Neste trabalho foi desenvolvido um estudo comparativo entre duas estratégias básicas para a combinação de mapas parciais baseados em marcos para sistemas multirobô: a estratégia por associação de marcos e a estratégia por distância relativa entre os robôs (também conhecida por rendez-vous). O ambiente simulado corresponde a um entorno plano povoado de árvores que são mapeadas por uma equipe de dois robôs móveis equipados com sensores laser para medir a largura e localização de cada _arvore (marco). Os mapas parciais são estimados usando o algoritmo FastSLAM. Além do estudo comparativo propõe-se também um algoritmo alternativo de localização e mapeamento simultâneos para multirrobôs cooperativos, utilizando as observações entre os robôs não só para o cálculo da transformação de coordenadas, mas também no desenvolvimento de um processo seqüencial para atualizar o alinhamento entre os mapas, explorando de forma mais eficiente as observações entre robôs. Os experimentos realizados demonstraram que o algoritmo proposto pode conduzir a resultados significativamente melhores em termos de precisão quando comparado com a abordagem de combinação de mapas tradicional (usando distância relativa entre os robôs). / In this text a comparative survey between the two basic strategies used to combine partial landmark based maps in multi-robot systems, data association and inter-robot observations (known as rendezvous), is presented. The simulated environment is a at place populated by trees, which are going to be mapped by a two-mobile robot team equipped with laser range finders in order to measure every tree (landmark) location and width. Partial maps are estimated using the algorithm FastSLAM. Besides the comparative study it is also proposed an alternative algorithm for Simultaneous Localization and Mapping (SLAM) in multi-robot cooperative systems. It uses observations between robots (detections) not only for calculating the coordinate transformation but also in the development of a sequential process for updating the alignment between maps, exploiting in a more efficient way the inter-robot observations. The experiments showed that the algorithm can lead to significantly better results in terms of accuracy when compared with the traditional approach of combining maps (using the relative distance between robots).

Imagine the following situation. You give your favorite robot, named Pippi, the task to fetch a heavy parcel that just arrived at your front door. While pushing the parcel back to you, she must travel through a door. Unfortunately, the parcel she is pushing is blocking her camera, giving her a hard time to see the door. If she cannot see the door, she cannot safely push the parcel through it. What would you as a human do in a similar situation? Most probably you would ask someone for help, someone to guide you through the door, as we ask for help when we need to park our car in a tight parking spot. Why not let the robots do the same? Why not let robots help each other? Luckily for Pippi, there is another robot, named Emil, vacuum cleaning the floor in the same room. Since Emil has a video camera and can view both Pippi and the door at the same time, he can estimate Pippi's position relative to the door and use this information to guide Pippi through the door by wireless communication. In that way he can enable Pippi to deliver the parcel to you. The goal of this thesis is to endow robots with the ability to help each other in a similar way. More specifically, we consider distributed robot systems in which: (1) each robot includes modular functionalities for sensing, acting and/or processing; and (2) robots can help each other by offering those functionalities. A functional configuration of such a system is any way to allocate and connect functionalities configuration among the robots. An interesting feature of a system of this type is the possibility to use different functional configurations to make the same set of robots perform different tasks, or to perform the same task under different conditions. In the above example, Emil is offering a perceptual functionality to Pippi. In a different situation, Emil could offer his motion functionality to help Pippi push a heavier parcel. In this thesis, we propose an approach to automatically generate, at run time, a functional configuration of a distributed robot system to perform a given task in a given environment, and to dynamically change this configuration in response to failures. Our approach is based on artificial intelligence planning techniques, and it is provably sound, complete and optimal. In order to handle tasks that require more than one step (i.e., one configuration) to be accomplished, we also show how methods for automatic configuration can be integrated with methods for task planning to produce a complete plan were each step is a configuration. For the scenario above, generating a complete plan before the execution starts enables Pippi to know before hand if she will be able to get the parcel or not. We also propose an approach to merge configurations, which enables concurrent execution of configurations, thus reducing execution time. We demonstrate the applicability of our approach on a specific type of distributed robot system, called Peis-Ecology, and show experiments in which configurations and sequences of configurations are automatically generated and executed on real robots. Further, we give an experiment where merged configurations are created and executed on simulated robots.

