Mean-shift exploration in shape assembly of robot swarms

Abstract

The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms. Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea is realized by adapting the mean-shift algorithm, which is an optimization technique widely used in machine learning for locating the maxima of a density function. The proposed strategy empowers robot swarms to assemble highly complex shapes with strong adaptability, as verified by experiments with swarms of 50 ground robots. The comparison between the proposed strategy and the state-of-the-art demonstrates its high efficiency especially for large-scale swarms. The proposed strategy can also be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.

Controlling the dynamic behavior of decentralized cluster through centralized approaches

“…Within such systems, individual behaviors are regulated by a central entity or device node. [16] The central node or controller must possess comprehensive information about the behavior characteristics and positions of all individuals, [17] enabling global coordination, decision-making, and task distribution. [18] For various tasks, the central entity must formulate distinct control strategies tailored to the task requirements and communicate them to individuals assigned specific roles.…”

“…[31] Some studies have ingeniously designed group tasks, allowing individual task adaptability through straightforward macro control, achieving efficient group task completion without escalating central control complexity. [16,32] The exploration of information sharing among individuals has also been undertaken to facilitate independent decision-making with global information reference, circumventing local optimal solutions and approximating characteristics akin to centralized control in decentralized groups. [33] However, these methods often necessitate individuals with complex functionalities and robust computational capabilities, posing challenges when dealing with simpler individuals unable to share information.…”

Micro/Nanorobotic Swarms: From Fundamentals to Functionalities

“…10,11 In the early 2000s, various types of macroscale (>10 −1 m) modular robotic systems and robotic swarms consisting of a few robots (e.g., Kheperas, 12 sbots, 13−15 and Jasmines 16 ) began to appear, and the macroscale robotic swarm was systematically reviewed. 17 Since then, macroscale and mesoscale (10 −3 m to 10 −1 m) robotic swarms with homogeneous agents, such as Slimebots, 18 Alices, 19 epucks, 20 Kilobots, 21 Bubblebots, 22 Bristle-bots, 23,24 Particlebots, 25 mindless rodlike robots, 26 Rainbow-bots, 27 and Morphobots, 28 and heterogeneous robotic swarms, such as Swarmanoid, 29 have been developed, inspired by natural swarm behaviors like insect pattern formations and phototaxis. To date, for large-scale robots, swarm coordination has been designed for cooperative tasks, such as directional motion, 21,25,30 obstacle traversal, 25,31,32 cargo allocation, 15,25,31,33 environment exploration, 34−36 and viral testing.…”

“…Since the 1990s, researchers have been leveraging natural swarm behaviors and intelligence for developing swarms in robotics, where groups of robots coordinate and cooperatively perform tasks. , In the 1990s, the development of algorithms had already begun for robotic swarms, including pattern generation, navigation, , and materials design. , In the early 2000s, various types of macroscale (>10 –1 m) modular robotic systems and robotic swarms consisting of a few robots (e.g., Kheperas, s-bots, − and Jasmines) began to appear, and the macroscale robotic swarm was systematically reviewed . Since then, macroscale and mesoscale (10 –3 m to 10 –1 m) robotic swarms with homogeneous agents, such as Slimebots, Alices, e-pucks, Kilobots, Bubblebots, Bristle-bots, , Particle-bots, mindless rodlike robots, Rainbow-bots, and Morphobots, and heterogeneous robotic swarms, such as Swarmanoid, have been developed, inspired by natural swarm behaviors like insect pattern formations and phototaxis. To date, for large-scale robots, swarm coordination has been designed for cooperative tasks, such as directional motion, ,, obstacle traversal, ,, cargo allocation, ,,, environment exploration, − and viral testing …”