Scalable multi-radio communication in modular robots

Decentralized control of self-reconfiguring modular robots requires reliable inter-module communication. Communication links must tolerate module misalignment and implement the neighbor-to-neighbor communication model. In this paper, we propose a wireless system based on multiple radios per module that addresses these challenges. Although the capacity of general wireless mesh networks is known to rapidly decrease with network size, we show that a multi-radio single-channel system has constant capacity in square and cubic lattices of infinite size. We validate the performance of such a system in a testbed with 15 unactuated modules using synthetic data and a benchmark decentralized algorithm. We also demonstrate automatic neighbor detection. The main benefits of radio communication in modular robots are tolerance to module misalignment and to eliminate the tight coupling between communication and mechanical design necessitated by typical existing infrared and wired systems. Our results are the first to establish the feasibility of radio as the primary means of inter-module communication in modular robots.     

High-level motion planning for CPG-driven modular robots

Modular robots may become candidates for search and rescue operations or even for future space missions, as they can change their structure to adapt to terrain conditions and to better fulfill a given task. A core problem in such missions is the ability to visit distant places in rough terrain. Traditionally, the motion of modular robots is modeled using locomotion generators that can provide various gaits, e.g. crawling or walking. However, pure locomotion generation cannot ensure that desired places in a complex environment with obstacles will in fact be reached. These cases require several locomotion generators providing motion primitives that are switched using a planning process that takes the obstacles into account. In this paper, we present a novel motion planning method for modular robots equipped with elementary motion primitives. The utilization of primitives significantly reduces the complexity of the motion planning which enables plans to be created for robots of arbitrary shapes. The primitives used here do not need to cope with environmental changes, which can therefore be realized using simple locomotion generators that are scalable, i.e., the primitives can provide motion for robots with many modules. As the motion primitives are realized using locomotion generators, no reconfiguration is required and the proposed approach can thus be used even for modular robots without self-reconfiguration capabilities. The performance of the proposed algorithm has been experimentally verified in various environments, in physical simulations and also in hardware experiments.     

Evolving non-trivial behaviors on real robots: A garbage collecting robot

Recently, a new approach involving a form of simulated evolution has been proposed to build autonomous robots. However, it is still not clear if this approach is adequate for real life problems. In this paper we show how control systems that perform a non-trivial sequence of behaviors can be obtained with this methodology by “canalizing” the evolutionary process in the right direction. In the experiment described in the paper, a mobile robot was successfully trained to keep clear an arena surrounded by walls by locating, recognizing, and grasping “garbage” objects and by taking collected objects outside the arena. The controller of the robot was evolved in simulation and then downloaded and tested on the real robot. We also show that while a given amount of supervision may canalize the evolutionary process in the right direction the addition of unnecessary constraints can delay the evolution of the desired behavior.     

可重构模块机器人分散容错控制

针对可重构模块机器人的执行器故障,提出一种基于自适应模糊系统的分散被动容错控制方法.该方法不需要机器人动力学模型与模块之间的信息交换,模块控制器分别采用间接和直接自适应方法设计,自适应参数的更新律基于Lyapunov稳定性理论设计,保证了系统的稳定性和H∞跟踪性能.数值仿真结果表明了所提出方法的有效性.     

Reconfigurable swarm robots produce self-assembling and self-repairing organisms

Reconfigurable robots are set to become a vital factor in the theoretical development and practical utilization of robotics. The core problem in this scientific area is steady information transfer between a swarm and its organisms and vice versa. To this end, we present a basic theoretical framework that stipulates the interoperation between the two modes. We evaluate our proposed framework by constructing 100 mobile microrobots of three different types that initiate the processes of self-reconfigurability and self-repair. The autonomous decision to self-aggregate to an organism mainly derives from the necessity to overcome existing obstructive environmental conditions, e.g. ramps or clefts. The methodological dichotomy that we have chosen to evaluate our concept was to pursue in parallel an approach based on embodied distributed cognition and an evolutionary path mainly based on artificial genomes and reproduction. In this paper, we evaluate these two different approaches in two distinct grand challenges and present the main results.     

