Self-organization in aggregating robot swarms: A DW-KNN topological approach

• Published in: BioSystems Vol. 165; pp. 106 - 121
• Main Authors: Khaldi, Belkacem, Harrou, Fouzi, Cherif, Foudil, Sun, Ying
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.03.2018
• Subjects: Distance-Weighted KNN; Self-organized aggregation; Smoothed Particles Hydrodynamics (SPH); Swarm robotics; Virtual viscoelastic model; Virtual viscoelastic model; Smoothed Particles Hydrodynamics (SPH); Self-organized aggregation; Swarm robotics; Distance-Weighted KNN; Self-Organized Aggregation; Virtual Viscoelastic Model; Swarm Robotics
• ISSN: 0303-2647; 1872-8324
• DOI: 10.1016/j.biosystems.2018.01.005

In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbours is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach, showing various self-organized aggregations performed by a swarm of N foot-bot robots. Also, we compared the aggregation quality of DW-KNN aggregation approach to that of the conventional KNN approach and found better performance.