Swarm robotics is a young multidisciplinary research field at the<p>intersection of disciplines such as distributed systems, robotics,<p>artificial intelligence, and complex systems. Considerable research<p>effort has been dedicated to the study of algorithms targeted to<p>specific problems. Nonetheless, implementation and comparison remain difficult due to the lack of shared tools and benchmarks. Among the tools necessary to enable experimentation, the most fundamental is a simulator that offers an adequate level of accuracy and flexibility to suit the diverse needs of the swarm robotics<p>community. The very nature of swarm robotics, in which systems may comprise large numbers of robots, forces the design to provide<p>runtimes that increase gracefully with increasing swarm sizes.<p><p>In this thesis, I argue that none of the existing simulators offers<p>satisfactory levels of accuracy, flexibility, and efficiency, due to<p>fundamental limitations of their design. To overcome these<p>limitations, I present ARGoS---a general, multi-robot simulator that<p>currently benchmarks as the fastest in the literature.<p><p>In the design of ARGoS, I faced a number of unsolved issues. First, in existing simulators, accuracy is an intrinsic feature of the<p>design. For single-robot applications this choice is reasonable, but<p>for the large number of robots typically involved in a swarm, it<p>results in an unacceptable trade-off between accuracy and<p>efficiency. Second, the prospect of swarm robotics spans diverse<p>potential applications, such as space exploration, ocean restoration,<p>deep-underground mining, and construction of large structures. These applications differ in terms of physics (e.g. motion dynamics) and available communication means. The existing general-purpose simulators are not suitable to simulate such diverse environments accurately and efficiently.<p><p>To design ARGoS I introduced novel concepts. First, in ARGoS accuracy is framed as a property of the experimental setup, and is tunable to the requirements of the experiment. To achieve this, I designed the architecture of ARGoS to offer unprecedented levels of modularity. The user can provide customized versions of individual modules, thus assigning computational resources to the relevant aspects. This feature enhances efficiency, since the user can lower the computational cost of unnecessary aspects of a simulation.<p><p>To further decrease runtimes, the architecture of ARGoS exploits the computational resources of modern multi-core systems. In contrast to existing designs with comparable features, ARGoS allows the user to define both the granularity and the scheduling strategy of the parallel tasks, attaining unmatched levels of scalability and efficiency in resource usage.<p><p>A further unique feature of ARGoS is the possibility to partition the<p>simulated space in regions managed by dedicated physics engines<p>running in parallel. This feature, besides enhancing parallelism,<p>enables experiments in which multiple regions with different features are simulated. For instance, ARGoS can perform accurate and efficient simulations of scenarios in which amphibian robots act both underwater and on sandy shores.<p><p>ARGoS is listed among the major results of the Swarmanoid<p>project. It is currently<p>the official simulator of 4 European projects<p>(ASCENS, H2SWARM, E-SWARM, Swarmix) and is used by 15<p>universities worldwide. While the core architecture of ARGoS is<p>complete, extensions are continually added by a community of<p>contributors. In particular, ARGoS was the first robot simulator to be<p>integrated with the ns3 network simulator, yielding a software<p>able to simulate both the physics and the network aspects of a<p>swarm. Further extensions under development include support for<p>large-scale modular robots, construction of 3D structures with<p>deformable material, and integration with advanced statistical<p>analysis tools such as MultiVeStA. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

By enabling coordinated task execution and movement, robotic swarms can achieve efficient exploration or disaster site management. When utilizing Ultra-wideband (UWB) radio technology for ranging, the proposed relative localization sensor can be made lightweight and relatively indifferent to the ambient environment. Infrastructure dependency is eliminated by making the whole sensor fit on a swarm agent, while allowing for a certain amount of positional error. In this thesis, a novel algorithm is implemented in to constrained hardware and compared to a more traditional trilateration approach. Both algorithms utilize Particle Swarm Optimization (PSO) to be more robust towards noise and achieves similar accuracy, but the proposed algorithm can run up to ten times faster. The antenna array which forms the localization sensor weighs only 56g, and achieves around 0.5m RMSE with a 10Hz update rate. Experiments show that the accuracy can be further improved if the rotational bias observed in the UWB devices are compensated for.

