Modular robotic systems
TLDR
This paper aims to investigate the research areas in MRS algorithms that have been evolved so far and to explore promising research directions for the future by reviewing 64 solution methods and algorithms according to their application in each operation and investigating their capabilities.About:
This article is published in Artificial Intelligence.The article was published on 2015-06-01 and is currently open access. It has received 64 citations till now. The article focuses on the topics: Modular design.read more
Citations
More filters
Journal ArticleDOI
A Framework for Taxonomy and Evaluation of Self-Reconfigurable Robotic Systems
TL;DR: A framework for taxonomy and evaluation (TAEV) of self-reconfigurable robots, based on the mechanism reconfigurability and the level of autonomy for reconfiguration is put forward.
Journal ArticleDOI
A survey of autonomous self-reconfiguration methods for robot-based programmable matter
TL;DR: An extensive survey of the current state of the art of self/reconfiguration algorithms and underlying models in modular robotic and self-organizing particle systems is proposed and three approaches for solving the shape formation problem are identified.
Journal ArticleDOI
Roombots extended: Challenges in the next generation of self-reconfigurable modular robots and their application in adaptive and assistive furniture
Simon Hauser,Mehmet Mutlu,Pierre-Alexandre Leziart,Hala Khodr,Alexandre Bernardino,Auke Jan Ijspeert +5 more
TL;DR: This work describes how Roombots tackled difficulties in real hardware and focuses qualitatively on selected hardware experiments rather than on quantitative measurements to showcase the many possibilities of an SRMR.
Journal ArticleDOI
Learning directed locomotion in modular robots with evolvable morphologies
Gongjin Lan,Gongjin Lan,Matteo De Carlo,Fuda van Diggelen,Jakub M. Tomczak,Diederik M. Roijers,Agoston E. Eiben +6 more
TL;DR: In this paper, the authors present a test suite of robots with different shapes and sizes and compare two learning algorithms, Bayesian optimization and HyperNEAT, for the task of directed locomotion in evolvable modular robots.
Proceedings Article
Distributed Self-Reconfiguration using a Deterministic Autonomous Scaffolding Structure
TL;DR: This paper proposes a method for constructing a parametric scaffolding model that increases the parallelism of the reconfiguration, supports its mechanical stability, and simplifies planning and coordination between agents.
References
More filters
Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.
Journal ArticleDOI
Self-assembly at all scales.
TL;DR: Self-assembling processes are common throughout nature and technology and involve components from the molecular to the planetary scale and many different kinds of interactions.