Abstract
Inspired by the morphogenesis of biological organisms, gene regulatory network-based methods have been used in complex pattern formation of swarm robotic systems. In this article, obstacle information was embedded into the gene regulatory network model to make the robots trap targets with an expected pattern while avoiding obstacles in a distributed manner. Based on the modified gene regulatory network model, an implicit function method was adopted to represent the expected pattern which is easily adjusted by adding extra feature points. Considering environmental constraints (e.g. tunnels or gaps in which robots must adjust their pattern to conduct trapping task), a pattern adaptation strategy was proposed for the pattern modeler to adaptively adjust the expected pattern. Also to trap multiple targets, a splitting pattern adaptation strategy was proposed for diffusively moving targets so that the robots can trap each target separately with split sub-patterns. The proposed model and strategies were verified through a set of simulation with complex environmental constraints and non-consensus movements of targets.
Highlights
In the field of robotics, natural species or life science phenomena inspire researchers who work with bionic robots[1] and robotic systems.[2]
Under the framework of hierarchical gene regulatory network (GRN), we introduce three inputs to the upper level to generate expected patterns, and such patterns are formatted by a swarm of robots via GRNbased controller
To model the expected pattern sðgÞ, which can adapt to the environmental constraints, an interpolating implicit function (IIF) is used in this article
Summary
In the field of robotics, natural species or life science phenomena inspire researchers who work with bionic robots[1] and robotic systems.[2]. Jin and Guo[24] proposed a hierarchical GRNs for adaptive morphogenetic pattern formation of a swarm of robots; this algorithm deals with the problem that combines pattern formation with target trapping. This article presents an adaptive pattern formation method for a swarm of robots to trap multiple targets in environments with complex obstacles or constraints. The position information of targets and environmental constraints are used as key points (interior, boundary, and exterior points) of implicit function to adjust the patterns when the trapping task is being carried out in complex environments. Two pattern adaptation strategies have been designed for constrained environments (e.g. tunnels or gaps) and multiple targets escaping in different directions where the original pattern has to be changed With these strategies, the key points of implicit function can be adaptively generated and the expected patterns can be adaptively adjusted. Simulation and analysis are presented in section ‘‘Simulation results and analysis’’ and the conclusions and potential future work are drawn in the last section
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