Design of a miniature switchable connection system for stochastic modular robots

This paper presents the design and implementation of a MIniature SwitchAble (MISA) connection system for stochastic modular robots. The MISA connector consists of attaching, aligning, locking, detaching and holding mechanisms. The connection function can be switched on and off by controlling shape memory alloy (SMA) actuators. Furthermore, it possess functions of self-attachment and self-alignment through magnetic interaction under random collision condition. The design details and prototype construction are presented. The preliminary experiments and results demonstrate feasibility and performance of this miniature connector. The potential application is to integrate it into modular microrobots.     

Design of a modular assembly of four-footed robots with multiple functions

Modern industries use many types of robots. In addition to general robotic arms, bipedal, tripedal, and quadrupedal robots, which were originally developed as toys, are gradually being used for multiple applications in manufacturing processes. This research begins with establishing the platform for four-footed robots with multiple functions, high sensitivity, and modular assembly and this is how a fundamental model of the industrial robots is constructed. Under additional loads, the four feet of the quadrupedal robot reinforce its carrying ability and reliability compared to bipedal or tripedal robots, which helps it to carry more objects and enhances functionality. Based on different requirements and demands from the manufacturing processes, the highly sensitive four-footed robot provides an expandable interface to add different sensing components. In addition, when combined with a wireless communication module or independent 1.2 GHz radio frequency CCD wireless image transmission system, the user can control the robot remotely and instantly. The design helps the four-footed robot to expand its applications. By assembling and disassembling modules and changing the sensing components, the highly sensitive four-footed robot can be used for different tasks. Moreover, the remote control function of the robot will increase interaction with human beings, so it can become highly become involved in people's lives. The platform of the four-footed robot will become a design reference for the commercialization of different industrial robots, and it will provide the design of industrial robots with more options and useful applications.     

The design of a representation and analysis method for modular self-reconfigurable robots

For Modular Self-Reconfigurable (MSR) robots to successfully reconfigure themselves, the control system must have the robot's topological structure before, during and after the reconfiguration process. Therefore, the module's docking status such as the docking orientation and the availability of docking ports must be ascertained before self-configuration takes place. This paper presents a representation method, based on labelled planar graphs and incidence matrices to represent the topologic structure of a group of connected modules. Essential information such as the orientations of the docked joints and docking ports are obtained from the labelled graph for reconfiguration. An innovative Hardware Orientation Detecting System (HODS) is proposed for detecting the orientation between two docking ports, which can be applied to all multi-orientation docking systems. An analytical method is also presented to determine whether a structure has enough degrees-of-freedom to perform self-reconfiguration.     

Multiagent control of self-reconfigurable robots

We demonstrate how multiagent systems provide useful control techniques for modular self-reconfigurable (metamorphic) robots. Such robots consist of many modules that can move relative to each other, thereby changing the overall shape of the robot to suit different tasks. Multiagent control is particularly well-suited for tasks involving uncertain and changing environments. We illustrate this approach through simulation experiments of Proteo, a metamorphic robot system currently under development.     

Evolutionary design of Sugeno-type fuzzy systems for modelling humanoid robots

An evolutionary design of Sugeno-type fuzzy systems for modelling humanoid robots is presented in this article, and issues related to the determination of the antecedent and consequent structures of the fuzzy model are addressed. In the design of the fuzzy model, determination of the type, the number of membership functions assigned to the input variables, the types of consequent equations for the fuzzy rules, the optimal number of input variables, and the dominant input variables among the input candidates are carried out using evolutionary algorithms. Using these algorithms, proper structures are evolved for the antecedent and the consequent of the Sugeno-type fuzzy model. Simulations are performed to show the effectiveness of the developed method when applied to a humanoid robot system with strong nonlinearities that have 10 input candidates.     

