We demonstrate our approach in experiments with up to ten differentially-driven robots and in simulations with fifty robots. The presented multi-robot system is capable of creating multi-pattern formations as well as transitions between arbitrary patterns, involving filling and non-filling patterns likewise.

Apr 15, 2021 · In this paper, we describe the formation control problem and survey recent advances focusing on aspects of maintaining a formation by a group of robots distinguished by the means of analysis.

May 1, 2017 · In this section, we review and discuss the application of these multicellular mechanisms in self-organising pattern formation of multi-robot systems, and show how researchers have borrowed the ideas and concepts, simplified and made them applicable to self-organisation of swarms of robots.

With this framework and Bayesian optimization, we can create effective control systems for several multi-robot tasks, including cohesion and segregation, pattern formation, and searching/foraging.

Jan 1, 2008 · An efficiency of an multi-robot systems depends on proper coordinating tasks of all robots. This paper presents a game theoretic approach to modelling and solving the pick-up and collection...

Apr 20, 2022 · A spatial-temporal optimization framework with a joint cost function that takes formation similarity, obstacle avoidance, and dynamic feasibility into account, which makes the swarm robots possess the ability to move in formation while avoiding obstacles.

Current application areas of pattern formation include search and rescue operations, landmine removal, remote terrain and space exploration, control of arrays of satellites and unmanned aerial vehicles (UAVs).

May 9, 2011 · This paper describes work on multi-robot pattern formation. Arbitrary target patterns are represented with an optimal robot deployment, using a method that is independent of the number of robots. Furthermore, the trajectories are visually appealing in the sense of being smooth, oscillation free, and showing fast convergence.

To improve efficiency, we propose a grouping-based optimization method. First, a specific grouping strategy (the number of groups and the number of data points in each group are fixed) is utilized to group the multiple robots.

In this paper, we propose a decentralized algorithm for a multi-robot system to generate a given formation with an arbitrary repeating pattern. We introduce basic pattern graph and assembling graph to define a repeating pattern and formation quality for measurement.