abstract: One potential application of multi-robot systems is collective transport, a task in which multiple mobile robots collaboratively transport a payload that is too large or heavy to be carried by a single robot. Numerous control schemes have been proposed for collective transport in environments where robots can localize themselves (e.g., using GPS) and communicate with one another, have information about the payload's geometric and dynamical properties, and follow predefined robot and/or payload trajectories. However, these approaches cannot be applied in uncertain environments where robots do not have reliable communication and GPS and lack information about the payload. These conditions characterize a variety of applications, including construction, mining, assembly in space and underwater, search-and-rescue, and disaster response. Toward this end, this thesis presents decentralized control strategies for collective transport by robots that regulate their actions using only their local sensor measurements and minimal prior information. These strategies can be implemented on robots that have limited or absent localization capabilities, do not explicitly exchange information, and are not assigned predefined trajectories. The controllers are developed for collective transport over planar surfaces, but can be extended to three-dimensional environments. This thesis addresses the above problem for two control objectives. First, decentralized controllers are proposed for velocity control of collective transport, in which the robots must transport a payload at a constant velocity through an unbounded domain that may contain strictly convex obstacles. The robots are provided only with the target transport velocity, and they do not have global localization or prior information about any obstacles in the environment. Second, decentralized controllers are proposed for position control of collective transport, in which the robots must transport a payload to a target position through a bounded or unbounded domain that may contain convex obstacles. The robots are subject to the same constraints as in the velocity control scenario, except that they are assumed to have global localization. Theoretical guarantees for successful execution of the task are derived using techniques from nonlinear control theory, and it is shown through simulations and physical robot experiments that the transport objectives are achieved with the proposed controllers. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2020

abstract: The need for incorporating game engines into robotics tools becomes increasingly crucial as their graphics continue to become more photorealistic. This thesis presents a simulation framework, referred to as OpenUAV, that addresses cloud simulation and photorealism challenges in academic and research goals. In this work, OpenUAV is used to create a simulation of an autonomous underwater vehicle (AUV) closely following a moving autonomous surface vehicle (ASV) in an underwater coral reef environment. It incorporates the Unity3D game engine and the robotics software Gazebo to take advantage of Unity3D's perception and Gazebo's physics simulation. The software is developed as a containerized solution that is deployable on cloud and on-premise systems. This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined. / Dissertation/Thesis / Masters Thesis Computer Science 2020

In various industries, such as manufacturing, logistics, agriculture, defense, search and rescue, and transportation, Multi-robot systems (MRSs) are increasingly gaining popularity. These systems involve multiple robots working together towards a shared objective, either autonomously or under human supervision. However, as MRSs operate in uncertain or even adversarial environments, and the sensors and actuators of each robot may be error-prone, they are susceptible to faults and security threats unique to MRSs. Classical techniques from distributed systems cannot detect or mitigate these threats. In this dissertation, novel techniques are proposed to enhance the security and fault-tolerance of MRSs through inter-robot observations and accusations. A fundamental security property is proposed for MRSs, which ensures that forbidden deviations from a desired multi-robot motion plan by the system supervisor are detected. Relying solely on self-reported motion information from the robots for monitoring deviations can leave the system vulnerable to attacks from a single compromised robot. The concept of co-observations is introduced, which are additional data reported to the supervisor to supplement the self-reported motion information. Co-observation-based detection is formalized as a method of identifying deviations from the expected motion plan based on discrepancies in the sequence of co-observations reported. An optimal deviation-detecting motion planning problem is formulated that achieves all the original application objectives while ensuring that all forbidden plan-deviation attacks trigger co-observation-based detection by the supervisor. A secure motion planner based on constraint solving is proposed as a proof-of-concept to implement the deviation-detecting security property. The security and resilience of MRSs against plan deviation attacks are further improved by limiting the information available to attackers. An efficient algorithm is proposed that verifies the inability of an attacker to stealthily perform forbidden plan deviation attacks with a given motion plan and announcement scheme. Such announcement schemes are referred to as horizon-limiting. An optimal horizon-limiting planning problem is formulated that maximizes planning lookahead while maintaining the announcement scheme as horizon-limiting. Co-observations and horizon-limiting announcements are shown to be efficient and scalable in protecting MRSs, including systems with hundreds of robots, as evidenced by a case study in a warehouse setting. Lastly, the Decentralized Blocklist Protocol (DBP), a method for designing Byzantine-resilient decentralized MRSs, is introduced. DBP is based on inter-robot accusations and allows cooperative robots to identify misbehavior through co-observations and share this information through the network. The method is adaptive to the number of faulty robots and is widely applicable to various decentralized MRS applications. It also permits fast information propagation, requires fewer cooperative observers of application-specific variables, and reduces the worst-case connectivity requirement, making it more scalable than existing methods. Empirical results demonstrate the scalability and effectiveness of DBP in cooperative target tracking, time synchronization, and localization case studies with hundreds of robots. The techniques proposed in this dissertation enhance the security and fault-tolerance of MRSs operating in uncertain and adversarial environments, aiding in the development of secure MRSs for emerging applications.

