent clocks may mitigate this problem. Nonetheless, poor or noisy relative clock rate estimates may drastically reduce the synchronization accuracy on the farthest nodes. In this paper Kalman filters are used to estimate and to compensate, drift rate differences, frequency skews and time offsets between pairs of adjacent transparent clocks. Although the idea of using a Kalman filter for synchronization purposes is not new per se, the proposed solution is specifically tailored to optimize the performance of networks with a long linear topology. Several simulation results confirm the validity of this approach.

Visual Servoing in the Large

In this paper we consider the problem of maneuvering an autonomous robot in complex unknown environments using vision. The goal is to accurately servo a wheeled vehicle to a desired posture using only feedback from an on-board camera, taking into account the nonholonomic nature of the vehicle kinematics and the limited field-of-view of the camera. With respect to existing visual servoing schemes, which achieve similar goals locally (i.e. when the desired and actual camera views are sufficiently similar), we propose a method to visually navigate the robot through an extended visual map before eventually reaching the desired goal. The map comprises a set of images, previously stored in an exploratory phase, that convey both topological and metric information regarding the connectivity through feasible robot paths and the geometry of the environment, respectively. Experimental results on a laboratory setup are reported showing the practicality of the proposed approach.