Akademik Veri Yönetim Sistemi
ROBOTICS AND AUTONOMOUS SYSTEMS, cilt.188, 2025 (SCI-Expanded)
In this study, a new path-planning algorithm named "Jellyfish Pump Algorithm (JPA)" for the selfreconfiguration of lattice-based self-reconfigurable modular robots (SRMRs) is presented. The JPA is inspired by the shape changing behavior of a jellyfish during its motion. This motion always satisfies the structural balance of the jellyfish with the help of adaptable and periodic shape changing actions. The proposed approach tries to confirm a physically balanced transformation process of the SRMRs considering external effects such as the gravity. The aim is to conserve the balance by employing a static plus shaped core structure of the robot body during the self-reconfiguration. The mobile modules are allowed to move around this core structure between initial and final configurations. The pivoting cube model is used as the abstraction method of the introduced algorithm. The comparison between pivoting and sliding cube models is presented considering actual world implementation aspects of SRMRs. The JPA is developed as a modification to the well-known self-reconfiguration algorithm of Melt Sort Grow. The JPA allows the robot to reach the final configuration by melting the initial configuration into a balanced intermediate phase having a plus shaped structure instead of a line configuration. The physical balance of the robot is satisfied at each step of the self-reconfiguration process. Appropriate simulations using generic 3D initial configurations have validated the proposed algorithm. Extreme cases such as locomotion and bridge formation are tested with the proposed algorithm considering the robustness and applicability. The time complexity of the JPA is O( n2) for n modules, whereas the balance restrictions enforce the algorithm to generate number of moves less than the square of number of mobile modules. The proposed algorithm was compared with a validated Melt Sort Grow algorithm considering number of moves and time complexity, and the efficiency of the algorithm was verified.