Determining task optimal modular robot assembly configurations

A "modular" robotic system consists of standardized joint and link units that can be assembled into a number of different kinematic configurations. Given a predetermined set of modules, this paper considers the problem of finding an "optimal" module assembly configuration for a specific task. The authors formulate the solution as a discrete optimization procedure. The formulation is based on an assembly incidence matrix representation of a modular robot and a general task-oriented objective function that can incorporate many realistic task criteria. Genetic algorithms (GA) are employed to solve this optimization problem, and a canonical method to represent a modular assembly in terms of genetic strings is introduced. An example involving a 3-DOF manipulator configuration is presented to demonstrate the feasibility of this approach

Determining task optimal modular robot assembly configurations

A "modular" robotic system consists of standardized joint and link units that can be assembled into a number of different kinematic configurations. Given a predetermined set of modules, this paper considers the problem of finding an "optimal" module assembly configuration for a specific task. The authors formulate the solution as a discrete optimization procedure. The formulation is based on an assembly incidence matrix representation of a modular robot and a general task-oriented objective function that can incorporate many realistic task criteria. Genetic algorithms (GA) are employed to solve this optimization problem, and a canonical method to represent a modular assembly in terms of genetic strings is introduced. An example involving a 3-DOF manipulator configuration is presented to demonstrate the feasibility of this approach

Automatic Modeling for Modular Reconfigurable Robotic Systems: Theory and Practice

A modular reconfigurable robot consists of a collection of individual link and joint components that can be assembled into a number of different robot ge-ometries. Compared to a conventional industrial robot with fixed geometry, such a system can provide flexibility to the user to cope with a wide spectru

A Rapidly Reconfigurable Robotics Workcell and Its Applictions for Tissue Engineering

This article describes the development of a component-based technology robot system that can be rapidly configured to perform a specific manufacturing task. The system is conceived with standard and inter-operable components including actuator modules, rigid link connectors and tools that can be assembled into robots with arbitrary geometry and degrees of freedom. The reconfigurable "plug-and-play" robot kinematic and dynamic modeling algorithms are developed. These algorithms are the basis for the control and simulation of reconfigurable robots. The concept of robot configuration optimization is introduced for the effective use of the rapidly reconfigurable robots. Control and communications of the workcell components are facilitated by a workcell-wide TCP/IP network and device level CAN-bus networks. An object-oriented simulation and visualization software for the reconfigurable robot is developed based on Windows NT. Prototypes of the robot systems configured to perform 3D contour following task and the positioning task are constructed and demonstrated. Applications of such systems for biomedical tissue scaffold fabrication are considered.Singapore-MIT Alliance (SMA

On adaptive robot systems for manufacturing applications

System adaptability is very important to current manufacturing practices due to frequent changes in the customer needs. Two basic concepts that can be employed to achieve system adaptability are flexible systems and modular systems. Flexible systems are fixed integral systems with some adjustable components. Adjustable components have limited ranges of parameter changes that can be made, thus restricting the adaptability of systems. Modular systems are composed of a set of pre-existing modules. Usually, the parameters of modules in modular systems are fixed, and thus increased system adaptability is realized only by increasing the number of modules. Increasing the number of modules could result in higher costs, poor positioning accuracy, and low system stiffness in the context of manufacturing applications. In this thesis, a new idea was formulated: a combination of the flexible system and modular system concepts. Systems developed based on this new idea are called adaptive systems. This thesis is focused on adaptive robot systems. An adaptive robot system is such that adaptive components or adjustable parameters are introduced upon the modular architecture of a robot system. This implies that there are two levels to achieve system adaptability: the level where a set of modules is appropriately assembled and the level where adjustable components or parameters are specified. Four main contributions were developed in this thesis study. First, a General Architecture of Modular Robots (GAMR) was developed. The starting point was to define the architecture of adaptive robot systems to have as many configuration variations as possible. A novel application of the Axiomatic Design Theory (ADT) was applied to GAMR development. It was found that GAMR was the one with the most coverage, and with a judicious definition of adjustable parameters. Second, a system called Automatic Kinematic and Dynamic Analysis (AKDA) was developed. This system was a foundation for synthesis of adaptive robot configurations. In comparison with the existing approach, the proposed approach has achieved systemization, generality, flexibility, and completeness. Third, this thesis research has developed a finding that in modular system design, simultaneous consideration of both kinematic and dynamic behaviors is a necessary step, owing to a strong coupling between design variables and system behaviors. Based on this finding, a method for simultaneous consideration of type synthesis, number synthesis, and dimension synthesis was developed. Fourth, an adaptive modular Parallel Kinematic Machine (PKM) was developed to demonstrate the benefits of adaptive robot systems in parallel kinematic machines, which have found many applications in machine tool industries. In this architecture, actuators and limbs were modularized, while the platforms were adjustable in such a way that both the joint positions and orientations on the platforms can be changed

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

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-

Heterogeneous Self-Reconfiguring Robotics: Ph.D. Thesis Proposal

Self-reconfiguring robots are modular systems that can change shape, or reconfigure, to match structure to task. They comprise many small, discrete, often identical modules that connect together and that are minimally actuated. Global shape transformation is achieved by composing local motions. Systems with a single module type, known as homogeneous systems, gain fault tolerance, robustness and low production cost from module interchangeability. However, we are interested in heterogeneous systems, which include multiple types of modules such as those with sensors, batteries or wheels. We believe that heterogeneous systems offer the same benefits as homogeneous systems with the added ability to match not only structure to task, but also capability to task. Although significant results have been achieved in understanding homogeneous systems, research in heterogeneous systems is challenging as key algorithmic issues remain unexplored. We propose in this thesis to investigate questions in four main areas: 1) how to classify heterogeneous systems, 2) how to develop efficient heterogeneous reconfiguration algorithms with desired characteristics, 3) how to characterize the complexity of key algorithmic problems, and 4) how to apply these heterogeneous algorithms to perform useful new tasks in simulation and in the physical world. Our goal is to develop an algorithmic basis for heterogeneous systems. This has theoretical significance in that it addresses a major open problem in the field, and practical significance in providing self-reconfiguring robots with increased capabilities

NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics