Antonio Petitti

Decentralized parameter estimation and observation for cooperative mobile manipulation of an unknown load using noisy measurements

In this paper, a distributed approach for the estimation of kinematic and inertial parameters of an unknown rigid body is presented. The body is manipulated by a pool of ground mobile manipulators. Each robot retrieves a noisy measurement of its velocity and the contact forces applied to the body. Kinematics and dynamics arguments are used to distributively estimate the relative positions of the contact points. Subsequently, distributed estimation filters and nonlinear observers are used to estimate the body mass, the relative position between its geometric center and its center of mass, and its moment of inertia. The manipulation strategy is functional to the estimation process, and is suitably designed to satisfy nonlinear observability conditions that are necessary for the success of the estimation. Numerical results corroborate our theoretical findings.

Consensus-based distributed estimation for target tracking in heterogeneous sensor networks

In this paper the problem of distributed target tracking is considered. A network of heterogeneous sensing agents is used to observe a maneuvering target and, at each iteration, all the agents are able to agree about the estimate of the target position, despite the fact that only a small percentage of agents can sense the target at each time instant. Our Consensus-based Distributed Target Tracking (CDTT) is a fully distributed iterative tracking algorithm, in which each iteration is based on two phases: an estimation phase and a consensus one. As a result, the estimated trajectories are identical for all the agents at each time instant. Numerical simulations and comparison with another target tracking algorithm are carried out to show the effectiveness and feasibility of our approach.

Decentralized motion control for cooperative manipulation with a team of networked mobile manipulators

In this paper we consider the cooperative control of the manipulation of a load on a plane by a team of mobile robots. We propose two different novel solutions. The first is a controller which ensures exact tracking of the load twist. This controller is partially decentralized since, locally, it does not rely on the state of all the robots but needs only to know the system parameters and load twist. Then we propose a fully decentralized controller that differs from the first one for the use of i) a decentralized estimation of the parameters and twist of the load based only on local measurements of the velocity of the contact points and ii) a discontinuous robustification term in the control law. The second controller ensures a practical stabilization of the twist in presence of estimation errors. The theoretical results are finally corroborated with a simulation campaign evaluating different manipulation settings.

On the convergence of the max-consensus protocol with asynchronous updates

In this paper, we present new theoretical results on the convergence of max-consensus protocols for asynchronous networks. The analysis is carried out exploiting well-established concepts in the field of partially asynchronous iterative algorithms and of analytic synchronization. As a main result, we propose a theoretical setting to prove the convergence of the asynchronous max-consensus protocol. Moreover, we provide an upper bound on the convergence time of the max-consensus protocol in asynchronous networks.

Asynchronous Max-Consensus Protocol With Time Delays: Convergence Results and Applications

This paper deals with the analysis of the convergence properties of the max-consensus protocol in presence of asynchronous updates and bounded time delays on directed static networks. The work is motivated by real-world applications in distributed decision-making systems, for which max-consensus is an effective paradigm. The main result of this paper is that the strongly connectedness of the directed communication network is a sufficient condition for the asynchronous max-consensus protocol to let a distributed system converge in finite time. Implementation issues are also taken into account, by complementing the theoretical analysis with the definition of a mechanism to detect convergence in a distributed fashion. Finally, a numerical example is given, highlighting both the issues related to the failure of synchronous protocols applied to asynchronous settings and the effectiveness of the proposed asynchronous framework.

Distributed Kalman filtering via node selection in heterogeneous sensor networks

In this paper, we propose a strategy for distributed Kalman filtering over sensor networks, based on node selection, rather than on sensor fusion. The presented approach is particularly suitable when sensors with limited sensing capability are considered. In this case, strategies based on sensor fusion may exhibit poor results, as several unreliable measurements may be included in the fusion process. On the other hand, our approach implements a distributed strategy able to select only the node with the most accurate estimate and to propagate it through the whole network in finite time. The algorithm is based on the definition of a metric of the estimate accuracy, and on the application of an agreement protocol based on max-consensus. We prove the convergence, in finite time, of all the local estimates to the most accurate one at each discrete iteration, as well as the equivalence with a centralised Kalman filter with multiple measurements, evolving according to a state-dependent switching dynamics. An application of the algorithm to the problem of distributed target tracking over a network of heterogeneous range-bearing sensors is shown. Simulation results and a comparison with two distributed Kalman filtering strategies based on sensor fusion confirm the suitability of the approach.

Distributed estimation of the inertial parameters of an unknown load via multi-robot manipulation

In this paper, we propose a distributed strategy for the estimation of the kinematic and inertial parameters of an unknown body manipulated by a team of mobile robots. We assume that each robot can measure its own velocity, as well as the contact forces exerted during the body manipulation, but neither the accelerations nor the positions of the contact points are directly accessible. Through kinematics and dynamics arguments, the relative positions of the contact points are estimated in a distributed fashion, and an observability condition is defined. Then, the inertial parameters (i.e., mass, relative position of the center of mass and moment of inertia) are estimated using distributed estimation filters and a nonlinear observer in cooperation with suitable control actions that ensure the observability of the parameters. Finally, we provide numerical simulations that corroborate our theoretical analysis.

Asynchronous consensus-based distributed target tracking

This paper addresses the problem of distributed target tracking, performed by a network of agents which update their local estimates asynchronously. The proposed solution extends and improves an existing consensus-based distributed target tracking framework to cope with real-world settings in which each agent is driven by a different clock. In the consensus-based target tracking framework, it is assumed that only a few agents can actually measure the target state at a given time, whereas the remainder is able to perform a model-based prediction. Subsequently, an algorithm based on max-consensus makes all the agents agree, in finite time, on the best available estimate in the network. The limitations imposed by the assumption of synchronous updates of the network nodes are here overcome by the introduction of the concept of asynchronous iteration. Moreover, an event-based approach makes for the lack of a common time scale at the network level. Furthermore, the synchronous scenario can be derived as a special case of the asynchronous setting. Finally, numerical simulations confirm the validity of the approach.

A Network of Stationary Sensors and Mobile Robots for Distributed Ambient Intelligence

In the last few decades, sensor networks have received significant attention in the field of ambient intelligence (AmI) for surveillance and assisted living applications, as they provide a powerful tool to capture relevant information about environments and human activities. Mobile robots hold promise for enhancing the potential of sensor networks toward the development of intelligent systems that are able not only to detect events but also to actively intervene in the environment accordingly. This article presents the Distributed Ambient Intelligence Architecture that aims at integrating multisensor robotic platforms with wireless sensor networks. Based on the robot operating system, it provides a flexible and scalable software infrastructure extendible to different AmI scenarios. This article describes the proposed architecture and presents experimental tests, showing the feasibility of the system in the context of ambient assisted living.

Distributed target tracking for sensor networks with only local communication

In this paper the problem of distributed target tracking is considered. A network of agents is used to observe a mobile target and, at each iteration, all the agents agree about the estimate of the target position, despite the fact that they only have local interactions and only a small percentage of them can sense the target. The proposed approach, named Consensus-based Distributed Target Tracking (CDTT), is a fully distributed iterative tracking algorithm. At each iteration our method applies two phases. During the perception phase the target position is obtained either as a measure or as a prediction; subsequently, in the consensus phase a consensus algorithm is applied in order to let all the agents agree on the target position. As a result, the estimated trajectories are identical for all the agents. Numerical simulations are carried out to show the effectiveness and feasibility of our approach.