Graph-Based Distributed Control for Adaptive Multi-Robot Patrolling through Local Formation Transformation

Multi-robot cooperative navigation in real-world environments is essential in many applications, including surveillance and search-and-rescue missions. State-of-the-art methods for cooperative navigation are often tested in ideal laboratory conditions and not ready to be deployed in real- world environments, which are often cluttered with static and dynamic obstacles. In this work, we explore a graph-based framework to achieve control of real robot formations moving in a world cluttered with a variety of obstacles by introducing a new distributed algorithm for reconfiguring the formation shape. We systematically validate the reconfiguration algorithm using three real robots in scenarios of increasing complexity

3D multi-robot patrolling with a two-level coordination strategy

Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks

Reactive Particle Swarm Control Architecture and Application for Scalar Field Adaptive Navigation

Adaptive navigation is a subcategory of navigation techniques that attempts to identify goal locations that satisfy specific criteria in an unknown area. In 2D scalar field adaptive navigation (SFAN), primitives navigate to or along features of interest in an unknown, possibly time-varying, planar scalar field. Features include extrema, contours, and fronts. This work solves the 2D SFAN problem using swarm robotic techniques. Robotic swarms are a subset of multi-robot systems that use decentralized control of simple interchangeable robots to perform collective actions. A subgroup of swarms is the Reactive Particle Swarm (RPS), characterized based on its simplicity, reactivity to its current environment, and flexibility of applications. Previous work in RPS lacks a unified implementation for RPS behaviors making cross-comparison and reuse challenging. This work presents a novel 1) RPS control architecture that streamlines the development of novel RPS behaviors, 2) elliptical aggregation algorithm that meets the four tenets of elliptical aggregation, and 3) series of 2D RPS SFAN primitives, and verifies all RPS base and composite behaviors using simulated and hardware-in-the-loop case studies. The architecture unifies the development of new RPS behaviors. The weighted summation of simple base behaviors and external command inputs form complex composite behaviors. This plug-and-play design concept allows for the rapid development of novel combinations of base behaviors, and emphasizes the topdown design of composite behaviors. A series of simulated and on-hardware case studies demonstrate the utility and flexibility of the architecture while establishing a library of verified RPS base behaviors. The four tenets of elliptical aggregation are 1) guidelines for swarm and ellipse parameter selection to ensure successful aggregation, 2) commandable ellipse parameters, 3) simplicity for scaling in the number of robots, and 4) adaptive sizing. The elliptical attraction behavior can be leveraged for SFAN to orient the swarm to improve feature sensing and size to overcome noise thresholds. The elliptical attraction behavior and adaptive sizing variant were verified using simulated and experimental trials. For 2D RPS SFAN primitives, the extremum seeking, contour following, and front identification behaviors and their adaptive sizing variants are verified using simulations incorporating both artificial and interpolated real-world scalar fields and hardware-in-the-loop trials. The ridge descent, trench ascent, and saddle point identification behaviors are presented in a preliminary form and are verified through simulation. Overall this work has four main contributions, 1) a novel RPS control architecture that unifies the implementation and streamlines the development of novel RPS behaviors, 2) a novel elliptical attraction behavior, 3) novel SFAN primitives, and 4) verification of all RPS behaviors through simulation and hardware-in-theloop trials

A Study of Gradient Climbing Technique Using Cluster Space Control of Multi-Robot Systems

The design of the multi-robot system for distributed sensing and gradient climbing focuses on the capability to optimize the performance of tasks simultaneously. The strategy is to utilize the cluster’s redundancy and flexibility to gain and maximize the overall coverage of surveying parameters so as to surpass the performance of any single robot. The collaborative nature of the cluster provides a more efficient and effective platform for collecting data and conducting fieldwork. The purpose of this study is to explore the existing cluster space control technique to show effective gradient-based navigation, particularly that of climbing a gradient in a sensed parameter field to the local maximum. In order to achieve positive results, we need to estimate the gradient direction based on real-time measurements captured by sensors on the distributed robotic network, and then maneuver the cluster to travel in the estimated direction. Verification and characterization of this technique has been performed through both simulation and hardware-in-the-loop experimentation. In these tests, the gradient controller enabled the cluster to sense and climb the gradient in a parameterized field using kayaks in a marine environment and utilizing wheeled robots in a land based system. The successful outcome of these demonstrations proves the value of the cluster space control technique and showcases how it can be used for efficiently locating minimum and maximum features in a parameter field

Effective Cooperation and Scalability in Multi-Robot Teams for Automatic Patrolling of Infrastructures

Tese de doutoramento em Engenharia Electrotécnica e de Computadores, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraIn the digital era that we live in, advances in technology have proliferated throughout our society, quickening the completion of tasks that were painful in the old days, improving solutions to the everyday problems that we face, and generally assisting human beings both in their professional and personal life. Robotics is a clear example of a broad technological field that evolves every day. In fact, scientists predict that in the upcoming few decades, robots will naturally interact and coexist alongside human beings. While it is true that robots already have a strong presence in industrial environments, e.g., robotic arms for manufacturing, the average person still looks upon robots with suspicion, since they are not acquainted by such type of technology. In this thesis, the author deploys teams of mobile robots in indoor scenarios to cooperatively perform patrolling missions, which represents an effort to bring robots closer to humans and assist them in monotonous or repetitive tasks, such as supervising and monitoring indoor infrastructures or simply cooperatively cleaning floors. In this context, the team of robots should be able to sense the environment, localize and navigate autonomously between way points while avoiding obstacles, incorporate any number of robots, communicate actions in a distributed way and being robust not only to agent failures but also communication failures, so as to effectively coordinate to achieve optimal collective performance. The referred capabilities are an evidence that such systems can only prove their reliability in real-world environments if robots are endowed with intelligence and autonomy. Thus, the author follows a line of research where patrolling units have the necessary tools for intelligent decision-making, according to the information of the mission, the environment and teammates' actions, using distributed coordination architectures. An incremental approach is followed. Firstly, the problem is presented and the literature is deeply studied in order to identify potential weaknesses and research opportunities, backing up the objectives and contributions proposed in this thesis. Then, problem fundamentals are described and benchmarking of multi-robot patrolling algorithms in realistic conditions is conducted. In these earlier stages, the role of different parameters of the problem, like environment connectivity, team size and strategy philosophy, will become evident through extensive empirical results and statistical analysis. In addition, scalability is deeply analyzed and tied with inter-robot interference and coordination, imposed by each patrolling strategy. After gaining sensibility to the problem, preliminary models for multi-robot patrol with special focus on real-world application are presented, using a Bayesian inspired formalism. Based on these, distributed strategies that lead to superior team performance are described. Interference between autonomous agents is explicitly dealt with, and the approaches are shown to scale to large teams of robots. Additionally, the robustness to agent and communication failures is demonstrated, as well as the flexibility of the model proposed. In fact, by later generalizing the model with learning agents and maintaining memory of past events, it is then shown that these capabilities can be inherited, while at the same time increasing team performance even further and fostering adaptability. This is verified in simulation experiments and real-world results in a large indoor scenario. Furthermore, since the issue of team scalability is highly in focus in this thesis, a method for estimating the optimal team size in a patrolling mission, according to the environment topology is proposed. Upper bounds for team performance prior to the mission start are provided, supporting the choice of the number of robots to be used so that temporal constraints can be satisfied. All methods developed in this thesis are tested and corroborated by experimental results, showing the usefulness of employing cooperative teams of robots in real-world environments and the potential for similar systems to emerge in our society.FCT - SFRH/BD/64426/200

An Institutional Robotics Approach to the Design of Socially Aware Multi-Robot Behaviors

We propose an institutional robotics approachto the design of socially-aware multi-robot systems, wherecooperation among the robots and their social interactions withhumans are guided using institutions. Inspired by the conceptsstemming from economical sciences, robot institutions serve ascoordination artifacts, which specify behavioral rules that areacceptable or desirable given the situation and which can bereplaced by other rules to enforce new acceptable or desirablebehaviors without changing the robot’s core code. In this paperwe propose a formal methodology for consistent design ofcoordinated multi-robot behaviors intended for use in human-populated environments. We illustrate theoretical concepts withpractical examples. Graph-based formations serve as a basisfor coordinated multi-robot behaviors and concepts from theliterature on human-aware navigation provide social rules thatare enforced by the institutions. Experiments are carried outin a high-fidelity robotic simulator to illustrate the applicationof the theoretical concepts

Safe navigation and human-robot interaction in assistant robotic applications

L'abstract è presente nell'allegato / the abstract is in the attachmen