Robots are increasingly utilized in industry for their capability to perform repetitive,complex tasks in environments unsuitable for humans. This surge in robotic applicationshas spurred research into Multi-Robot Systems (MRS), which aim to tackle complex tasksrequiring collaboration among multiple robots, thereby boosting overall efficiency. However,MRS introduces multifaceted challenges that span various domains, including robot perception,localization, task assignment, communication, and control. This dissertation delves into theintricate aspects of task assignment and path planning within MRS.The first area of focus is on multi-robot navigation, specifically addressing the limitationsinherent in current Multi-Agent Path Finding (MAPF) models. Traditional MAPF solutionstend to oversimplify, treating robots as holonomic units on grid maps. While this approachis impractical in real-world settings where robots have distinct geometries and kinematicrestrictions, it is important to note that even in its simplified form, MAPF is categorized as anNP-hard problem. The complexity inherent in MAPF becomes even more pronounced whenextending these models to non-holonomic robots, underscoring the significant computationalchallenges involved. To address these challenges, this thesis introduces a novel MAPF solverdesigned for non-holonomic, heterogeneous robots. This solver integrates the hybrid A*algorithm, accommodating kinematic constraints, with a conflict-based search (CBS) for efficientconflict resolution. A depth-first search approach in the conflict tree is utilized to accelerate theidentification of viable solutions.The second research direction explores synergizing task assignment with path-finding inMRS. While there is substantial research in both decentralized and centralized task assignmentstrategies, integrating these with path-finding remains underexplored. This dissertation evaluatesdecoupled methods for sequentially resolving task assignment and MAPF challenges. Oneproposed method combines the Hungarian algorithm and a Traveling Salesman Problem (TSP)solver for swift, albeit suboptimal, task allocation. Subsequently, robot paths are generatedindependently, under the assumption of collision-free navigation. During actual navigation, aNonlinear Model Predictive Controller (NMPC) is deployed for dynamic collision avoidance. Analternative approach seeks optimal solutions by conceptualizing task assignment as a MultipleTraveling Salesman Problem (MTSP), solved using a simulated annealing algorithm. In tandem,CBS is iteratively applied to minimize the cumulative path costs of the robots.

Multi-agent systems (MAS) have been of interest to many researchers during the last decades. This thesis focuses on multi-robot systems (MRS) and programmable matter as two types of MAS. Regarding MRS, the focus is on the 'mergeable nervous system' (MNS) concept which allows the robots to connect to one another and establish a communication network through self-organization and then use the network to temporarily report sensing events and cede authority to a single robot in the system. Here, in a collective perception scenario, we experimentally evaluate the performance of an MNS-enabled approach and compare it with that of several decentralized benchmark approaches. We show that an MNS-enabled approach is high-performing, fault-tolerant, and scalable, so it is an appropriate approach for MRS. As a goal of the thesis, using an MNS-enabled approach, we present for the first time a comprehensive comparison of control architectures in multi-robot systems, which includes a comparison of accuracy, efficiency, speed, energy consumption, scalability, and fault tolerance. Our comparisons provide designers of multi-robot systems with a better understanding for selecting the best-performing control depending on the system's objectives. Additionally, as a separate goal, we design a high-level leader based programmable matter, which can perform some basic primitive operations in a grid environment, and construct it using lower-level organisms. We design and implement deterministic algorithms for "curl" operation of this high-level matter, an instance of shape formation problem. We prove the correctness of the presented algorithms, analytically determine their complexity, and experimentally evaluate their performance.

