Robot swarms can be tasked with a variety of automated sensing and inspection applications in aerial, aquatic, and surface environments. In this paper, we study a simplified two-outcome surface inspection task. We task a group of robots to inspect and collectively classify a 2D surface section based on a binary pattern projected on the surface. We use a decentralized Bayesian decision-making algorithm and deploy a swarm of 3-cm sized wheeled robots to inspect a randomized black and white tiled surface section of size 1m×1m in simulation. We first describe the model parameters that characterize our simulated environment, the robot swarm, and the inspection algorithm. We then employ a noise-resistant heuristic optimization scheme based on the Particle Swarm Optimization (PSO) using a fitness evaluation that combines the swarm’s classification decision accuracy and decision time. We use our fitness measure definition to asses the optimized parameters through 100 randomized simulations that vary surface pattern and initial robot poses. The optimized algorithm parameters show up to 55% improvement in median of fitness evaluations against an empirically chosen parameter set.

Groups of robots can be tasked with identifying a location in an environment where a feature cue is past a threshold, then disseminating this information throughout the group – such as identifying a high-enough elevation location to place a communications tower. This is a continuous-cue target search, where multi-robot search algorithms like particle swarm optimization (PSO) can improve search time through parallelization. However, many robots lack global communication in large spaces, and PSO-based algorithms often fail to consider how robots disseminate target knowledge after a single robot locates it. We present a two-stage hybrid algorithm to solve this task: (1) locating a target with a variation of PSO, and (2) moving to maximize target knowledge across the group. We conducted parameter sweep simulations of up to 32 robots in a grid-based grayscale environment. Pre-decision, we find that PSO with a variable velocity update interval improves target localization. In the post-decision phase, we show that dispersion is the fastest strategy to communicate with all other robots. Our algorithm is also competitive with a coverage sweep benchmark, while requiring significantly less inter-individual coordination.

Social insects successfully create bridges, rafts, nests and other structures out of their own bodies and do so with no centralized control system, simply by following local rules. For example, while traversing rough terrain, army ants (genus Eciton) build bridges which grow and dissolve in response to local traffic. Because these self-assembled structures incorporate smart, flexible materials (i.e. ant bodies) and emerge from local behavior, the bridges are adaptive and dynamic. With the goal of realizing robotic collectives with similar features, we designed a hardware system, Eciton robotica, consisting of flexible robots that can climb over each other to assemble compliant structures and communicate locally using vibration. In simulation, we demonstrate self-assembly of structures: using only local rules and information, robots build and dissolve bridges in response to local traffic and varying terrain. Unlike previous self-assembling robotic systems that focused on latticebased structures and predetermined shapes, our system takes a new approach where soft robots attach to create amorphous structures whose final self-assembled shape can adapt to the needs of the group.

Creating accurate models of stochastically self-assembling systems is a key step in developing control strategies, centralized or distributed, for the self-assembly process. This paper comparatively studies several aspects of developing probabilistic models for programmable self-assembling systems of stochastically interacting modules. In particular, we systematically investigate Markov models as well as hidden Markov models to predict the self-assembly process dynamics. We consider a case study leveraging our fluidic self-assembly robotic system. The ground truth is obtained through a high-fidelity simulation, calibrated using real experimental data. We first consider Markov models and employ the formalism of chemical reaction networks. In order to compute the model parameters, i.e. the reaction rates in the network, three different methods are studied. We then investigate the validity of the underlying well-mixed assumption, and thus the Markov property, for our system through estimation of the diffusion coefficient, through two different approaches. The system is shown to be borderline well-mixed, motivating extension of the initial Markov models to more complex models in order to achieve improved model accuracy. We formulate an automatic method for creating a hidden Markov model starting from a Markov model, based on a previously existing systematic method. Sample trajectories of the models are realized using the Gillespie's method. The resulting hidden Markov model is shown to achieve an improved accuracy over the standard Markov model.

Several self-assembly systems have been developed in recent years, where depending on the capabilities of the building blocks and the controlability of the environment, the assembly process is guided typically through either a fully centralized or a fully distributed control approach. In this work, we present a novel experimental system for studying the range of fully centralized to fully distributed control strategies. The system is built around the floating 3-cm-sized Lily robots, and comprises a water-filled tank with peripheral pumps, an overhead camera, an overhead projector, and a workstation capable of controlling the fluidic flow field, setting the ambient luminosity, communicating with the robots over radio, and visually tracking their trajectories. We carry out several experiments to characterize the system and validate its capabilities. First, a statistical analysis is conducted to show that the system is governed by reaction diffusion dynamics, and validate the applicability of the standard chemical kinetics modeling. Additionally, the natural tendency of the system for structure formation subject to different flow fields is investigated and corresponding implications on guiding the self-assembly process are discussed. Finally, two control approaches are studied: 1) a fully distributed control approach and 2) a distributed approach with additional central supervision exhibiting an improved performance. The formation time statistics are compared and a discussion on the generalization of the method is provided.

Fluid-mediated programmable stochastic self-assembly offers promising means to formation of target structures capable of a variety of functionalities. While miniaturized building blocks allow for finer resolutions in such structures, as well as access to unconventional environments, they can only be endowed with very limited on-board resources. In this paper we present the design, fabrication, and experimental results validating the key functionalities of the Lily robot as the building block in a programmable stochastic fluidic self-assembly system, capable of forming 2D structures. In particular, we aim at driving a system including an arbitrary number of Lilies to form target structures through parallel self-assembly, using exclusively local information and communication. While capable of wireless communication to a base station, Lilies are endowed with custom-designed electropermanent magnets to latch and also to communicate locally with their neighbors. Several experiments validate the reliability of the radio channel as well as the robustness of the local induction-based communication which allows for data transfer at 9600 bps with a success rate of 92.8% without repetition. The latches are shown to hold four times the weight of a single robot and to drag in another Lily from a distance of 4 mm in water.