Multimode locomotion via SuperBot reconfigurable robots

One of the most challenging issues for a self-sustaining robotic system is how to use its limited resources to accomplish a large variety of tasks. The scope of such tasks could include transportation, exploration, construction, inspection, maintenance,in-situ resource utilization, and support for astronauts. This paper proposes a modular and reconfigurable solution for this challenge by allowing a robot to support multiple modes of locomotion and select the appropriate mode for the task at hand. This solution relies on robots that are made of reconfigurable modules. Each locomotion mode consists of a set of characteristics for the environment type, speed, turning-ability, energy-efficiency, and recoverability from failures. This paper demonstrates a solution using the SuperBot robot that combines advantages from M-TRAN, CONRO, ATRON, and other chain-based and lattice-based robots. At the present, a single real SuperBot module can move, turn, sidewind, maneuver, and travel on batteries up to 500 m on carpet in an office environment. In physics-based simulation, SuperBot modules can perform multimodal locomotions such as snake, caterpillar, insect, spider, rolling track, H-walker, etc. It can move at speeds of up to 1.0 m/s on flat terrain using less than 6 W per module, and climb slopes of no less 40 degrees. Harris Chi Ho Chiu is a PhD Student in Computer Science at the University of Southern California and a research assistant in Polymorphic Robotics Laboratory of Information Science Institute. He received his Master in Computer Science from the University of Southern California and his Bachelor of Engineering from the University of Hong Kong. His research interests include intelligent automated systems, modular self-reconfigurable systems, artificial intelligence, and machine learning. Michael Rubenstein is currently a PhD student at the Polymorphic Robotics Laboratory, working on the CONRO and Superbot self-reconfigurable robotic systems. He has received his bachelors in Electrical Engineering from Purdue University, and his masters in Electrical Engineering from the University of Southern California, and is currently working towards his PhD in Computer Science from the University of Southern California. His interests include modular self-reconfigurable systems, autonomous robots, self-healing systems, and self-replicating systems. Jagadesh B Venkatesh is a member of the Polymorphic Robotics Laboratory at the Information Sciences Institute. He is currently a Master’s candidate in the Product Development Engineering program at the University of Southern California. He received his MS in Computer Science with specialization in Intelligent Robotics, also at the University of Southern California in 2005. His current interest is the commercialization of robotic technologies, specifically in the consumer robotics sector.     

Automated modeling of modular robotic configurations

This research presents an automated method to build kinematic and dynamic models for assembling modular components of modular robotic systems. By comparison with other approaches, the proposed method is applicable to any robotic configuration with serial, parallel, or hybrid structures. In addition, it is object oriented so that each modular component is an element with a submodel and the overall model can be assembled from submodels subject to the connection constraints.     

Evolutionary algorithms and de novo peptide design

One of the goals of computational chemistry is the automated de novo design of bioactive molecules. Despite significant progress in computational approaches to ligand design and efficient evaluation of binding energy, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design issue. This paper presents an automated methodology for computer-aided peptide design based on evolutionary algorithms. It provides an automatic tool for peptide de novo design, based on protein surface patches defined by user. Regarding the restrictive constrains of this problem a special emphasis has been made on the design of the evolutionary algorithms implemented.     

An engineering approach to sociable robots

Robotics researchers and cognitive scientists are becoming more and more interested in so-called sociable robots. These machines normally have expressive power (facial features, voice,?…) as well as abilities for locating, paying attention to, and addressing people. The design objective is to make robots which are able to sustain natural interactions with people. This capacity falls within the range classed as social intelligence in humans. This position paper argues that the reproduction of social intelligence, as opposed to other types of human ability, may lead to fragile performance, in the sense that tested cases may produce rather different performances to future (untested) cases and situations. This limitation stems from the fact that our social abilities, which appear early in life, are mainly unconscious in origin. This is in contrast with other human abilities that we carry out using conscious effort, and for which we can easily conceive algorithms and representations. This novel perspective is deemed useful for defining the obstacles and limitations of a field that is generating increasing interest. Taking into account the mentioned issues, a development approach suited to the problem is proposed. The use of this approach is demonstrated in the development of CASIMIRO, a robotic head with basic interaction abilities.     

