Evolutionary Modular Robotics: Survey and Analysis

This paper surveys various applications of artificial evolution in the field of modular robots. Evolutionary robotics aims to design autonomous adaptive robots automatically that can evolve to accomplish a specific task while adapting to environmental changes. A number of studies have demonstrated the feasibility of evolutionary algorithms for generating robotic control and morphology. However, a huge challenge faced was how to manufacture these robots. Therefore, modular robots were employed to simplify robotic evolution and their implementation in real hardware. Consequently, more research work has emerged on using evolutionary computation to design modular robots rather than using traditional hand design approaches in order to avoid cognition bias. These techniques have the potential of developing adaptive robots that can achieve tasks not fully understood by human designers. Furthermore, evolutionary algorithms were studied to generate global modular robotic behaviors including; self-assembly, self-reconfiguration, self-repair, and self-reproduction. These characteristics allow modular robots to explore unstructured and hazardous environments. In order to accomplish the aforementioned evolutionary modular robotic promises, this paper reviews current research on evolutionary robotics and modular robots. The motivation behind this work is to identify the most promising methods that can lead to developing autonomous adaptive robotic systems that require the minimum task related knowledge on the designer side.https://doi.org/10.1007/s10846-018-0902-

A new meta-module for efficient reconfiguration of hinged-units modular robots

We present a robust and compact meta-module for edge-hinged modular robot units such as M-TRAN, SuperBot, SMORES, UBot, PolyBot and CKBot, as well as for central-point-hinged ones such as Molecubes and Roombots. Thanks to the rotational degrees of freedom of these units, the novel meta-module is able to expand and contract, as to double/halve its length in each dimension. Moreover, for a large class of edge-hinged robots the proposed meta-module also performs the scrunch/relax and transfer operations required by any tunneling-based reconfiguration strategy, such as those designed for Crystalline and Telecube robots. These results make it possible to apply efficient geometric reconfiguration algorithms to this type of robots. We prove the size of this new meta-module to be optimal. Its robustness and performance substantially improve over previous results.Peer ReviewedPostprint (author's final draft

Autonomous Task-Based Evolutionary Design of Modular Robots

In an attempt to solve the problem of finding a set of multiple unique modular robotic designs that can be constructed using a given repertoire of modules to perform a specific task, a novel synthesis framework is introduced based on design optimization concepts and evolutionary algorithms to search for the optimal design. Designing modular robotic systems faces two main challenges: the lack of basic rules of thumb and design bias introduced by human designers. The space of possible designs cannot be easily grasped by human designers especially for new tasks or tasks that are not fully understood by designers. Therefore, evolutionary computation is employed to design modular robots autonomously. Evolutionary algorithms can efficiently handle problems with discrete search spaces and solutions of variable sizes as these algorithms offer feasible robustness to local minima in the search space; and they can be parallelized easily to reducing system runtime. Moreover, they do not have to make assumptions about the solution form. This dissertation proposes a novel autonomous system for task-based modular robotic design based on evolutionary algorithms to search for the optimal design. The introduced system offers a flexible synthesis algorithm that can accommodate to different task-based design needs and can be applied to different modular shapes to produce homogenous modular robots. The proposed system uses a new representation for modular robotic assembly configuration based on graph theory and Assembly Incidence Matrix (AIM), in order to enable efficient and extendible task-based design of modular robots that can take input modules of different geometries and Degrees Of Freedom (DOFs). Robotic simulation is a powerful tool for saving time and money when designing robots as it provides an accurate method of assessing robotic adequacy to accomplish a specific task. Furthermore, it is difficult to predict robotic performance without simulation. Thus, simulation is used in this research to evaluate the robotic designs by measuring the fitness of the evolved robots, while incorporating the environmental features and robotic hardware constraints. Results are illustrated for a number of benchmark problems. The results presented a significant advance in robotic design automation state of the art

Modular Self-Reconfigurable Robotic Systems: A Survey on Hardware Architectures

Modular self-reconfigurable robots present wide and unique solutions for growing demands in the domains of space exploration, automation, consumer products, and so forth. The higher utilization factor and self-healing capabilities are most demanded traits in robotics for real world applications and modular robotics offer better solutions in these perspectives in relation to traditional robotics. The researchers in robotics domain identified various applications and prototyped numerous robotic models while addressing constraints such as homogeneity, reconfigurability, form factor, and power consumption. The diversified nature of various modular robotic solutions proposed for real world applications and utilization of different sensor and actuator interfacing techniques along with physical model optimizations presents implicit challenges to researchers while identifying and visualizing the merits/demerits of various approaches to a solution. This paper attempts to simplify the comparison of various hardware prototypes by providing a brief study on hardware architectures of modular robots capable of self-healing and reconfiguration along with design techniques adopted in modeling robots, interfacing technologies, and so forth over the past 25 years

