When designing swarm-robotic systems, systematic comparison of algorithms from different domains is necessary to determine which is capable of scaling up to handle the target problem size and target operating conditions. We propose a set of quantitative metrics for scalability, flexibility, and emergence which are capable of addressing these needs during the system design process. We demonstrate the applicability of our proposed metrics as a design tool by solving a large object gathering problem in temporally varying operating conditions using iterative hypothesis evaluation. We provide experimental results obtained in simulation for swarms of over 10,000 robots.
Within SR, no widely accepted theory of self-organizing systems exists @cite_7 @cite_23 . Cotsaftis presents a control-theoretic model of emergence, distinguishing between systems which can be studied by the methods of scientific reductionism, and systems which originate from the existence of a threshold above which interaction between system components overtakes outside interactions, leading to system self-organization and new behavior not predictable from component study, similar to @cite_24 . While many papers cite evidence that their algorithms exhibit emergent behavior @cite_16 @cite_2 @cite_17 @cite_22 , or even prove simple emergent properties via temporal logic ( @cite_5 ), few provide a quantitative method for measuring emergence (with the exception of @cite_13 , who used robot nearest-neighbor calculations to calculate a degree of interaction for the swarm). We present an empirical method for measuring the level of self organization present in a swarm by measuring the linearity of inter-robot interference as the swarm size is increased, as a small step towards the development of a more general emergence theory.
@cite_22 @cite_7 @cite_24 @cite_23 @cite_2 @cite_5 @cite_16 @cite_13 @cite_17
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Designing and analyzing self-organizing systems such as robotic swarms is a challenging task even though we have complete knowledge about the robot’s interior. It is difficult to determine the individual robot’s behavior based on the swarm behavior and vice versa due to the high number of agent–agent interactions. A step towards a solution of this problem is the development of appropriate models which accurately predict the swarm behavior based on a specified control algorithm. Such models would reduce the necessary number of time-consuming simulations and experiments during the design process of an algorithm. In this paper we propose a model with focus on an explicit representation of space because the effectiveness of many swarm robotic scenarios depends on spatial inhomogeneity. We use methods of statistical physics to address spatiality. Starting from a description of a single robot we derive an abstract model of swarm motion. The model is then extended to a generic model framework of communicating robots. In two examples we validate models against simulation results. Our experience shows that qualitative correctness is easily achieved, while quantitative correctness is disproportionately more difficult but still possible. This work presents a novel control approach for allocating a robotic swarm among boundaries. It represents the first step toward developing a methodology for encounter-based swarm allocation that incorporates rigorously characterized spatial effects in the system without requiring analytical expressions for encounter rates. Our approach utilizes a macroscopic model of the swarm population dynamics to design stochastic robot control policies that result in target allocations of robots to the boundaries of regions of different types. The control policies use only local information and have provable guarantees on the collective swarm behavior. We analytically derive the relationship between the stochastic control policies and target allocations for a scenario in which circular robots avoid collisions with each other, bind to boundaries of disk-shaped regions, and command bound robots to unbind. We validate this relationship in simulation and show that it is robust to environmental changes, such as a change in the number or size of robots and disks. This paper presents a methodology for finding optimal control parameters as well as optimal system parameters for robot swarm controllers using probabilistic, population dynamic models. With distributed task allocation as a case study, we show how optimal control parameters leading to a desired steady-state task distribution for two fully-distributed algorithms can be found even if the parameters of the system are unknown. First, a reactive algorithm in which robots change states independently from each other and which leads to a linear macroscopic model describing the dynamics of the system is considered. Second, a threshold-based algorithm where robots change states based on the number of other robots in this state and which leads to a non-linear model is investigated. Whereas analytical results can be obtained for the linear system, the optimization of the non-linear controller is performed numerically. Finally, we show using stochastic simulations that whereas the presented methodology and models work best if the swarm size is large, useful results can already be obtained for team-sizes below a hundred robots. The methodology presented can be applied to scenarios involving the control of large numbers of entities with limited computational and communication abilities as well as a tight energy budget, such as swarms of robots from the centimeter to nanometer range or sensor networks. The development of a complex system depends on the self-coordinated action of a large number of agents, often determining unexpected global behavior. The case of software evolution has great practical importance: knowledge of what is to be considered atypical can guide developers in recognizing and reacting to abnormal behavior. Although the initial framework of a theory of software exists, the current theoretical achievements do not fully capture existing quantitative data or predict future trends. Here we show that two elementary laws describe the evolution of package sizes in a Linux-based operating system: first, relative changes in size follow a random walk with non-Gaussian jumps; second, each size change is bounded by a limit that is dependent on the starting size, an intriguing behavior that we call “soft bound.” Our approach is based on data analysis and on a simple theoretical model, which is able to reproduce empirical details without relying on any adjustable parameter and generates definite predictions. The same analysis allows us to formulate and support the hypothesis that a similar mechanism is shaping the distribution of mammalian body sizes, via size-dependent constraints during cladogenesis. Whereas generally accepted approaches struggle to reproduce the large-mass shoulder displayed by the distribution of extant mammalian species, this is a natural consequence of the softly bounded nature of the process. Additionally, the hypothesis that this model is valid has the relevant implication that, contrary to a common assumption, mammalian masses are still evolving, albeit very slowly.