Graph-based optimal reconfiguration planning for self-reconfigurable robots

The goal of optimal reconfiguration planning (ORP) is to find a shortest reconfiguration sequence to transform a modular and reconfigurable robot from an arbitrary configuration into another. This paper investigates this challenging problem for chain-type robots based on graph representations and presents a series of theoretical results: (1) a formal proof that this is an NP-complete problem, (2) a reconfiguration planning algorithm called MDCOP which generates the optimal graph-based reconfiguration plan, and (3) another algorithm called GreedyCM which can find a near-optimal solution in polynomial time. Experimental and statistical results demonstrate that the solutions found by GreedyCM are indeed near-optimal and the approach is computationally feasible for large-scale robots.     

ModRED: Hardware design and reconfiguration planning for a high dexterity modular self-reconfigurable robot for extra-terrestrial exploration

This paper presents a homogeneous modular robot system design based on four per-module degrees of freedom (DOF), including a prismatic DOF to increase the versatility of its reconfiguration and locomotion capabilities. The ModRED (Modular Robot for Exploration and Discovery) modules are developed with rotary-plate genderless single sided docking mechanisms (RoGenSiD) that allow chain-type configurations and lead towards hybrid-type configurations. Various locomotion gaits are simulated through the Webots robot simulator and implemented in the real ModRED system. This work also addresses the problem of dynamic reconfiguration in a modular self-reconfigurable robot (MSR). The self-reconfiguration problem is modeled as an instance of the graph-based coalition formation problem. We formulate the problem as a linear program that finds the “best” partition or coalition structure among a set of ModRED modules. The technique is verified experimentally for a variety of settings on an accurately simulated model of the ModRED robot within the Webots robot simulator. Our experimental results show that our technique can find the best partition with a reasonably low computational overhead.     

A genetic algorithm-based solution methodology for modular design

Combinatorial optimization problems usually have a finite number of feasible solutions. However, the process of solving these types of problems can be a very long and tedious task. Moreover, the cost and time for getting accurate and acceptable results is usually quite large. As the complexity and size of these problems grow, the current methods for solving problems such as the scheduling problem or the classification problem have become obsolete, and the need for an efficient method that will ensure good solutions for these complicated problems has increased. This paper presents a genetic algorithm (GA)-based method used in the solution of a set of combinatorial optimization problems. A definition of a combinatorial optimization problem is first given. The definition is followed by an introduction to genetic algorithms and an explanation of their role in solving combinatorial optimization problems such as the traveling salesman problem. A heuristic GA is then developed and used as a tool for solving various combinatorial optimization problems such as the modular design problem. A modularity case study is used to test and measure the performance of the developed algorithm.     

异质多移动机器人协同技术研究的进展

随着移动机器人应用的领域和范围的不断扩展，多移动机器人由于其单个机器人无法比拟的优越性已经越来越受到重视．从体系结构、协作与协调、协作环境感知与定位、重构及机器学习几个重要课题对多移动机器人协同技术进行了综述，尤其侧重于各种技术如何处理和包容团队中的异质性，并分析了本领域中的研究难点问题，最后展望了异质多移动机器人研究的前景与发展趋势．     

Evolving gaits for hexapod robots using cyclic genetic algorithms

A major facet of multi-legged robot control is locomotion. Each leg must move in such a manner that it efficiently produces thrust and provides maximum support. The motion of all the legs must be coordinated so that they are working together to provide constant stability while propelling the robot forward. In this paper, we discuss the use of a cyclic genetic algorithm (CGA) to evolve control programs that produce gaits for actual hexapod robots. Tests done in simulation and verified on the actual robot show that the CGA successfully produces gaits for both fully capable and disabled robots.