In this thesis, we are addressing new method of object transportation using multi-robot system. The new method of object transportation is called A grid-based cyclic robot allocation (GCRA) method which consists multiple spherical robots. The object is placed on top of group of spherical robots before the transportation. The rotation of the multiple spherical robots cause the displacement of the object and reach the goal location based on the direction and speed of the rotation of the robots. The GCRA method for spherical robots is proposed along with specific stability criterion, which designs the formation of the multi-robot system. The formation is created based on the customized grid which is to be modified based on the properties of the object. The shape and the center of gravity of the shape define the horizontal gap, $g_x$ and vertical gap, $g_y$. All the possible locations of spherical robots is the cross points of grid which implies that $g_x$ and $g_y$ defines the distance between the robots and based on the boundary of the robots placed underneath the object, the condition of the stability is defined. It also identifies minimum number of robots required based on the arbitrary shape of an object for stable omni-directional translation of the object on a 2 dimensional space. The desired positions and formation of the robots is identified based goal position of the object. Under centralized system, position control is applied to drive the robots to the desired positions. The position control simultaneously makes the object mobile and maintain the stability of the object. Mathematical proof of the proposed method is shown verifying the stability of the transportation process with the assumptions of no slip between the robots and the object. 2 Dimensional Simulation results of robot allocation using GCRA for several arbitrary shapes certify the proposed method.

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Méthodologie de Conception de Système Multi-robots : de la Simulation à la Démonstration. Les systèmes multi-robots sont des systèmes complexes mais prometteurs dans de nombreux domaines, les nombreux travaux académiques dans ce domaine attestent de l'importance qu'ils auront dans le futur. Cependant, si ces promesses sont réelles, elles ne sont pas encore réalisées comme en témoigne le faible nombre de systèmes multi-robots utilisés dans l'industrie. Pourtant des solutions existent afin de permettre aux industriels et académiques de travailler ensemble à cette problématique. Nous proposons un état de l'art et les défis associés à la conception des systèmes multi-robots d'un point de vue académique et industriel. Nous présentons ensuite trois contributions pour la conception de ces systèmes : une réalisation d'un essaim hétérogène en tant que cas d'étude pratique afin de mettre en évidence les obstacles de conception. La modification d'un autopilote et d'un simulateur pour les rendre compatibles aux développements des systèmes multi-robots. La démonstration d'un outil d'évaluation sur la base des deux contributions précédentes. Enfin, nous concluons sur la portée de ces travaux et des perspectives à venir sur la base de l'open source / Multi-robot System Design Methodology : from Simulation to Demonstration Multi-robot systems are complex but promising systems in many fields, the number of academic works in this field underlines the importance they will have in the future. However, while these promises are real, they have not yet been realized, as evidenced by the small number of multi-robot systems used in the industry. However, solutions exist to enable industrialists and academics to work together on this issue. We propose a state of the art and challenges associated with the design of multi-robot systems from an academic and industrial point of view. We then present three contributions for the design of these systems: a realization of a heterogeneous swarm as a practical case study in order to highlight the design obstacles. The modification of an autopilot and a simulator to make them compatible with the development of multi-robot systems. Demonstration of an evaluation tool based on the two previous contributions. Finally, we conclude on the scope of this work and future perspectives based on open source.

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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.

Coordination_oru är ett programramverk för forskning som skapades vid Örebrouniversitet i Sverige. Det är ett testramverk för en specifik algoritm för koordination avrobotar som utvecklas vidare till en simulationsplatform som möjliggör systematisktexperiment. I det här examensarbetet skapas en experimentspecifikation med alladelar som behövs för att fullt konfigurera systematiska experiment förCoordination_oru-ramverket. Experimentspecifikation utvecklas för att anpassa ettgrafiskt användargränssnitt som bidrar till att göra det enklare för användare attkontrollera, ändra och hantera systemet. Dessutom kan användare skapa och köraexperiment med möjlighet att justera karta, väg, robotens hastighet, acceleration,storlek/form, färg och destination samt att se resultatet. Användargränssnittet harutvecklats med JSON-formatet för att hantera konfiguration avexperimentspecifikation. Dessutom används CSV-formatet för att lagra resultatdata itabellform under projektet

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.

