Attilio Priolo

A distributed algorithm for average consensus on strongly connected weighted digraphs

In this work we propose a distributed algorithm to solve the discrete-time average consensus problem on strongly connected weighted digraphs (SCWDs). The key idea is to couple the computation of the average with the estimation of the left eigenvector associated with the zero eigenvalue of the Laplacian matrix according to the protocol described in Qu et al. (2012). The major contribution is the removal of the requirement of the knowledge of the out-neighborhood of an agent, thus paving the way for a simple implementation based on a pure broadcast-based communication scheme.

Evaluating Network Rigidity in Realistic Systems: Decentralization, Asynchronicity, and Parallelization

In this paper, we consider the problem of evaluating the rigidity of a planar network, while satisfying common objectives of real-world systems: decentralization, asynchronicity, and parallelization. The implications that rigidity has in fundamental multirobot problems, e.g., guaranteed formation stability and relative localizability, motivates this study. We propose the decentralization of the pebble game algorithm of Jacobs et al. , which is an O(n2) method that determines the generic rigidity of a planar network. Our decentralization is based on asynchronous messaging and distributed memory, coupled with auctions for electing leaders to arbitrate rigidity evaluation. Further, we provide a parallelization that takes inspiration from gossip algorithms to yield significantly reduced execution time and messaging. An analysis of the correctness, finite termination, and complexity is given, along with a simulated application in decentralized rigidity control. Finally, we provide Monte Carlo analysis in a Contiki networking environment, illustrating the real-world applicability of our methods, and yielding a bridge between rigidity theory and realistic interacting systems.

Distributed combinatorial rigidity control in multi-agent networks

In this paper, we propose a distributed control law to maintain the combinatorial rigidity of a multi-agent system in the plane, when interaction is proximity-limited. Motivated by the generic properties of rigidity as a function of the underlying network graph, local link addition and deletion rules are proposed that preserve combinatorial rigidity through agent mobility. Specifically, redundancy of network links over local sub-graphs allows the determination of topological transitions that preserve rigidity. It is shown that local redundancy of a network link is sufficient for global redundancy, and thus applying minimal communication, and computation that scales like O(n2), the generic topological rigidity of a network can be preserved.

A swarm aggregation algorithm based on local interaction for multi-robot systems with actuator saturations

We propose a swarm aggregation algorithm based on local interactions in the presence of saturations on the robot actuators. This assumption allows to better model the physical limitations of actual mobile robotic platforms. In our framework, robot-to-robot interactions are limited to the visibility neighborhood, i.e., to robots that are within the range of visibility of each other. A theoretical analysis of the convergence properties is presented for the proposed swarm aggregation algorithm. Extensive simulations have been performed to corroborate the theoretical results. In addition, experiments with a team of low-cost mobile robots have been carried out to show the effectiveness of the proposed approach.

A swarm aggregation algorithm for multi-robot systems based on local interaction

In this work, a swarm aggregation algorithm for multi-robot systems based on local interaction is proposed. In the considered framework, a swarm of robots is moving within an outdoor environment according to the guidance commands issued by a remote control station. Robots, which are responsible for the execution of the guidance commands, are required to show a cohesive behavior through local interactions. A theoretical validation of the swarm interaction modeling is given. Simulations along with experimental results carried out by exploiting a Kinect® -based remote control system are provided to corroborate the theoretical results.

A swarm aggregation algorithm based on local interaction with actuator saturations and integrated obstacle avoidance

In this paper, a novel decentralized swarm aggregation algorithm for multi-robot systems with an integrated obstacle avoidance is proposed. In this framework, the interaction among robots is limited to their visibility neighborhood, i.e., robots that are within the visibility range of each other. Furthermore, to better comply with the hardware/software limitations of mobile robotic platforms, robots actuators are assumed to be saturated. A theoretical characterization of the main properties of the proposed swarm aggregation algorithm is provided. Simulations have been carried out to validate the theoretical results and experiments have been performed with a team of low-cost mobile robots to demonstrate the effectiveness of the proposed approach in real scenario.

Swarm-based path-following for cooperative unmanned surface vehicles

This article proposes a swarm-based path-following guidance system for an autonomous marine multi-vehicle system. In particular, a team of unmanned surface vehicles is required to converge to and navigate along a desired reference path, while at the same time aggregating and maintaining a range-based formation configuration. First, a separate description is given for a swarm methodology, initially developed for small ground mobile robots and exploited to aggregate the robot team, and a virtual target–based path-following guidance system developed for unmanned surface vehicles, exploited to drive not the single vehicles but the robot formation as a whole. Then, the integration of the two proposed methodologies is reported and proven, in order to guarantee the feasibility and stability of the overall guidance framework. Simulative results are proposed to validate the effectiveness of the proposed methodology and to evaluate the system performances.

Decentralized generic rigidity evaluation in interconnected systems

In this paper, we consider the problem of evaluating the generic rigidity of an interconnected system in the plane, without a priori knowledge of the networks topological properties. We propose the decentralization of the pebble game algorithm of Jacobs et. al., an O(n2) method that determines the generic rigidity of a planar network. Our decentralization is based on asynchronous inter-agent message-passing and a distributed memory architecture, coupled with consensus-based auctions for electing leaders in the system. We provide analysis of the asynchronous messaging structure and its interaction with leader election, and Monte Carlo simulations demonstrating complexity and correctness. Finally, a novel rigidity evaluation and control scenario in the accompanying media illustrates the applicability of our proposed algorithm.

A RSSI-based technique for inter-distance computation in Multi-Robot Systems

Multi-Robot Systems, i.e., collections of robots which cooperate to achieve a common goal, have become more and more appealing over the years to the robotics community. Research interests come from the several advantages that a multi-robot approach offers over traditional single complex robotic system approaches, such as a larger range of task domains or a higher robustness and flexibility. A primary concern is how to effectively achieve a cooperative behavior in multi-robot systems. Indeed, formation control turned out to be an important technique to achieve this goal. However, the majority of the approaches available in the literature assumes robots to be able to measure inter-distances among them. For this reason, in this paper we focus our attention on providing a reliable technique to compute inter-distances among robots. Experimental results, carried out by exploiting Xbee-Pro radio transceiver, are given to show the effectiveness of the proposed technique.

A fitness-sharing based genetic algorithm for collaborative multi-robot localization

In this paper, a novel genetic algorithm based on a “collaborative” fitness-sharing technique to deal with the multi-robot localization problem is proposed. Indeed, the use of the fitness-sharing is twofold and competitive. It preserves the diversity among individuals during the space exploration process, thus maintaining evolutionary niches over time, and reinforces the best hypotheses by means of collaboration among robots, thus augmenting the selection pressure. Simulations by exploiting the robotics framework Player/Stage have been performed along with a proper statistical analysis for performance assessment.