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The term “swarm” has been applied to many systems (in biology, engineering, computation, etc.) as they have some of the qualities that the English-language term “swarm” denotes. With the growth of the various area of “swarm” research, the “swarm” terminology has become somewhat confusing. In this paper, we reflect on this terminology to help clarify its association with various robotic concepts.

Swarm robotics is a novel approach to the coordination of large numbers of relatively simple robots which takes its inspiration from social insects. This paper proposes a definition to this newly emerging approach by 1) describing the desirable properties of swarm robotic systems, as observed in the system-level functioning of social insects, 2) proposing a definition for the term swarm robotics, and putting forward a set of criteria that can be used to distinguish swarm robotics research from other multi-robot studies, 3) providing a review of some studies which can act as sources of inspiration, and a list of promising domains for the utilization of swarm robotic systems.

This paper reviews research in three important areas concerning robot swarms: communication, diversity, and learning. Communication (or the lack of it) is a key design consideration for robot teams. Communication can enable certain types of coordination that would be impossible otherwise. However communication can also add unnecessary cost and complexity. Important research issues regarding communication concern what should be communicated, over what range, and when the communication should occur. We also consider how diverse behaviors might help or hinder a team, and how to measure diversity in the first place. Finally, we show how learning can provide a powerful means for enabling a team to master a task or adapt to changing conditions. We hypothesize that these three topics are critically interrelated in the context of learning swarms, and we suggest research directions to explore them.

This paper provides an overview of the SWARM-BOTS project, a robotic project sponsored by the Future and Emerging Technologies program of the European Commission. The paper illustrates the goals of the project, the robot prototype and the 3D simulator we built. It also reports on the results of experimental work in which distributed adaptive controllers are used to control a group of real, or simulated, robots so that they perform a variety of tasks which require cooperation and coordination.

Using the biologically inspired notion of ‘virtual pheromone’ we describe how a robot swarm can become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. By virtue of this simple peer-to-peer messaging scheme, many coordinated activities can be accomplished without centralized control.

We describe a project where behaviors of robot swarms are designed and studied for use in a distributed mapping domain. Behaviors are studied in both simulation and physical robots. We discuss the advantages and challenges of swarm robotics, in general and specific to our research. Software implementations and algorithms are introduced, as well as methodologies for the creation and assessment of swarm behaviors.

This paper presents the visions and initial results of the I-SWARM project funded by the European Commission. The goal of the project is to build the first very large-scale artificial swarm (VLSAS) with a swarm size of up to 1,000 micro-robots with a planned size of 2×2×1 mm. First, the motivation for such a swarm is described and then first considerations and issues arising from the robots’ size resembling “artificial ants” and the MST approach taken to realize that size are given. The paper will conclude with a list of possible scenarios inspired by biology for such a robot swarm.

This paper provides an overview of our framework, called , for the distributed control of swarms of robots. We focus on robotic behaviors that are similar to those shown by solids, liquids, and gases. Solid formations are useful for distributed sensing tasks, while liquids are for obstacle avoidance tasks. Gases are handy for coverage tasks, such as surveillance and sweeping. Theoretical analyses are provided that allow us to reliably control these behaviors. Finally, our implementation on seven robots is summarized.

The purpose of this work is to enable an array of mobile sensors to autonomously arrange themselves into a regularly spaced lattice formation such that they may collectively be used as an effective phased-array sensor. Existing approaches to this problem encounter issues with local minima which allow the formation of lattice patterns that are locally regular but have discontinuities or defects that would be undesirable in a narrow-band beamforming application. By exploiting a common reference orientation, such as might be obtained from a magnetic compass, we have been able to create control laws that operate on orthogonal axes and thereby minimize the occurrence of local minima. The result is that we can now form lattice patterns with greater uniformity over extended distances, with significantly less energy or movement per robot. Despite the need for a shared directional reference, our methods are also robust to significant error in the reference readings.

While techniques exist for simulating swarming behaviors, these methods usually provide only simplistic navigation and planning capabilities. In this review, we explore the benefits of integrating roadmap-based path planning methods with flocking techniques to achieve different behaviors. We show how group behaviors such as exploring can be facilitated by using dynamic roadmaps (e.g., modifying edge weights) as an implicit means of communication between flock members. Extending ideas from cognitive modeling, we embed behavior rules in individual flock members and in the roadmap. These behavior rules enable the flock members to modify their actions based on their current location and state. We propose new techniques for several distinct group behaviors: homing, exploring (covering and goal searching), passing through narrow areas and shepherding. We present results that show that our methods provide significant improvement over methods that utilize purely local knowledge and moreover, that we achieve performance approaching that which could be obtained by an ideal method that has complete global knowledge. Animations of these behaviors can be viewed on our webpages.