Imagine coating buildings and bridges with smart particles (also coined smart paint) that monitor structural integrity and sense and report on traffic and wind loads, leading to technology that could do such inspection jobs faster and cheaper and increase safety at the same time. In this paper, we study the problem of uniformly coating objects of arbitrary shape in the context of self-organizing programmable matter, i.e., programmable matter which consists of simple computational elements called particles that can establish and release bonds and can actively move in a self-organized way. Particles are anonymous, have constant-size memory, and utilize only local interactions in order to coat an object. We continue the study of our universal coating algorithm by focusing on its runtime analysis, showing that our algorithm terminates within a linear number of rounds with high probability. We also present a matching linear lower bound that holds with high probability. We use this lower bound to show a linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information, which implies that our algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of our algorithm may be better than linear in practice.
Our focus, however, is on active systems, in which computational particles control their actions and motions to complete specific tasks. Coating has been extensively studied in the area of , but not commonly treated as a stand-alone problem; it is instead examined as part of (e.g., @cite_19 ) or (e.g., see respective section of @cite_13 ). Some research focuses on coating objects as an independent task under the name of or . The techniques used in this context include stochastic robot behaviors @cite_2 @cite_20 , rule-based control mechanisms @cite_21 and potential field-based approaches @cite_6 . While the analytic techniques developed in swarm robotics are somewhat relevant to this work, many such systems assume more computational power and movement capabilities than the model studied in this work does. Michail and Spirakis recently proposed a model @cite_18 for network construction inspired by population protocols @cite_12 . The population protocol model is related to self-organizing particle systems, but is different in that agents (corresponding to our particles) can move freely in space and establish connections at any time. It would, however, be possible to adapt their approach to study coating problems under the population protocol model.
@cite_18 @cite_21 @cite_6 @cite_19 @cite_2 @cite_13 @cite_12 @cite_20
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In this paper, we present an approach to designing decentralized robot control policies that mimic certain microscopic and macroscopic behaviors of ants performing collective transport tasks. In prior work, we used a stochastic hybrid system model to characterize the observed team dynamics of ant group retrieval of a rigid load. We have also used macroscopic population dynamic models to design enzyme-inspired stochastic control policies that allocate a robotic swarm around multiple boundaries in a way that is robust to environmental variations. Here, we build on this prior work to synthesize stochastic robot attachment–detachment policies for tasks in which a robotic swarm must achieve non-uniform spatial distributions around multiple loads and transport them at a constant velocity. Three methods are presented for designing robot control policies that replicate the steady-state distributions, transient dynamics, and fluxes between states that we have observed in ant populations during group retrieval. The equilibrium population matching method can be used to achieve a desired transport team composition as quickly as possible; the transient matching method can control the transient population dynamics of the team while driving it to the desired composition; and the rate matching method regulates the rates at which robots join and leave a load during transport. We validate our model predictions in an agent-based simulation, verify that each controller design method produces successful transport of a load at a regulated velocity, and compare the advantages and disadvantages of each method. Imagine coating buildings and bridges with smart particles (also coined smart paint) that monitor structural integrity and sense and report on traffic and wind loads, leading to technology that could do such inspection jobs faster and cheaper and increase safety at the same time. In this paper, we study the problem of uniformly coating objects of arbitrary shape in the context of self-organizing programmable matter, i.e., programmable matter which consists of simple computational elements called particles that can establish and release bonds and can actively move in a self-organized way. Particles are anonymous, have constant-size memory, and utilize only local interactions in order to coat an object. We continue the study of our universal coating algorithm by focusing on its runtime analysis, showing that our algorithm terminates within a linear number of rounds with high probability. We also present a matching linear lower bound that holds with high probability. We use this lower bound to show a linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information, which implies that our algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of our algorithm may be better than linear in practice. Self-assembly of active, robotic agents, rather than of passive agents such as molecules, is an emerging research field that is attracting increasing attention. Active self-assembly techniques are especially attractive at very small spatial scales, where alternative construction methods are unavailable or have severe limitations. Building nanostructures by using swarms of very simple nanorobots is a promising approach for manufacturing nanoscale devices and systems. The method described in this paper allows a group of simple, physically identical, identically programmed and reactive (i.e., stateless) agents to construct and repair polygonal approximations to arbitrary structures in the plane. The distributed algorithms presented here are tolerant of robot failures and of externally-induced disturbances. The structures are self-healing, and self-replicating in a weak sense. Their components can be re-used once the structures are no longer needed. A specification of vertices at relative positions, and the edges between them, is translated by a compiler into reactive rules for assembly agents. These rules lead to the construction and repair of the specified shape. Simulation results are presented, which validate the proposed algorithms. 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.