abstract: The problem of modeling and controlling the distribution of a multi-agent system has recently evolved into an interdisciplinary effort. When the agent population is very large, i.e., at least on the order of hundreds of agents, it is important that techniques for analyzing and controlling the system scale well with the number of agents. One scalable approach to characterizing the behavior of a multi-agent system is possible when the agents' states evolve over time according to a Markov process. In this case, the density of agents over space and time is governed by a set of difference or differential equations known as a {\it mean-field model}, whose parameters determine the stochastic control policies of the individual agents. These models often have the advantage of being easier to analyze than the individual agent dynamics. Mean-field models have been used to describe the behavior of chemical reaction networks, biological collectives such as social insect colonies, and more recently, swarms of robots that, like natural swarms, consist of hundreds or thousands of agents that are individually limited in capability but can coordinate to achieve a particular collective goal. This dissertation presents a control-theoretic analysis of mean-field models for which the agent dynamics are governed by either a continuous-time Markov chain on an arbitrary state space, or a discrete-time Markov chain on a continuous state space. Three main problems are investigated. First, the problem of stabilization is addressed, that is, the design of transition probabilities/rates of the Markov process (the agent control parameters) that make a target distribution, satisfying certain conditions, invariant. Such a control approach could be used to achieve desired multi-agent distributions for spatial coverage and task allocation. However, the convergence of the multi-agent distribution to the designed equilibrium does not imply the convergence of the individual agents to fixed states. To prevent the agents from continuing to transition between states once the target distribution is reached, and thus potentially waste energy, the second problem addressed within this dissertation is the construction of feedback control laws that prevent agents from transitioning once the equilibrium distribution is reached. The third problem addressed is the computation of optimized transition probabilities/rates that maximize the speed at which the system converges to the target distribution. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2020

The space industry has been accelerating technological evolution since its inception. It has resulted in countless innovations being adapted and eventually finding their way into people's everyday life. It has also played a significant role in understanding life itself and the circumstances that are necessary to support it. A crucial step in obtaining an even further understanding of humanity's place in the universe is truly comprehending the Moon and its mysteries. In order to do the previously mentioned a manned research operation to the Moon is necessary. However, doing this requires an in-situ resources utilization (ISRU) approach due to the complexity as well as cost of launching material and equipment to space. The Moon holds a lot of valuable resources from which several critical substances and materials can be extracted, e.g., oxygen and hydrogen. In order to make use of the locally available resources, such as the regolith, a standardized approach is necessary.  There are several ways of designing something tasked with mining the Moon as well as enabling supporting activities, e.g., infrastructure development. A Design Thinking approach was used in order to get clarity regarding on how a concept doing this might look like. This thesis deals with the topic on a high, conceptual level due to the complexity of the subject. The needfinding and literature study provided background and context to design a solution enabling the earlier mentioned goal. The solution is a swarm system of Lunar rovers that are capable of operating together, as a unit, as well as on their own depending on the nature of the task that is performed. The activities are performed by interchangeable tool modules operated by the robots rather than the robots themselves. This provides prerequisites for a more flexible as well as resilient mission operation compared to many other scenarios. The interchangeable modules mechanism is the most important aspect of the proposed concept. Another important aspect concerning the platform of the concept is that it enables an infrastructure for new activities post-launch as long as the module fulfills some constraints. The thesis provides concepts for the robot, a regolith collector module as well as the container module. As a means of verifying the concept, a subsystem was selected and tested. The subsystem that was chosen was the module exchanging mechanism. Thus, a conceptual version of this was built and tested. The test was delimited and intended to determine whether an approach using screws and movable arms was appropriate to pick up a simplified container module. The test performed concluded that the subsystem has potential, even though a more similar mechanism to the one actually envisioned would be necessary to test. However, there are certain iterations that beneficially may be performed prior to a complete representation of the module equipping mechanism is built.

