Analysis of Dynamic Task Allocation in Multi-Robot Systems

Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of task, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of the collective behavior. Specifically, we analyze the effect that the number of observations and the choice of the decision function have on the performance of the system. The mathematical models are validated in a multi-robot multi-foraging scenario. The model's predictions agree very closely with experimental results from sensor-based simulations.Comment: Preprint version of the paper published in International Journal of Robotics, March 2006, Volume 25, pp. 225-24

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

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

Towards Multi-Level Modeling of Self-Assembling Intelligent Micro-Systems

We investigate and model the dynamics of two-dimensional stochastic self-assembly of intelligent micro-systems with minimal requirements in terms of sensing, actuation, and control. A microscopic agent-based model accounts for spatiality and serves as a baseline for assessing the accuracy of models at higher abstraction level. Spatiality is relaxed in Monte Carlo simulations, which still capture the binding energy of each individual aggregate. Finally, we introduce a macroscopic model that only keeps track of the average number of aggregates in each energy state. This model is able to quantitatively and qualitatively predict the dynamics observed at lower, more detailed modeling levels. Since we investigate an idealized system, thus making very few assumptions about the exact nature of the final target system, our framework is potentially applicable to a large body of self-assembling agents ranging from functional micro-robots endowed with simple sensors and actuators to elementary microfabricated parts. In particular, we show how our suite of models at different abstraction levels can be used for optimizing both the design of the building blocks and the control of the stochastic process