SUPERBOT: A Deployable, Multi-Functional, and Modular Self-Reconfigurable Robotic System

Abstract – Self-reconfigurable robots are modular robots that can autonomously change their shape and size to meet specific operational demands. Recently, there has been a great interest in using self-reconfigurable robots in applications such as reconnaissance, rescue missions, and space applications. Designing and controlling self-reconfigurable robots is a difficult task. Hence, the research has primarily been focused on developing systems that can function in a controlled environment. This paper presents a novel self-reconfigurable robotic system called SuperBot, which addresses the challenges of building and controlling deployable self-reconfigurable robots. Six prototype modules have been built and preliminary experimental results demonstrate that SuperBot is a flexible and powerful system that can be used in challenging realworld applications

Modular Self-Reconfigurable Robot Systems

The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing fiel

EXPERIMAENTAL EVALUATION OF A DISTRIBUTED CONTROL SYSTEM FOR CHAIN-TYPE SELF-RECONFIGURABLE ROBOTS

I would like to thank Professor Wei-Min Shen for advising and supporting me through the course of my research. I am thankful to him for giving me the opportunity to enjoy and appreciate science, and to become a scientist. It was both an honor and a pleasure to be his student and work with him. I would also like to thank the other members of my defense committee Professors George Bekey, Behrokh Khoshnevis, and Peter Will for their insightful advice. Professor Will was also the principle investigator of the CONRO project that I worked on and I thank him for his encouragement and support. I also thank the other two members of my dissertation committee Professors Maja Mataric and Gaurav Sukhatme. Special thanks go to all professors, graduate students, and project assistants at Information Sciences Institute for creating such an exciting research environment. It was a great privilege to be a member of this community. I’d like to thank my parents for their endless love and encouragement and I am thankful to my wife, Atousa, for her love and support and being my partner in this journey. i

Distributed Behavior Collaboration for SelfReconfigurable Robots

Abstract — This paper describes a distributed and decentralized approach for modules in a self-reconfigurable robot to select appropriate behaviors based on four factors: the current global task, the local topological location in the current configuration, the local state/sensor information, and the received messages from their neighbors. This approach does not assume any unique global identifiers for the modules, and is robust for reconfigurations of modules. The approach is enabled by the extended neighbor topology built upon a previous local topology representation and a hormone-inspired communication and control protocols. Experimental results on the CONRO robot have shown some unique features of this approach for the control of self-reconfigurable robots in general. Keywords- Self-reconfigurable robots,distributed control, behavior selection, distributed collaboration

Hormone-inspired adaptive communication and distributed control for conro self-reconfigurable robots

Abstract—This paper presents a biologically inspired approach to two basic problems in modular self-reconfigurable robots: adaptive communication in self-reconfigurable and dynamic networks, and collaboration between the physically coupled modules to accomplish global effects such as locomotion and reconfiguration. Inspired by the biological concept of hormone, the paper develops the Adaptive Communication (AC) protocol that enables modules continuously to discover changes in their local topology, and the Adaptive Distributed Control (ADC) protocol that allows modules to use hormone-like messages in collaborating their actions to accomplish locomotion and selfreconfiguration. These protocols are implemented and evaluated, and experiments in the CONRO self-reconfigurable robot and in a Newtonian simulation environment have shown that the protocols are robust and scaleable when configurations change dynamically and unexpectedly, and they can support online reconfiguration, module-level behavior shifting, and locomotion. The paper also discusses the implication of the hormone-inspired approach for distributed multiple robots and self-reconfigurable systems in general. Index Terms — Self-reconfigurable robots, self-reconfigurable systems, adaptive communication, dynamic networks, distribute

Autonomous Discovery and Functional Response to Topology Change in Self-Reconfigurable Robots

Abstract — The topology of a self-reconfigurable Robot can change at anytime. This can be as a result of the failure of some modules of the robot, joining new modules to the robot, displacement of some modules from one location to another caused by the self-reconfiguration task or any combination of these cases. Considering that the process of selecting relevant behaviors to accomplish a given task is based on the current topology of the self-reconfigurable robot, modules must be able to detect and respond to any changes to the robot topology. When changes to the topology of the robot are detected, modules can investigate new ways of accomplishing the given task. This paper presents a distributed solution, FEATURE algorithm, to the problem of autonomous discovery and functional response to topology change. The result is experimentally verified and demonstrated on the CONRO self-reconfigurable robots