Regenerative Patterning in Swarm Robots: Mutual Benefits of Research in Robotics and Stem Cell Biology

This paper presents a novel perspective of Robotic Stem Cells (RSCs), defined as the basic non-biological elements with stem cell like properties that can self-reorganize to repair damage to their swarming organization. Self here means that the elements can autonomously decide and execute their actions without requiring any preset triggers, commands, or help from external sources. We develop this concept for two purposes. One is to develop a new theory for self-organization and self-assembly of multi-robots systems that can detect and recover from unforeseen errors or attacks. This self-healing and self-regeneration is used to minimize the compromise of overall function for the robot team. The other is to decipher the basic algorithms of regenerative behaviors in multi-cellular animal models, so that we can understand the fundamental principles used in the regeneration of biological systems. RSCs are envisioned to be basic building elements for future systems that are capable of self-organization, self-assembly, self-healing and self-regeneration. We first discuss the essential features of biological stem cells for such a purpose, and then propose the functional requirements of robotic stem cells with properties equivalent to gene controller, program selector and executor. We show that RSCs are a novel robotic model for scalable self-organization and self-healing in computer simulations and physical implementation. As our understanding of stem cells advances, we expect that future robots will be more versatile, resilient and complex, and such new robotic systems may also demand and inspire new knowledge from stem cell biology and related fields, such as artificial intelligence and tissue engineering

Autonomous In-Orbit Satellite Assembly from a Modular Heterogeneous Swarm

This paper presents a decentralized, distributed guidance and control scheme to combine a heterogeneous swarm of component satellites into a large satellite structure. The component satellites for the heterogeneous swarm are chosen to promote flexibility in final shape inspired by crystal structures and Islamic tile art. After the ideal fundamental building blocks are selected, basic nanosatellite-class satellite designs are made to assist in simulations involving attitude control. The Swarm Orbital Construction Algorithm (SOCA) is a guidance and control algorithm to allow for the limited type heterogeneity and docking ability required for in-orbit assembly. The algorithm consists of two parts, a distributed auction which uses barrier functions to ensure the proper agent selection for each target, and a trajectory generation portion which leverages model predictive control and sequential convex programming to achieve optimal collision-free trajectories to the desired target point even with nonlinear system dynamics. The optimization constraints use a boundary layer to determine whether the collision avoidance or the docking constraints should be applied. The algorithm was tested in a simulated perturbed 6-DOF spacecraft dynamic environment for planar and out-of-plane final structures and on two robotic platforms, including a swarm of frictionless spacecraft simulation robots

Model predictive control for cooperative control of space robots

The problem of Orbital Manipulation of Passive body is discussed here. Two scenarios including passive object rigidly attached to robotic servicers and passive body attached to servicers through manipulators are discussed. The Model Predictive Control (MPC) technique is briefly presented and successfully tested through simulations on two cases of position control of passive body in the orbit

Distributed reinforcement learning for self-reconfiguring modular robots

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 101-106).In this thesis, we study distributed reinforcement learning in the context of automating the design of decentralized control for groups of cooperating, coupled robots. Specifically, we develop a framework and algorithms for automatically generating distributed controllers for self-reconfiguring modular robots using reinforcement learning. The promise of self-reconfiguring modular robots is that of robustness, adaptability and versatility. Yet most state-of-the-art distributed controllers are laboriously handcrafted and task-specific, due to the inherent complexities of distributed, local-only control. In this thesis, we propose and develop a framework for using reinforcement learning for automatic generation of such controllers. The approach is profitable because reinforcement learning methods search for good behaviors during the lifetime of the learning agent, and are therefore applicable to online adaptation as well as automatic controller design. However, we must overcome the challenges due to the fundamental partial observability inherent in a distributed system such as a self reconfiguring modular robot. We use a family of policy search methods that we adapt to our distributed problem. The outcome of a local search is always influenced by the search space dimensionality, its starting point, and the amount and quality of available exploration through experience.(cont) We undertake a systematic study of the effects that certain robot and task parameters, such as the number of modules, presence of exploration constraints, availability of nearest-neighbor communications, and partial behavioral knowledge from previous experience, have on the speed and reliability of learning through policy search in self-reconfiguring modular robots. In the process, we develop novel algorithmic variations and compact search space representations for learning in our domain, which we test experimentally on a number of tasks. This thesis is an empirical study of reinforcement learning in a simulated lattice based self-reconfiguring modular robot domain. However, our results contribute to the broader understanding of automatic generation of group control and design of distributed reinforcement learning algorithms.by Paulina Varshavskaya.Ph.D

