Automatic Couplings With Mechanical Overload Protection for Modular Robots

In this thesis we present the design and implementation of a novel self-reconfiguring modular (SR-MR) robotic system: Roombots. We are aiming at three main applications with Roombots; locomotion through self-reconfiguration in the regular cubic 3D-lattice on structured surfaces, locomotion in non-structured environments applying central pattern generators (CPG) as the locomotion controller, and self-assembly and reconfiguration of static objects of the day-to-day environment, such as furniture. Robot assemblies from self-reconfigurable modular robots have the ability to adapt to a given task and working environment by altering their shape through a series of reconfiguration moves, and attachments and detachments between the modules. We are interested in self-reconfiguring modular robots for their shape-changing capabilities, and their distributed characteristics. We envision the following applications for Roombots: First, self-reconfiguration in a structured 3D lattice, i.e. a floor and walls equipped with connectors. Embedded connectors can provide pivot points for locomotion of SR-MR assemblies, and docking and recharging places for our adaptive furniture pieces. Second, our proposed concept for locomotion control of modular robots on non-structured ground are central pattern generators. Third, we would like to build adaptive and versatile furniture from modular robots and light-weight elements. In the following we are able to count more than 60 modular robotic systems, developed over the last two decades. However we were unable to identify an existing system which could provide us with all desired kinematic and geometric capabilities. This led us to design and implement a novel self-reconfiguring modular robotic system: Roombots. To tackle the module design we attempt to identify both meaningful design parameters from existing modular robots, and essential features for our applications. The combination of both leads to the kinematic and geometric description of the Roombots modules, and eventually to its implementation. In order to be able to assemble furniture from our Roombots units in the future, we need a reconfiguration framework which supports the specific requirements of Roombots. Metamodules made of two units attached in-series are attracted and guided by a virtual force-field, they use broadcast signals, look-up tables of collision clouds and simple assumptions about their near environment to reach their seeding positions, which are currently hand coded. For the task of locomotion in non-structured environments we propose a framework for learning to move with modular robots using central pattern generators and online optimization. The distributed implementation of CPGs offers an ideal substrate for producing locomotion patterns and for online learning, and an optimization framework for fast learning.

This article discusses the advantages of a modular robot that can reassemble itself for different tasks. Modular robots are composed of multiple, linked modules. Although individual modules can move on their own, the greatest advantage of modular systems is their structural reconfigurability. Modules can be combined and assembled to form configurations for specific tasks and then reassembled to suit other tasks. Modular robotic systems are also very well suited for dynamic and unpredictable application areas such as search and rescue operations. Modular robots can be reconfigured to suit various situations. Quite a number of modular robotic system prototypes have been developed and studied in the past, each containing unique geometries and capabilities. In some systems, a module only has one degree of freedom. In order to exhibit practical functionality, multiple interconnected modules are required. Other modular robotic systems use more complicated modules with two or three degrees of freedom. However, in most of these systems, a single module is incapable of certain fundamental locomotive behaviors, such as turning.

This paper presents a design method and experiments for whole-body locomotion by a modular robot. There are two types of locomotion for modular robots: a repeating self-reconfiguration and whole-body motion such as walking or crawling. For whole-body locomotion, designing a control method is more difficult than for ordinary robots because a modular robotic system can form various configurations, each of which has many degrees of freedom. This study proposes a unified framework for automatically designing an efficient locomotion controller suitable for any module configuration. The method utilizes neural oscillators (central pattern generators, CPGs), each of which works as a distributed joint controller of each module, and a genetic algorithm to optimize the CPG network. We verified the method by software simulations and hardware experiments, in which our modular robotic system, named M-TRAN II, performed stable and effective locomotion in various configurations.

A self-repairing approach for the M-Lattice modular robotic system using digital hormone model

A system has been developed to interactively assemble modular and reconfigurable robotic systems in a computer animation environment. The modular robot is based on a set of one, two and three degree of freedom joint modules and generic links. These modules may be assembled into a large class of robotic systems that include serial, parallel, mobile and hybrid configurations. A model of the configured robotic system is immediately available for use in a wide variety of robotics research areas, including: obstacle avoidance, redundant inverse kinematics, dynamic simulations, mobile platforms and world model databases.

A modular robot has a distributed mechanical composition which can make various configurations and also make locomotion in a wide variety of configurations. Modular robots are thought to be useful in extreme or unknown environments by adaptively changing their shape and locomotion patterns. As for locomotion, two types can be used; one is whole-body fixed-configuration locomotion and the other is locomotion by self-reconfiguration. In this paper we deal with the former type of locomotion which is realized by coordinated joint actuation. So far, proposed control methods for whole-body locomotion by modular robots have been based on predefined locomotion sequences. However, locomotion based on predefined sequences cannot adapt to changing terrain conditions such as uphill, downhill, slippery and sticky grounds. To solve such problems, we propose a distributed control mechanism using a CPG controller which enables adaptive locomotion by modular robots. Besides the real-time CPG control we introduce a decentralized control mechanism for detecting the situation that the robot is stuck and initiating transformation to another shape for recovering the situation. The results of various hardware experiments by 4-legged structure prove the feasibility of the method for adaptive locomotion and transformation by our M-TRAN II modules.

