A unified mathematical model for synchronisation and swarming has recently been proposed. Each system entity, called a "swarmalator", coordinates its internal phase and location with the other entities in a way that these two attributes are mutually coupled. This paper realises and studies, for the first time, the concept of swarmalators in a technical system. We adapt and extend the original model for its use with mobile robots and implement it in the Robot Operating System 2 (ROS 2). Simulations and experiments with small robots demonstrate the feasibility of the model and show its potential to be applied to real-world systems. All types of space-time patterns achieved in theory can be reproduced in practice. Applications can be found in monitoring, exploration, entertainment and art, among other domains.
There are different methods for pattern formation and control (see @cite_12 @cite_24 @cite_18 ). Self-organised flocking of mobile robots was studied and evaluated in simulations and on robots by @cite_21 and @cite_11 . Robotic swarm navigation with obstacle avoidance was analysed in @cite_3 .
@cite_18 @cite_21 @cite_3 @cite_24 @cite_12 @cite_11
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This paper presents the optimal path of nonholonomic multi robots with coherent formation in a leader-follower structure in the presence of obstacles using Asexual Reproduction Optimization (ARO). The robots path planning based on potential field method are accomplished and a novel formation controller for mobile robots based on potential field method is proposed. The efficiency of the proposed method is verified through simulation and experimental studies by applying them to control the formation of four e-Pucks robots (low-cost mobile robot platform). Also the proposed method is compared with Simulated Annealing, Improved Harmony Search and Cuckoo Optimization Algorithm methods and the experimental results, higher performance and fast convergence time to the best solution of the ARO demonstrated that this optimization method is appropriate for real time control application. In flocking, a swarm of robots moves cohesively in a common direction. Traditionally, flocking is realized using two main control rules: proximal control, which controls the cohesion of the swarm using local range-and bearing information about neighboring robots; and alignment control, which allows the robots to align in a common direction and uses more elaborate sensing mechanisms to obtain the orientation of neighboring robots. So far, limited attention has been given to motion control, used to translate the output of these two control rules into robot motion. In this paper, we propose a novel motion control method: magnitude-dependent motion control (MDMC). Through simulations and real robot experiments, we show that, with MDMC, flocking in a random direction is possible without the need for alignment control and for robots having a preferred direction of travel. MDMC has the advantage to be implementable on very simple robots that lack the capability to detect the orientation of their neighbors. In addition, we introduce a small proportion of robots informed about a desired direction of travel. We compare MDMC with a motion control method used in previous robotics literature, which we call magnitude-independent motion control (MIMC), and we show that the swarms can travel longer distances in the desired direction when using MDMC instead of MIMC. Finally, we systematically study flocking under various conditions: with or without alignment control, with or without informed robots, with MDMC or with MIMC. This paper proposes evolutionary wall-following control of a mobile robot using an interval type-2 fuzzy controller (IT2FC) with species-differential-evolution-activated continuous ant colony optimization (SDE-CACO). Both the position and speed of a mobile robot are controlled by using two IT2FCs to improve noise resistance ability. A new cost function is defined to accurately evaluate the wall-following performance of an evolutionary IT2FC. A two-stage training approach is proposed that learns a position IT2FC followed by a speed IT2FC to optimize both the wall-following accuracy and the moving speed. The proposed learning approach avoids the time consuming task of the exhaustive collection of supervised input-output training pairs. All fuzzy rules are generated online using a clustering-based approach during the evolutionary learning process. All of the free parameters in an online-generated IT2FC are optimized using SDE-CACO, in which an SDE mutation operation is incorporated within a continuous ACO to improve its explorative ability. The proposed SDE-CACO is compared with various population-based optimization algorithms to demonstrate its efficiency and effectiveness in the wall-following control problem. This study also includes experiments that demonstrate wall-following control utilizing a real mobile robot. The study investigates the leader-follower formation control problem, for which the objective is to control a group of robots such that they move as a rigid formation with a prescribed constant velocity. It is assumed in the study that there are two leader robots, who are the only robots in the group that are informed about the prescribed velocity. All the other robots are followers and do not have the reference velocity information. The authors take the robotic formation as coupled triangular sub-formations and develop adaptive control strategies to enable each follower robot to attain and maintain a stable triangular formation with respect to its two leading neighbours. As a result, the whole group forms a rigid formation. Analyses on convergence and stability properties of equilibrium formations are provided, which show that the desired formation is asymptotically stable. Finally, simulations are given to illustrate our results.