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In this dissertation, we study multi-robot coordination in the context of multi-target tracking. Specifically, we are interested in the coordination achieved by means of submodular function optimization. Submodularity encodes the diminishing returns property that arises in multi-robot coordination. For example, the marginal gain of assigning an additional robot to track the same target diminishes as the number of robots assigned increases. The advantage of formulating coordination problems as submodular optimization is that a simple, greedy algorithm is guaranteed to give a good performance. However, often this comes at the expense of unrealistic models and assumptions. For example, the standard formulation does not take into account the fact that robots may fail, either randomly or due to adversarial attacks. When operating in uncertain conditions, we typically seek to optimize the expected performance. However, this does not give any flexibility for a user to seek conservative or aggressive behaviors from the team of robots. Furthermore, most coordination algorithms force robots to communicate at each time step, even though they may not need to. Our goal in this dissertation is to overcome these limitations by devising coordination algorithms that are parsimonious in communication, allow a user to manage the risk of the robot performance, and are resilient to worst-case robot failures and attacks. In the first part of this dissertation, we focus on designing parsimonious communication strategies for target tracking. Specifically, we investigate the problem of determining when to communicate and who to communicate with. When the robots use range sensors, the tracking performance is a function of the relative positions of the robots and the targets. We propose a self-triggered communication strategy in which a robot communicates its own position with its neighbors only when a certain set of conditions are violated. We prove that this strategy converges to the optimal robot positions for tracking a single target and in practice, reduces the number of communication messages by 30%. When tracking multiple targets, we can reduce the communication by forming subsets of robots and assigning one subset to track a target. We investigate a number of measures for tracking quality based on the observability matrix and show which ones are submodular and which ones are not. For non-submodular measures, we show a greedy algorithm gives a 1/(n+1) approximation, if we restrict the subset to n robots. In optimizing submodular functions, a common assumption is that the function value is deterministic, which may not hold in practice. For example, the sensor performance may depend on environmental conditions which are not known exactly. In the second part of the dissertation, we design an algorithm for stochastic submodular optimization. The standard formulation for stochastic optimization optimizes the expected performance. However, the expectation is a risk-neutral measure. Instead, we optimize the Conditional Value-at-Risk (CVaR), which allows the user the flexibility of choosing a risk level. We present an algorithm, based on the greedy algorithm, and prove that its performance has bounded suboptimality and improves with running time. We also present an online version of the algorithm to adapt to real-time scenarios. In the third part of this dissertation, we focus on scenarios where a set of robots may fail naturally or due to adversarial attacks. Our objective is to track as many targets as possible, a submodular measure, assuming worst-case robot failures. We present both centralized and distributed resilient tracking algorithms to cope with centralized and distributed communication settings. We prove these algorithms give a constant-factor approximation of the optimal in polynomial running time. / Doctor of Philosophy / Today, robotics and autonomous systems have been increasingly used in various areas such as manufacturing, military, agriculture, medical sciences, and environmental monitoring. However, most of these systems are fragile and vulnerable to adversarial attacks and uncertain environmental conditions. In most cases, even if a part of the system fails, the entire system performance can be significantly undermined. As robots start to coexist with humans, we need algorithms that can be trusted under real-world, not just ideal conditions. Thus, this dissertation focuses on enabling security, trustworthiness, and long-term autonomy in robotics and autonomous systems. In particular, we devise coordination algorithms that are resilient to attacks, trustworthy in the face of the uncertain conditions, and allow the long-term operation of multi-robot systems. We evaluate our algorithms through extensive simulations and proof-of-concept experiments. Generally speaking, multi-robot systems form the "physical" layer of Cyber-Physical Sytems (CPS), the Internet of Things (IoT), and Smart City. Thus, our research can find applications in the areas of connected and autonomous vehicles, intelligent transportation, communications and sensor networks, and environmental monitoring in smart cities.