Systematic strategies for 3-dimensional modular robots

Modular robots have been studied an classified from different perspectives, generally focusing on the mechatronics. But the geometric attributes and constraints are the ones that determine the self-reconfiguration strategies. In two dimensions, robots can be geometrically classified by the grid in which their units are arranged and the free cells required to move a unit to an edge-adjacent or vertex-adjacent cell. Since a similar analysis does not exist in three dimensions, we present here a systematic study of the geometric aspects of three-dimensional modular robots. We find relations among the different designs but there are no general models, except from the pivoting cube one, that lead to deterministic reconfiguration plans. In general the motion capabilities of a single module are very limited and its motion constraints are not simple. A widely used method for reducing the complexity and improving the speed of reconfiguration plans is the use of meta-modules. We present a robust and compact meta-module of M-TRAN and other similar robots that is able to perform the expand/contract operations of the Telecube units, for which efficient reconfiguration is possible. Our meta-modules also perform the scrunch/relax and transfer operations of Telecube meta-modules required by the known reconfiguration algorithms. These reduction proofs make it possible to apply efficient geometric reconfiguration algorithms to this type of robots

Dynamic Reconfiguration in Modular Self-Reconfigurable Robots Using Multi-Agent Coalition Games

In this thesis, we consider the problem of autonomous self-reconfiguration by modular self-reconfigurable robots (MSRs). MSRs are composed of small units or modules that can be dynamically configured to form different structures, such as a lattice or a chain. The main problem in maneuvering MSRs is to enable them to autonomously reconfigure their structure depending on the operational conditions in the environment. We first discuss limitations of previous approaches to solve the MSR self-reconfiguration problem. We will then present a novel framework that uses a layered architecture comprising a conventional gait table-based maneuver to move the robot in a fixed configuration, but using a more complex coalition game-based technique for autonomously reconfiguring the robot. We discuss the complexity of solving the reconfiguration problem within the coalition game-based framework and propose a stochastic planning and pruning based approach to solve the coalition-game based MSR reconfiguration problem. We tested our MSR self-reconfiguration algorithm using an accurately simulated model of an MSR called ModRED (Modular Robot for Exploration and Discovery) within the Webots robot simulator. Our results show that using our coalition formation algorithm, MSRs are able to reconfigure efficiently after encountering an obstacle. The average “reward” or efficiency obtained by an MSR also improves by 2-10% while using our coalition formation algorithm as compared to a previously existing multi-agent coalition formation algorithm. To the best of our knowledge, this work represents two novel contributions in the field of modular robots. First, ours is one of the first research techniques that has combined principles from human team formation techniques from the area of computational economics with dynamic self-reconfiguration in modular self-reconfigurable robots. Secondly, the modeling of uncertainty in coalition games using Markov Decision Processes is a novel and previously unexplored problem in the area of coalition formation. Overall, this thesis addresses a challenging research problem at the intersection of artificial intelligence, game theory and robotics and opens up several new directions for further research to improve the control and reconfiguration of modular robots

A Hybrid and Extendable Self-Reconfigurable Modular Robotic System

Modular robotics has the potential to transform the perception of robotic systems from machines built for specific tasks to multi-purpose tools capable of performing virtually any task. This thesis presents the design, implementation and study of a new self-reconfigurable modular robotic system for use as a research and education platform. The system features a high-speed genderless connector (HiGen), a hybrid module (HyMod), an extensions framework, and a control architecture. The HiGen connector features inter-module communication and is able to join with other HiGen connectors in a manner that allows either side to disconnect in the event of failure. The rapid actuation of HiGen allows connections to be made and broken at a speed that is, to our knowledge, an order of magnitude faster than existing mechanical genderless approaches that feature single-sided disconnect, benefiting the self-reconfiguration time of modular robots. HyMod is a chain, lattice, and mobile hybrid modular robot, consisting of a spherical joint unit that is capable of moving independently and grouping with other units to form arbitrary cubic lattice structures. HyMod is the first module, to our knowledge, that combines efficient single-module locomotion, enabling self-assembly, with the ability for modules to freely rotate within their lattice positions, aiding the self-reconfigurability of large structures. The extension framework is used to augment the capabilities of HyMod units. Extensions are modules that feature specialized functionality, and interface with HyMod units via passive HiGen connectors, allowing them to be un-powered until required for a task. Control of the system is achieved using a software architecture. Based on message routing, the architecture allows for the concurrent use of both centralized and distributed module control strategies. An analysis of the system is presented, and experiments conducted to demonstrate its capabilities. Future versions of the system created by this thesis could see uses in reconfigurable manufacturing, search and rescue, and space exploration