Self-reconfigurable robots: an introduction

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.

Self-reconfigurable robots are built from robotic modules typically organised in a lattice. The robotic modules themselves are complete, although simple, robots and have onboard batteries, actuators, sensors, processing power, and communication capabilities. The modules can automatically connect to and disconnect from neighbour modules and move around in the lattice of modules. The self-reconfigurable robot as a whole can, through this automatic rearrangement of modules, change its own shape to adapt to the environment or as a response to new tasks. Potential advantages of self-reconfigurable robots are extreme versatility and robustness. The organisation of self-reconfigurable robots in a lattice structure and the emphasis on local communication between modules mean that lattice automata are a useful basis for control of self-reconfigurable robots. However, there are significant differences which arise mainly from the physical nature of self-reconfigurable robots as opposed to the virtual nature of lattice automata. The problems resulting from these differences are mutual exclusion, handling motion constraints of modules, and unrealistic assumption about global, spatial orientation. Despite these problems the self-reconfigurable robot community has successfully applied lattice automata to simple control problems. However, for more complex problems hybrid solutions based on lattice automata and distributed algorithms are used. Hence, lattice automata have shown to have potential for the control of self-reconfigurable robots, but still a unifying implementation based on lattice automata solving a complex control problem running on physical self-reconfigurable robot is yet to be demonstrated.

Transferring motion instructions from a user enables robots to perform new and unforeseen operations. Robot collectives, in particular, offer greater adaptability to changing tasks and environments. However, effectively transferring motion instructions becomes challenging as the collective's shape and size evolve. These changes often require additional system constraints to maintain robust motion control, which typically depends on pre-programmed knowledge of new tasks, ultimately limiting the collective's adaptability. To overcome the above challenges, we propose a physical and computational platform for user-guided control of self-reconfigurable modular robots. This platform consists of an optimization scheme for online processing of user commands, which prevents any modular robot actions that would violate system or environment constraints. The second component consists of Joint-space Joysticks, which match the robot's morphology, enabling the user to control diverse and dynamically changing modular robot structures through direct physical interaction. We present a platform that enables users to safely control modular, shape-changing robots through a physical interface. We demonstrate the platform's efficacy and generalizability across a diverse set of modular robot morphologies using two independent robotic systems - Mori3 and Roombots - performing a range of tasks including pick-and-place, human assistance, legged locomotion, and workspace expansion.

Mostly in natural disaster situations, for example an earthquake, we will face a situation where people get trapped and unable to be found as soon as possible due to the unpredictable terrain. Hence, robot is being seen as scouts that able to adapt and perceive, and usually snake type robot is able to move throughout rubble and small confines. Then, the idea of using modular self-reconfigurable (MSR) robots based on CEBOT in 1988 spark an interest to researchers to fit the search and rescue purpose. Besides that, the idea of controlling robots without manual control but by using “thinking” have fascinated researchers and lead to the development of interfaces between human and machines. In 1970, a Brain Computer Interface (BCI) system has been establish and the BCI system usually is based on the Electroencephalogram (EEG) which is the electrical potentials generated in which the information generated by user and independent of any muscular activities. The implementations of both technologies to each other will frontier for a new alternatives which improves self-reconfigurable modular robot in term of control strategy. As a preliminary framework, a simulation is needed to determine both systems capabilities, and it can be achieved by interfacing the robot simulator, OpenVIBE and Virtual Experimentation Platform (V-REP) with the Python programming language. This study presentation will focus on how to control the modular robot in V-REP environment by using Python language which can be used for interfacing with BCI system created in OpenVIBE software and the simulated robot configuration. Each joint handles of modular robot based on V-REP hierarchy being streamed by Python language. There are several methods for establishment of communication between OpenVIBE and other applications. The method purpose in this paper will utilizing Lab Streaming Layer (LSL) and Python script as mediator. The outcome that is expected to be obtained for this study will be the success of communication establish to control virtual modular robot based on data from OpenVIBE. The future work based on this study will involving the BCI system designed using OpenVIBE and implementation to real modular robot might be applied.

Modular self-reconfigurable robots (MSRs) are mostly used in environments where it is difficult to navigate and explore otherwise. Especially, the shape-changing ability of MSRs makes them more dexterous in these situations compared to fixed-body robots. But when the MSR forms a new configuration, usually, the locomotion pattern for that particular configuration is not known by its constituting robotic modules. The main challenge for modules is to learn how to move in that specific configuration within a reasonable amount of time. In this paper, we study the problem where an MSR needs to learn its movement pattern on-the-fly. To solve this problem, we have proposed a game theoretic solution based on multi-agent reinforcement learning using which the constituting modules distributedly learn the best actions that they need to perform to travel more distance in less time. We have implemented this approach in simulation on both ModRED and Yamor MSR platforms. Results show that our approach performs better (up to 7.86 times) in terms of average speed achieved for most of the tested configurations as compared to an existing locomotion learning approach.

