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Studies of minimalist multi-robot systems consider multiple robotic agents, each with limited individual capabilities, but with the capacity for self-organization in order to collectively perform coordinated tasks. Object clustering is a widely studied task in which self-organized robots form piles from dispersed objects. Our work considers a variation of an object clustering derived from the influential ant-inspired work of Beckers, Holland and Deneubourg which proposed stigmergy as a design principle for such multi-robot systems. Since puck mechanics contribute to cluster accrual dynamics, we studied a new scenario with square objects because these pucks into clusters differently from cylindrical ones. Although central clusters are usually desired, workspace boundaries can cause perimeter cluster formation to dominate. This research demonstrates successful clustering of square boxes - an especially challenging instance since flat edges exacerbate adhesion to boundaries - using simpler robots than previous published research. Our solution consists of two novel behaviours, Twisting and Digging, which exploit the objects’ geometry to pry boxes free from boundaries. Physical robot experiments illustrate that cooperation between twisters and diggers can succeed in forming a single central cluster. We empirically explored the significance of different divisions of labor by measuring the spatial distribution of robots and the system performance. Data from over 40 hours of physical robot experiments show that different divisions of labor have distinct features, e.g., one is reliable while another is especially efficient.

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Autonomous robots like household service robots, self-driving cars and dronesare emerging as important parts of our daily lives in the near future. They need tocomprehend and fulfill complex tasks specified by the users with minimal humanintervention. Also they should be able to handle un-modeled changes and contingentevents in the workspace. More importantly, they shall communicate and collaboratewith each other in an efficient and correct manner. In this thesis, we address theseissues by focusing on the distributed and hybrid control of multi-robot systemsunder complex individual tasks. We start from the nominal case where a single dynamical robot is deployed in astatic and fully-known workspace. Its local tasks are specified as Linear TemporalLogic (LTL) formulas containing the desired motion. We provide an automatedframework as the nominal solution to construct the hybrid controller that drives therobot such that its resulting trajectory satisfies the given task. Then we expand theproblem by considering a team of networked dynamical robots, where each robot hasa locally-specified individual task also as LTL formulas. In particular, we analyzefour different aspects as described below. When the workspace is only partially known to each robot, the nominal solutionmight be inadequate. Thus we first propose an algorithm for initial plan synthesis tohandle partially infeasible tasks that contain hard and soft constraints. We designan on-line scheme for each robot to verify and improve its local plan during runtime, utilizing its sensory measurements and communications with other robots. Itis ensured that the hard constraints for safety are always fulfilled while the softconstraints for performance are improved gradually. Secondly, we introduce a new approach to construct a full model of both robotmotion and actions. Based on this model, we can specify much broader robotic tasksand it is used to model inter-robot collaborative actions, which are essential for manymulti-robot applications to improve system capability, efficiency and robustness.Accordingly, we devise a distributed strategy where the robots coordinate theirmotion and action plans to fulfill the desired collaboration by their local tasks. Thirdly, continuous relative-motion constraints among the robots, such as collision avoidance and connectivity maintenance, are closely related to the stability,safety and integrity of multi-robot systems. We propose two different hybrid controlapproaches to guarantee the satisfaction of all local tasks and the relative-motionconstraints at all time: the first one is based on potential fields and nonlinear controltechnique; the second uses Embedded Graph Grammars (EGGs) as the main tool. At last, we take into account two common cooperative robotic tasks, namelyservice and formation tasks. These tasks are requested and exchanged among therobots during run time. The proposed hybrid control scheme ensures that the real-time plan execution incorporates not only local tasks of each robot but also thecontingent service and formation tasks it receives. Some of the theoretical results of the thesis have been implemented and demonstrated on various robotic platforms. / Denna avhandling fokuserar på distribuerad och hybridstyrning av multi-robot-system för komplexa, lokala och tidsberoende uppgifter. Dessa uppgifter specificerasav logiska formler rörande robotens rörelser och andra ageranden. Avhandlingenbehandlar ett tvärvetenskapligt område som integrerar reglering av nätverkaderobotsystem och planering baserad på formella metoder. Ett ramverk för hybridstyrning av flera dynamiska robotar med lokalt specificerade uppgifter presenteras.Fyra huvudscenarier betraktas: (1) robot-planering med motstridiga arbetsuppgifterinom ett delvis okänt arbetsområde; (2) beroende uppgifter för en grupp heterogenaoch samverkande robotar; (3) relativa rörelsebegränsningar hos varje robot; samt(4) robotar med uppgifter som begärs och bekräftas under körning. Numeriskasimuleringar och experiment visas för att validera de teoretiska resultaten. / <p>QC 20151204</p> / EU STREP RECONFIG: FP7-ICT-2011-9-600825 / Swedish Research Council (VR)