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Decision-making is an essential topic for multi-robot coordination and collaboration and is also the main topic of this thesis. Examples can be found in autonomous driving, environmental monitoring, intelligent transportation, etc. To study this problem, we first use multiple applications as motivating examples and then construct the general formulation and solution for those applications. Finally, we extend our investigation from the fundamental problem formulation to resilient and decentralized versions. All those problems are studied in the combinatorial optimization domain with the help of submodular and matroid optimization techniques. As a motivating example, we use a multi-robot environmental monitoring problem to extract the general formulation of a multi-robot decision-making problem. Consider the problem of deploying multi-agent teams for environmental monitoring in a precision farming application. We want to answer the question of when and where to deploy our robots. This is a typical task allocation problem in multi-robot systems. Using the above problem as an example, we first focus on this decision-making problem, e.g., intermittent deployment problem, in a centralized scenario. Given a predictable agriculture environment, we want to make decisions for robots for this monitoring task. The problem is formulated as a combinatorial submodular optimization with matroid constraints. By utilizing the properties of submodularity, we aim to develop a solution with performance guarantees. This motivating example demonstrates how to use a submodular function and matroids to model and solve decision-making problems in multi-robot systems. Based on this framework, we continue to explore the fundamental decision-making problem in several other directions in multi-robot systems, including the robust decision-making problem. All those problems and solutions are formulated and considered in a centralized scenario. In the second part of this thesis, we switch our focus from centralized to decentralized scenarios. We first investigate a case where the robots in a distributed multi-robot system need to work together to guard the system against worst-case attacks while making decisions. By worst-case attacks, we refer to the case where the system may have up to $K$ sensor failures. To increase resilience, we propose a fully distributed algorithm to guide each robot's action selection when the system is attacked. The proposed algorithm guarantees performance in a worst-case scenario where up to a portion of the robots malfunction due to attacks. Based on this specific task allocation problem in robotics, we then create a unified framework for a more general case in a decentralized scenario, e.g., asynchronous decentralized decision-making problems with matroid and knapsack constraints. Finally, several applications in decentralized scenarios are used to validate the theoretical guaranteed performance in robotics. / Doctor of Philosophy / Robots have been widely used as mobile sensing agents nowadays in various applications. Especially with the help of multi-robot systems and artificial intelligence, our lives have changed dramatically in the last decades. One of the most fundamental questions is how to utilize multi-robot systems to finish tasks successfully. To answer this, we need first to formulate the problem from applications and then find theoretically guaranteed answers to those questions. Meanwhile, the robustness and resilience of the solution also need to be taken care of, as cyber-attacks or system failures can happen everywhere. Motivated by those two main goals, this thesis will first use multiple applications to introduce the thesis's topic. We then provide solutions to those problems in centralized and decentralized scenarios. Meanwhile, to increase the system's ability to handle failures, we need to answer how to improve the robustness and resilience of the proposed solutions. Therefore, the topic of this thesis spread from problem formulation to failure-proof solutions. The result of this thesis can be widely used in multi-robot decision-making applications, including autonomous driving, intelligent transportation, and other cyber-physical systems.

Multi-robot systems (MRSs) have gained significant momentum as of late in the robotics community as they find application in tasks such as unknown environment exploration, distributed surveillance, and search and rescue. Operating robot teams in real world environments introduces a notion of uncertainty into the system, especially when it comes to the ability of the MRS to reliably communicate. This poses a significant challenge as a stable communication topology is the backbone of the team's ability to coordinate. Additionally, as these systems continue to evolve and integrate further into our society, a growing threat of adversarial attackers pose the risk of compromising nominal operation. As such, this dissertation aims to model the effects of uncertainty in communication on the topology of the MRS using a probabilistic interaction model. More specifically we are interested in studying a probabilistic perspective to those topologies that pertain to the security and resilience of an MRS against adversarial attacks. Having a model that is capable of capturing how probabilistic topologies may evolve over time is essential for secure and resilient planning under communication uncertainty. As a result, we develop probabilistic models, both exact and approximate, for the topological properties of system left-invertibility and (r, s)-robustness that respectively characterize the security and resilience of an MRS. In our modeling, we use binary decision diagrams, convolutional neural networks, matroid theory and more to tackle the problems related to probabilistic security and resilience where we find exact solutions, calculate bounds, solve optimization problems, and compute informative paths for exploration. / Doctor of Philosophy / When robots coordinate and interact together to achieve a collaborative task as a team, we obtain what is known as a multi-robot system or MRS for short. MRSs have several advantages over single robots. These include reliability through redundancy, where several robots can perform a given task in case one of the robots unexpectedly fails. The ability to work faster and more efficiently by working in parallel and at different locations. And taking on more complex tasks that can be too demanding for a single robot to complete. Unfortunately, the advantages of MRSs come at a cost, they are generally harder to coordinate, the action of one robot often depends on the action of other robots in the system, and they are more vulnerable to being attacked or exploited by malicious attackers who want to disrupt nominal operation. As one would expect, communication plays a very important roles in coordinating a team of robots. Unfortunately, robots operating in real world environments are subject to disturbances such as noise, obstacles, and interference that hinders the team's ability to effectively exchange information. In addition to being crucial in coordination, effective information exchange plays a major role in detecting and avoiding adversarial robots. Whenever misinformation is being spread in the team, the best way to counter such adversarial behavior is to communicate with as much well-behaving robots as possible to identity and isolate inconsistencies. In this dissertation we try to study how uncertainty in communication affects a system's ability to detect adversarial behavior, and how we can model such a phenomenon to help us account for these uncertainties when designing secure and resilient multi-robot systems.