This paper presents the design and implementation of a novel modular snake robot for rough terrain. The proposed snake robot is composed of several identical modules that are assembled together. It is more flexible and robust in harsh environments than traditional wheel and track based mobile robots. A cosine model is proposed to control the locomotion of the snake robot. The modular snake robot is able to creep at different speeds and in different directions by modulating the parameters of the cosine model. Its basic locomotion performance has been verified in experiments.

This paper presents a novel 6-URS self-reconfigurable all-dimensional walking robot. The model of the 6-URS self-reconfigurable walking robot is established, then its reconfiguration modes are proposed. Based on the screw theory, the degrees of freedom (DOF) of this robot before and after reconfiguration are analysed separately to verify the feasibility of self-reconfiguration. This robot can achieve self-reconfigurable function with the design of self-reconfigurable limbs. Moreover, the trajectory planning and simulation of three kinds of gaits are performed, including walking horizontally, turning and climbing. The above analysis shows that the 6-URS self-reconfigurable all-dimensional robot satisfies the walking requirements and has the reconfigurable ability in unstrcutured environment.

As for military reconnaissance, disaster relief applications such as stairs, stair type of obstacle, the paper propose the use of chain -type self-reconfigurable robots, overally plan the function modules of the robot, finish the design and making of the robot's mechanical system and control system and the corresponding performance testing. The test results show that the design is scientific and reasonable and meet the application requirements. Innovation in this article include: enhancing the use of harmonic reducer, realizing a large reduction ratio and high output torque in a smaller space; using a number of highly integrated controller chip in the control system's functional modules to separate the task between scheduling and implementation. Embeding microcontroller is mainly responsible for scheduling management, and specific implementation can be finished by the lower control chip. This can greatly reduce the burden on microcontroller, let the microcontroller to have more time to respond and deal with real-time event, and so that the embeding control system has good performance of real-time, and it is helpful to achieving the intelligent control of the self-reconfigurable robots team.

In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm—MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments. By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm’s capability of generating stepping stones for reaching high-performing solutions.

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.

This paper discusses the experimental testing on the actuation design and motion of one type of novel self-reconfigurable modular robot. The shape of the modular robot is octagonal and hence it is named as Octabot. Each module is equipped with one micro-controller and eight actuators. One each side of the modular robot, the actuator is driven by one set of electromagnet for both moving the module and connecting other modules. Multiple Octabots can be used to easily generate a large- scale robot formation. The magnetic field of the actuator is analyzed first in ANSYS and preliminary experimental tests of multiple Octabots are then performed to demonstrate the reconfigurable operation.

This paper proposes a novel self-reconfigurable modular robot for search and rescue. The modular robot consists of two main structures, a planetary wheel driven robot body and a double unlocking system as the docking structure. By means of the planetary wheel structure, the robot has a strong flexibility to cross obstacles in a disaster environment. In virtue of the docking structure with double unlocking function, the self-reconfigurable ability has improved robot reliability. Further, the modular robot can be connected or disconnected according to the actual task requirements. Finally, to verify the stability and reliability, the finite element analysis is carried out.

Highly redundant modular robots may undergo large shape changes which significantly affect the geometry and dynamics of the robot. In these motions, the shape change may induce a tipping or rolling behavior of the robot. The paper describes the dynamic modeling, locomotion planning, control and simulation of such rolling motions for the Tetrobot modular robots. The motion is described by the path profiles of controlled nodes, the tipping criteria and dynamic tipping motion and an impact-reaction model of contact with the ground. These phases of motion are described using Newton-Euler dynamic equations and the principle of conservation of angular momentum. In the paper, a two-phase planning and switching control sequence is introduced to achieve stable and reliable motion of a Tetrobot modular robot. Simulation results illustrate the tipping behavior of a tetrahedron, the dynamic contact and rolling of an icosahedral Tetrobot and dynamic control of the rolling Tetrobot. The resulting models are useful to analyze and control both intentional rolling as a new mode of mobility as well as the avoidance of unintentional tipping and rolling during task execution.

Existing self-reconfigurable robots achieve connections and disconnections by a separate drive of the docking system. In this paper, we present a new docking system with which the connections and disconnections are driven by locomotion actuators, without the need for a separate drive, which reduces the weight and the complexity of the modules. This self-reconfigurable robot consists of two types of fundamental modules, i.e., active and passive modules. By the docking system, two types of connections are formed with the fundamental modules, and the docking and undocking actions are achieved through simple control with less sensory feedback. This paper describes the design of the robotic modules, the docking system, the docking process, and the docking force analysis. An experiment is performed to demonstrate the self-reconfigurable robot with the docking system.

We have proposed a self-reconfigurable robotic module, which has a very simple structure. The system is capable of not only building a static structure, but also generating a dynamic robotic motion. We have also developed a simulator for the motion planning. In this paper, we present details of the mechanical and electrical designs of the developed module and its control system architecture. Experiments using ten modules demonstrate the robotic configuration change, crawling locomotion and three types of quadruped locomotion.