In this thesis, we examine algorithms for the division of labour in a group of robot. The algorithms make no use of direct communication. Instead, they are based only on the interactions among the robots and between the group and the environment. Division of labour is the mechanism that decides how many robots shall be used to perform a task. The efficiency of the group of robots depends in fact on the number of robots involved in a task. If too few robots are used to achieve a task, they might not be successful or might perform poorly. If too many robots are used, it might be a waste of resources. The number of robots to use might be decided a priori by the system designer. More interestingly, the group of robots might autonomously select how many and which robots to use. In this thesis, we study algorithms of the latter type. The robotic literature offers already some solutions, but most of them use a form of direct communication between agents. Direct, or explicit, communication between the robots is usually considered a necessary condition for co-ordination. Recent studies have questioned this assumption. The claim is based on observations of animal colonies, e.g., ants and termites. They can effectively co-operate without directly communicating, but using indirect forms of communication like stigmergy. Because they do not rely on communication, such colonies show robust behaviours at group level, a condition that one wishes also for groups of robots. Algorithms for robot co-ordination without direct communication have been proposed in the last few years. They are interesting not only because they are a stimulating intellectual challenge, but also because they address a situation that might likely occur when using robots for real-world out-door applications. Unfortunately, they are still poorly studied. This thesis helps the understanding and the development of such algorithms. We start from a specific case to learn its characteristics. Then we improve our understandings through comparisons with other solutions, and finally we port everything into another domain. We first study an algorithm for division of labour that was inspired by ants' foraging. We test the algorithm in an application similar to ants' foraging: prey retrieval. We prove that the model used for ants' foraging can be effective also in real conditions. Our analysis allows us to understand the underlying mechanisms of the division of labour and to define some way of measuring it. Using this knowledge, we continue by comparing the ant-inspired algorithm with similar solutions that can be found in the literature and by assessing their differences. In performing these comparisons, we take care of using a formal methodology that allows us to spare resources. Namely, we use concepts of experiment design to reduce the number of experiments with real robots, without losing significance in the results. Finally, we apply and port what we previously learnt into another application: Sensor/Actor Networks (SANETs). We develop an architecture for division of labour that is based on the same mechanisms as the ants' foraging model. Although the individuals in the SANET can communicate, the communication channel might be overloaded. Therefore, the agents of a SANET shall be able to co-ordinate without accessing the communication channel.

<p>The purpose of this work is to providing a general set of base classes for building simulators, for example a robot system. Instead of every time building a new simulator from the beginning, you get the already completed components from a library. The library should in that way act as a springboard for development.</p><p>To do this work, there’s a lot of knowledge required in how a general robot system could look like. Therefore it was necessary to gather some information about robotics. </p><p>On the basis of what the information gave, two different user cases were built. From these user cases, the components were fetched, which was supposed to be included in the library. </p><p>The result of this work leads to a number of classes, which describes the components. A simple test program is also done, which handles the components. To get the classes more useful, the com-ponents must be broken down in much smaller parts. The operations on the classes should also be more realistic than what has been achieved in this work.</p>

The purpose of this work is to providing a general set of base classes for building simulators, for example a robot system. Instead of every time building a new simulator from the beginning, you get the already completed components from a library. The library should in that way act as a springboard for development. To do this work, there’s a lot of knowledge required in how a general robot system could look like. Therefore it was necessary to gather some information about robotics. On the basis of what the information gave, two different user cases were built. From these user cases, the components were fetched, which was supposed to be included in the library. The result of this work leads to a number of classes, which describes the components. A simple test program is also done, which handles the components. To get the classes more useful, the com-ponents must be broken down in much smaller parts. The operations on the classes should also be more realistic than what has been achieved in this work.

This thesis examines the use of multiple robots in cooperative simultaneous localization and mapping (SLAM), where each robot must estimate the poses of all robots in the team, along with the positions of all known landmarks. The robot team must operate under the condition that the communication network between robots is never guaranteed to be fully connected. Under this condition, a novel algorithm is derived that allows each robot to obtain the centralized-equivalent estimate in a decentralized manner, whenever possible. The algorithm is then extended to a decentralized and distributed approach where robots share the computational burden in considering different data association hypotheses in generating the centralized-equivalent consensus estimate.

This thesis examines the use of multiple robots in cooperative simultaneous localization and mapping (SLAM), where each robot must estimate the poses of all robots in the team, along with the positions of all known landmarks. The robot team must operate under the condition that the communication network between robots is never guaranteed to be fully connected. Under this condition, a novel algorithm is derived that allows each robot to obtain the centralized-equivalent estimate in a decentralized manner, whenever possible. The algorithm is then extended to a decentralized and distributed approach where robots share the computational burden in considering different data association hypotheses in generating the centralized-equivalent consensus estimate.