Collective Complexity out of Individual Simplicity

The concept of Swarm Intelligence (SI) was first introduced by Gerardo Beni, Suzanne Hackwood, and Jing Wang in 1989 when they were investigating the properties of simulated, self-organizing agents in the framework of cellular robotic systems [1]. Eric Bonabeau, Marco Dorigo, and Guy Theraulaz extend the restrictive context of this early work to include “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies,” such as ants, termites, bees, wasps, “and other animal societies.” The abilities of such systems appear to transcend the abilities of the constituent individuals. In most biological cases studied so far, robust and capable high-level group behavior has been found to be mediated by nothing more than a small set of simple low-level interactions between individuals, and between individuals and the environment. The SI approach, therefore, emphasizes parallelism, distributedness, and exploitation of direct (agent-to-agent) or indirect (via the environment) local interactions among relatively simple agents

Modeling and Analysis of Beaconless and Beacon-Based Policies for a Swarm-Intelligent Inspection System

We are developing a swarm-intelligent inspection system based on a swarm of autonomous, miniature robots, using only on-board, local sensors. To estimate intrinsic advantages and limitations of the proposed possible distributed control solution, we capture the dynamic of the system at a higher abstraction level using non-spatial probabilistic microscopic and macroscopic models. In a previous publication, we showed that we are able to predict quantitatively the performances of the swarm of robots for a given metric and a beaconless policy. In this paper, after briefly reviewing our modeling methodology, we explore the effect of adding an additional state to the individual robot controller, which allow robots to serve as a beacon for teammates and therefore bias their inspection routes. Results show that this additional complexity helps the swarm of robots to be more efficient in terms of energy consumption but not necessarily in terms of time required to complete the inspection. We also demonstrate that a beacon-based policy introduces a strong coupling among the behavior of robots, coupling which in turn results in nonlinearities at the macroscopic model level

Collective Inspection of Regular Structures using a Swarm of Miniature Robots

We present a series of experiments concerned with the inspection of regular, engineered structures carried out using swarms of five to twenty autonomous, miniature robots, solely endowed with onboard, local sensors. Individual robot controllers are behaviorbased and the swarm coordination relies on a fully distributed control algorithm. The resulting collective behavior emerges from a combination of simple robot-robot interactions and the underlying environmental template. To estimate intrinsic advantages and limitations of the proposed control solution, we capture its characteristics at higher abstraction levels using nonspatial, microscopic and macroscopic probabilistic models. Although both types of models achieve only qualitatively correct predictions, they help us to shed light on the influence of the environmental template and control design choices on the considered nonspatial swarm metrics (inspection time and redundancy). Modeling results suggest that additional geometric details of the environmental structure should be taken into account for improving prediction accuracy and that the proposed control solution can be further optimized without changing its underlying architecture

A macroscopic analytical model of collaboration in distributed robotic systems

In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots, 11, 149-171]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased

Dynamic positioning of beacon vehicles for cooperative underwater navigation

Autonomous Underwater Vehicles (AUVs) are used for an ever increasing range of applications due to the maturing of the technology. Due to the absence of the GPS signal underwater, the correct estimation of its position is a challenge for submerged vehicles. One promising strategy to mitigate this problem is to use a group of AUVs where one or more assume the role of a beacon vehicle which has a very accurate position estimate due to an expensive navigation suite or frequent surfacings. These beacon vehicles broadcast their position and the remaining survey vehicles can use this position information and intra-vehicle ranges to update their position estimate. The effectiveness of this approach strongly depends on the geometry between the beacon vehicles and the survey vehicles. The trajectories of the beacon vehicles should thus be planned with the goal to minimize the position uncertainty of the survey vehicles. We propose a distributed algorithm which dynamically computes the locally optimal position for a beacon vehicle using only information obtained from broadcast communication of the survey vehicles. It does not need prior information about the survey vehicles' trajectory and can be used for any group size of beacon and survey vehicles.United States. Office of Naval Research (Grant N00014-97-1-0202)United States. Office of Naval Research (Grant N00014-05-1-0255)United States. Office of Naval Research (Grant N00014-02-C- 0210)United States. Office of Naval Research (Grant N00014-07-1-1102

A Reciprocal Sampling Algorithm for Lightweight Distributed Multi-Robot Localization

This work is situated in the context of collaboratively solving the localization problem for unknown initial conditions. We address this problem with a novel, fully decentralized, real-time particle filter algorithm, designed to accommodate realistic robotic assumptions including noisy sensors, and asynchronous and lossy communication. In particular, we introduce a collaborative reciprocal sampling algorithm which allows a drastic reduction in the number of particles needed to achieve localization. We elaborate an analysis of our reciprocal sampling method and support our conclusions with simulation results. Finally, we validate our approach on a team of four real robots within a controlled experimental setup

Distributed Adaptation in Multi-Robot Search using Particle Swarm Optimization

We present an adaptive strategy for a group of robots engaged in the localization of multiple targets. The robotic search algorithm is inspired by chemotaxis behavior in bacteria, and the algorithmic parameters are updated using a distributed implementation of the Particle Swarm Optimization technique. We explore the efficacy of the adaptation, the impact of using local fitness measurements to improve global fitness, and the effect of different particle neighborhood sizes on performance. The robustness of the approach in non-static environments is tested in a time-varying scenario

Characterization and Validation of a Novel Robotic System for Fluid-Mediated Programmable Stochastic Self-Assembly

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