Evolving hierarchical gene regulatory networks for morphogenetic pattern formation of swarm robots

Morphogenesis, the biological developmental process of multicellular organisms, is a robust self-organising mechanism for pattern formation governed by gene regulatory networks (GRNs). Recent findings suggest that GRNs often show the use of frequently recurring patterns termed network motifs. Inspired by these biological studies, this paper proposes a morphogenetic approach to pattern formation for swarm robots to entrap targets based on an evolving hierarchical gene regulatory network (EH-GRN). The proposed EH-GRN consists of two layers: The upper layer is for adaptive pattern generation where the GRN model is evolved by basic network motifs, and the lower layer is responsible for driving robots to the target pattern generated by the upper layer. Obstacle information is introduced as one of environmental inputs along with that of targets in order to generate patterns adaptive to unknown environmental changes. Besides, splitting or merging of multiple patterns resulting from target movement is addressed by the inherent feature of the upper layer and the k-means clustering algorithm. Numerical simulations have been performed for scenarios containing static/moving targets and obstacles to validate the effectiveness and benefit of the proposed approach for complex shape generation in dynamic environments

Morphogen diffusion algorithms for tracking and herding using a swarm of kilobots

© 2016 Springer-Verlag Berlin Heidelberg This paper investigates self-organised collective formation control using swarm robots. In particular, we focus on collective tracking and herding using a large number of very simple robots. To this end, we choose kilobots as our swarm robot test bed due to its low cost and attractive operational scalability. Note, however, that kilobots have extremely limited locomotion, sensing and communication capabilities. To handle these limitations, a number of new control algorithms based on morphogen diffusion and network connectivity preservation have been suggested for collective object tracking and herding. Numerical simulations of large-scale swarm systems as well as preliminary physical experiments with a relatively small number of kilobots have been performed to verify the effectiveness of the proposed algorithms

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

Devobot: From Biological Morphogenesis to Morphogenetic Swarm Robotics

Complex systems are composed of a large number of relatively simple entities interacting with each other and their environment. From those entities and interactions emerge new and often unpredictable collective structures. Complex systems are widely present in nature, from cells and living organisms to human societies. A major biological process behind this emergence in natural complex systems is morphogenesis, which refers mainly, although not exclusively, to shape development in multicellular organisms. Inspired by morphogenesis, the field of Morphogenetic Engineering (ME) aims to design a system’s global architecture and behaviour in a bottom-up fashion from the self-organisation of a myriad of small components. In particular, Morphogenetic Robotics (MR) strives to apply ME to Swarm Robotics in order to create robot collectives exhibiting morphogenetic properties. While most MR works focus on small and cheap hardware, such as Kilobots, only few or them investigate swarms of mobile and more “intelligent” robot models. In this thesis, we present two original works involving higher-end MR swarms based on the PsiSwarm platform, a two-wheeled saucer-size robot running the Mbed operating system. First, we describe a novel distributed algorithm capable of growing a densely packed “multi-robot organism” out of a group of 40 PsiSwarms, based on ME principles. Then, in another study closer to Modular Robotics (MoR), and taking inspiration from “programmable network growth”, we demonstrate the self-organisation of (virtual) branched structures among a flock of robots. Both works use MORSE, a realistic simulation tool, while a path toward crossing the “reality gap” is shown by preliminary experiments conducted using real hardware