In comparison with dedicated robot systems, the main goal of modular robot systems is to achieve system adaptability by providing various modular configuration variations to meet possible task requirements. System architecture determines the system configuration variations, as the architecture specifies primary building blocks and the types of ways they are connected. In order to make systems more adaptive, configuration variations are expected to be as many as possible subject to the constraints of machining manufacturing applications. This paper reported briefly our recent research progresses on modular robot system architecture, including: (1) a proposed strategy to achieve high system adaptability; (2) a systematic method to develop modular system architecture; (3) the general representation of modular robot configurations.

Operations in extreme and hostile environments, such as offshore oil and gas production, nuclear decommissioning, nuclear facility maintenance, deep mining, space exploration, and subsea applications, require the execution of sophisticated tasks. In nuclear environments, robotic systems have advanced significantly over the past years but still suffer from task failures caused by informational and physical uncertainty of the highly unstructured nature of the environment and exasperated by the time constraints imposed by high radiation levels. Herein, a survey is presented of current robotic systems that can operate in such extreme environments and offer a novel approach to solving the challenges they impose, encapsulated by the mission statement of providing structure in unstructured environments and exemplified by a new self‐assembling modular robotic system, the Connect‐R.

Modular robot systems towards the execution of cooperative tasks in large facilities

Modular robotic systems have been investigated to change the appearance and functions by controlling robotic modules. These reconfigurable modular systems are developed for the interactions between the external environment including humans. Capabilities of compliant interactions are as helpful as those of self-reconfiguration for the modular systems in adapting to the environments. This paper presents a novel concept of a modular robot that has the abilities of compliant actuation and self-reconfiguration to form compliant structures. The authors develop the initial prototype of the modular robots, named Module-W (M-W). Empirical results with the prototype suggest that M-W has the potential to form compliant structures.

Block Type Modular Robot: Mom's Friend

This paper presents two bio-inspired algorithms for coalition formation of multiple modular robot systems. An effective and efficient coalition formation system can help modular robot system take full advantage of reconfigurability of modular robots. In this paper, the multirobot coalition formation problem is illustrated and a mathematical model for the problem is described. Two bio-inspired algorithms, ant-colony algorithm (ACA) and genetic algorithm (GA), are introduced for solving the mathematical model. With the two algorithms, it is able to form a large number of robots into many different groups for a variety of applications, such as parallel performance of multiple tasks by multiple teams of robots. The paper compares the efficiency and effectiveness of two algorithms for solving the presented problem with case study. The results for the comparison study are analyzed and discussed. Also, the implementation details of the simulation and experiment using ACA are presented in the paper.

The advantages of modular robot systems stem from their ability to change between different configurations, enabling them to adapt to complex and dynamic real-world environments. Then, how to perform the accurate and efficient change of the modular robot system, i.e., the self-reconfiguration problem, is essential. Existing reconfiguration algorithms are based on discrete motion primitives and are suitable for lattice-type modular robots. The modules of freeform modular robots are connected without alignment, and the motion space is continuous. It renders existing reconfiguration methods infeasible. In this paper, we design a parallel distributed self-reconfiguration algorithm for freeform modular robots based on multi-agent reinforcement learning to realize the automatic design of conflict-free reconfiguration controllers in continuous action spaces. To avoid conflicts, we incorporate a collaborative mechanism into reinforcement learning. Furthermore, we design the distributed termination criteria to achieve timely termination in the presence of limited communication and local observability. When compared to the baselines, simulations show that the proposed method improves efficiency and congruence, and module movement demonstrates altruism.

The last few years have witnessed an increasing interest in modular reconfigurable robotics for education, inspired robotic research, and space applications. This paper presents the latest results of the Cube-M modular project. Firstly, an overview of the research achievements in modular robot is given. Then the new modular robot Cube-M with one degree of freedom, an improved version of the Y1 modular robot, is presented. After that, the discussion focuses on the realization of a distributed hardware and control. Each module has embedded intelligent capabilities with an independent onboard controller to enable the realization of completely modular design. In the end, a snake-like robot prototype is built to confirm the feasibility of our design principle.

Nowadays, robotic technology finds its way quickly across industries affecting business and people lives. In addition, the rapid growth in communication technologies leads to a new generation of robotics called as Modular Robotic System (MRS). Basically, MRS is autonomous kinematic machine with variable morphology, structure, and functionality. The detectors represent the eyes of the MRS that collect data about different environments and states, while the controller forms the brain of the MRS that must analyze the collected data and take decision about the suitable reconfiguration morphology. However, the huge number of data sensed by the detectors provides two main challenges for MRS; first, it overloads the limited storage of the MRS and, second, it complicates the self-reconfiguration decision required to change its shape according to the monitored environment. In this paper, we propose a sensing-based processing mechanism for data storage and decision making in modular robotic systems. Our mechanism consists of two phases: data storage reduction and self-reconfiguration. The first phase uses aggregation process in order to eliminate redundant data collected thus, reduce the amount of data need to be stored in MRS. The second phase uses the fuzzy logic model and allows MRS to be self-reconfigurable by taking the right decision about the desired shape. The efficiency of our mechanism is demonstrated based on real data simulation while important results have been obtained in terms of data storage and self-reconfiguration decision.