Adaptive Division of Labor in Large-Scale Minimalist Multi-Robt Systems

A Large-Scale Minimalist Multi-Robot System (LMMS) is one composed of a group of robots each with limited capabilities in terms of sensing, computation, and communication. Such systems have received increased attention due to their empirically demonstrated performance and beneficial characteristics, such as their robustness to environmental perturbations and individual robot failure and their scalability to large numbers of robots. However, little work has been done in investigating ways to endow such a LMMS with the capability to achieve a desired division of labor over a set of dynamically evolving concurrent tasks, important in many task-achieving LMMS. Such a capability can help to increase the efficiency and robustness of overall task performance as well as open new domains in which LMMS can be seen as a viable alternative to more complex control solutions. In this paper we present a method for achieving a desired division of labor in a LMMS, experimentally validate it in a realistic simulation, and demonstrate its potential to scale to large numbers of robots and its ability to adapt to environmental perturbations. I. I NTRODUCTION AND MOTIVATION A Large-Scale Minimalist Multi-Robot System (LMMS) is a multi-robot system composed of a large number of robots, each having limited capabilities in terms of sensing, computational power, and communication range and bandwidth. We define a minimalist robotas one which maintains little or no state information, extracts limited, local, and noisy information from its available sensors, and lacks the capability for active communication with other robots. Due to these limited capabilities, the world in which a minimalist robot is situated is formally partially-observable and highly non-stationary, and it is therefore not practical to assume that such a robot is capable of reliably knowing a significant portion of the current global state of the environment or of overall task progress. These limitations in sensing, communication, and computation preclude a minimalist robot from performing tasks requiring significant computation or communication capabilities. Nonetheless, minimalist robots have been shown to be highly effective at a number of collective tasks, such as multi-robot formation control [4], collecti on [7], and robot soccer [18]. A system composed of a large number of such minimalist robots has the potential of conferring advantages including increased robustness to individual robot failure as no single robot is critical to task performance, the prospect of scaling to increasingly larger numbers of robots as there are few bottlenecks in terms of complex communication, planning, or coordination requirements, and increased adaptability to changes in the environment since individuals act based on local information and are not tied to globally coordinated plans. The aim of this work is to investigate a method by which to endow a LMMS with the capability to achieve a desired division of labor over a set of dynamically evolving concurrent tasks, a critical requirement of any task achieving large-scale multi-robot system. We define division of laboras the phenomenon in which individuals in a multi-robot system concurrently execute a set of tasks. Such division of labor may need to be continuously adjusted in response to changes in the task environment or group performance. The broader scope of this work is in understanding the ways in which to achieve robust, scalable, and efficient coordination in a LMMS. This paper is organized as follows. In Section II we provide the relevant related work. In Section III we give a detailed description of the concurrent foraging task domain we use as validation of our division of labor mechanism. In Section IV we present the robot controller we use to produce a division of labor in a LMMS. In Section V we describe and analyze experimental results, and in Section VI we draw conclusions from this work.