Autonomous construction using scarce resources in unknown environments - Ingredients for an intelligent robotic interaction with the physical world

The goal of creating machines that autonomously perform useful work in a safe, robust and intelligent manner continues to motivate robotics research. Achieving this autonomy requires capabilities for understanding the environment, physically interacting with it, predicting the outcomes of actions and reasoning with this knowledge. Such intelligent physical interaction was at the centre of early robotic investigations and remains an open topic. In this paper, we build on the fruit of decades of research to explore further this question in the context of autonomous construction in unknown environments with scarce resources. Our scenario involves a miniature mobile robot that autonomously maps an environment and uses cubes to bridge ditches and build vertical structures according to high-level goals given by a human. Based on a "real but contrived" experimental design, our results encompass practical insights for future applications that also need to integrate complex behaviours under hardware constraints, and shed light on the broader question of the capabilities required for intelligent physical interaction with the real world

Optimal self assembly of modular manipulators with active and passive modules

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 89-92).In this thesis, we describe algorithms to build self-assembling robot systems composed of active modular robots and passive bars. The robotic module is the Shady3D robot and the passive component is a rigid bar with embedded IR LEDs. We propose algorithms that demonstrate the cooperative aggregation of modular robotic manipulators with greater capability and workspace out of these two types of elements. The distributed algorithms are based on locally optimal matching. We demonstrate how to build an active structure by the cooperative aggregation and disassembly of modular robotic manipulators. A target structure is modeled as a dynamic graph. We prove that the same optimality - quadratic competitive ratio - as for the static graph can be achieved for the algorithms. We demonstrate how this algorithm can be used to build truss-like structures. We present results from physical experiments in which two 3DOF Shady3D robots and one rigid bar coordinate to self-assemble into a 6DOF manipulator. We then demonstrate cooperative algorithms for forward and inverse kinematics, grasping, and mobility with this arm.by Seung-kook Yun.S.M

Dynamic programming applied to electromagnetic satellite actuation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections."June 2013." Cataloged from department-submitted PDF version of thesisIncludes bibliographical references (p. 135-140).Electromagnetic formation flight (EMFF) is an enabling technology for a number of space mission architectures. While much work has been done for EMFF control for large separation distances, little work has been done for close-proximity EMFF control, where the system dynamics are quite complex. Dynamic programming has been heavily used in the optimization world, but not on embedded systems. In this thesis, dynamic programming is applied to satellite control, using close-proximity EMFF control as a case study. The concepts of dynamic programming and approximate dynamic programming are discussed. Several versions of the close-proximity EMFF control problem are formulated as a dynamic programming problems. One of the formulations is used as a case study for developing and examining the cost-to-go. Methods for implementing an approximate dynamic programming controller on a satellite are discussed. Methods for resolving physical states and dynamic programming states are presented. Because the success of dynamic programming depends on the system model, a novel method for finding the mass properties of a satellite, which would likely be used in the dynamic programming model, is introduced. This method is used to characterize the mass properties of three satellite systems: SPHERES, VERTIGO, and RINGS. Finally, a method for position and attitude estimation for systems that use line-of-sight measurements that does not require the use of a model is developed. This method is useful for model validation of the models used in the dynamic programming formulation.by Gregory John Eslinger.S.M

Modular robots for making and climbing 3-D trusses

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (p. 139-143).A truss climbing robot has been extensively investigated because of its wide range of promising applications such as construction and inspection of truss structures. It is designed to have degrees of freedom to move in three-dimensional truss structures. Although many degrees of freedom allow the robot to reach various position and orientation, it causes complexity of design and control. In this thesis, the concept of modular robots is suggested as a solution to reconcile a trade-off between the functionality and the simplicity of a truss climbing robot. A single module has fewer degrees of freedom than required to achieve full 3-D motion, but it can move freely in a 2-D plane. For full 3-D motion, multiple modules connect to and cooperate with each other. Thus, modular truss climbing robots can have both properties: functionality and simplicity. A modular truss climbing robot, called Shady3D, is presented as the hardware implementation of this concept. This robot has three motive degrees of freedom, and can form a six-degree-of-freedom structure by connecting to another identical module using a passive bar as a medium. Algorithms to move the robot in a 3-D truss structure have been developed and tested in hardware experiments.(cont.) The cooperation capability of two modules is also demonstrated. As a next step beyond truss climbing robots, the concept of a self-assembling truss robot with active and passive modules is presented. In this system, multiple Shady3D robots are employed as active modules and they become an active truss structure using passive bars. The procedure of self-assembling such a truss is demonstrated in computer simulations, which show a potential application in robotic truss assembly.by Yeoreum Yoon.S.M