Coordination Strategies for Connected Robotic Networks

This dissertation is organized in two main parts. In Part I we study networks of homogeneous robots consisting of agents having the same dynamics, sensing, and communication capabilities. We first present a centralized approach based on disjunctive programming to tether a chain of mobile routers between a base station and a user that is moving in a concave environment. We consider line-of-sight (LOS) communication and use a mixed integer approach to determine the number of required agents and their goal locations.;Because centralized approaches have the limitation that a central node has to compute all the operations and everything has to be known a priori, we study decentralized approaches to swarm groups of mobile agents while considering communication constraints. Specifically we introduce a spring-mass virtual interaction between the agents and use artificial potentials to navigate the network in a cluttered environment.;When dealing with connected networks of mobile robots, the problem of detecting variations in the topology is an important area of study. Therefore we present a novel decentralized technique to detect and estimate changes in the topology of mobile robotic networks. In this case we assume that each robot is equipped with a chaotic oscillator whose state is propagated to the other robots through wireless communication. The key idea of our approach is that every node receives an aggregate signal from the surrounding neighbors, which can be used to detect changes in the local network topology. We introduce an adaptive strategy that each robot independently implements to: (i) estimate the net coupling of all the oscillators in its neighborhood, and (ii) synchronize the state of the oscillators onto the same time evolution. We show that by using this strategy, synchronization can be attained and changes of the network topology can be detected. Finally in the last chapter of Part I we exploit antenna diversity in a practical case study to localize and improve the signal quality between a mobile agent and a user.;In Part II we focus our attention on heterogeneous systems. With the term heterogeneous we imply the synergy of multiple robotic platforms characterized by different dynamics and specialized sensing and communication capabilities. An approach that makes use of a heterogeneous team of agents has several advantages when cost, integration of capabilities, or possible large search areas need to be considered. A heterogeneous team allows for the robots to become "specialized" in their abilities and therefore accomplish sub-goals more efficiently which in turn makes the overall mission more efficient. In certain situations the use of homogeneous systems can be unfeasible or have limited performance. For instance, aerial vehicles have the capability to cover an area faster, while flying over obstacles, but cannot have a detailed view of mines, caves or building where line-of-sight is lost. On the other hand, ground vehicles, like wheeled or multi-legged robotic platforms, can explore a limited area but with much more accuracy. Here we introduce a hierarchical approach to coordinate aerial and ground agents as intelligent transportation systems. We use optimization theory and potential functions to guide the agents toward specific areas of a partially known environment while maintaining a robust connectivity among the network and exploiting the different mobilities of the agents.;Finally we conclude this dissertation with a case study for heterogeneous systems characterized by different kinematics, sensing, and manipulation properties. We consider connectivity constraints and realistic communication, exploiting mobility to implement a power control algorithm that increases the Signal to Interference plus Noise Ratio (SINR) among certain members of the network. We also create realistic sensing fields and manipulation by using the geometric properties of the sensor field-of-view and the manipulability metric, respectively. The control strategy for each agent of the heterogeneous system is governed by an artificial physics law that considers the different kinematics of the agents and the environment, in a decentralized fashion. We show that the network is able to stay connected all the time and covers the environment well. (Abstract shortened by UMI.).