No description available.

A swarm is a group of uninformed individuals that exhibit collective behaviors. Without any information about the external world, a swarm has limited ability to achieve complex goals. Prior work on human-swarm interaction methods allow a human to influence these uninformed individuals through either leadership or predation as informed agents that directly interact with humans. These methods of influence have two main limitations: (1) although leaders sustain influence over nominal agents for a long period of time, they tend to cause all collective structures to turn in to flocks (negating the benefit of other swarm formations) and (2) predators tend to cause collective structures to fragment. In this thesis, we present the use of mediators as a novel form for human-swarm influence and use mediators to shape the perimeter of a swarm. The mediator method uses special agents that operate from within the spatial center of a swarm. This approach allows a human operator to coordinate multiple mediators to modulate a rotating torus into various shapes while sustaining influence over the swarm, avoiding fragmentation, and maintaining the swarm's connectivity. The use of mediators allows a human to mold and adapt the torus' behavior and structure to a wide range of spatio-temporal tasks such as military protection and decontamination tasks. Results from an experiment that compares previous forms of human influence with mediator-based control indicate that mediator-based control is more amenable to human influence for certain types of problems.

The self-organized nest construction behaviors of European paper wasps (Polistes dominula) show potential for adoption in artificial intelligence and robotic systems where centralized control proves challenging. However, P. dominula nest construction mechanisms are not fully understood. This research investigated how nest structures stimulate P. dominula worker action at different stages of nest construction. A novel stochastic site selection model, weighted by simple rules for cell age, height, and wall count, was implemented in a three-dimensional, step-by-step nest construction simulation. The simulation was built on top of a hexagonal coordinate system to improve precision and performance. Real and idealized nest data were used to evaluate simulated nests via two parameters: outer wall counts and compactness numbers. Structures generated with age-based rules were not significantly different from real nest structures along both parameters.

No description available.

This thesis describes a change to the Physiocomimetics Robotic Swarm Control framework that implements communication constraints into swarm behavior. These constraints are necessary to successfully implement theoretical applications in the real world. We describe the basic background of swarm robotics, the Physiocomimetics framework and methods that have attempted to implement communications constraints into robotic swarms. The Framework is changed by the inclusion of different virtual particles at a global and local scale that only cause a force on swarm elements if those elements are disconnected from a swarm network. The global particles introduced are a point of known connectivity and a global centroid of the swarm. The local particles introduced are the point of last connectivity and a local centroid. These particles are tested in various simulations and the results are discussed. The global particles are very effective at insuring the communication constraints of the swarm, but the local particles only have partial success. Additionally, some observations are made about swarm formations and the effect of the communication range used during swarm formation.

Doctor of Philosophy / Department of Computing and Information Sciences / David Gustafson / We use probabilistic graphs to predict the location of swarms over 100 steps in simulations in grid worlds. One graph can be used to make predictions for worlds of different dimensions. The worlds are constructed from a single 5x5 square pattern, each square of which may be either unoccupied or occupied by an obstacle or a target. Simulated robots move through the worlds avoiding the obstacles and tagging the targets. The interactions between the robots and the robots and the environment lead to behavior that, even in deterministic simulations, can be difficult to anticipate. The graphs capture the local rate and direction of swarm movement through the pattern. The graphs are used to create a transition matrix, which along with an occupancy matrix, can be used to predict the occupancy in the patterns in the 100 steps using 100 matrix multiplications. In the future, the graphs could be used to predict the movement of physical swarms though patterned environments such as city blocks in applications such as disaster response search and rescue. The predictions could assist in the design and deployment of such swarms and help rule out undesirable behavior.