Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation

• Published in: IEEE access Vol. 7; pp. 96372 - 96383
• Main Authors: Khaldi, Belkacem, Harrou, Fouzi, Cherif, Foudil, Sun, Ying
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k -NN; Agglomeration; Computational modeling; distance metrics; Finite element method; Measurement; partially faulty robots; Robot kinematics; Robot sensing systems; Robotics; Robots; self-organized aggregation; Swarm robotics; Task complexity; Trigonometric functions; virtual viscoelastic mesh
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2930677

Aggregation is a vital behavior when performing complex tasks in most of the swarm systems, such as swarm robotics systems. In this paper, three new aggregation methods, namely the distance-angular, the distance-cosine, and the distance-Minkowski <inline-formula> <tex-math notation="LaTeX">\mathit {k} </tex-math></inline-formula>-nearest neighbor (<inline-formula> <tex-math notation="LaTeX">\mathit {k} </tex-math></inline-formula>-NN) have been introduced. These aggregation methods are mainly built on well-known metrics: the cosine, angular, and Minkowski distance functions, which are used here to compute distances among robots' neighbors. Relying on these methods, each robot identifies its <inline-formula> <tex-math notation="LaTeX">\mathit {k} </tex-math></inline-formula>-nearest neighborhood set that will interact with. Then, in order to achieve the aggregation, the interactions sensing capabilities among the set members are modeled using a virtual viscoelastic mesh. Analysis of the results obtained from the ARGoS simulator shows a significant improvement in the swarm aggregation performance compared to the conventional distance-weighted <inline-formula> <tex-math notation="LaTeX">\mathit {k} </tex-math></inline-formula>-NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors.