Consider a robot trying to accomplish a mission in an unfamiliar, hostile, and dynamic environment. As the robot encounters a seemingly insurmountable obstacle, it quickly reconfigures itself into two smaller robots, and with the aid of a nearby puddle of water, these two robots conduct self-detection procedures to confirm successful reconfiguration. One robot then calibrates itself using its reduced sensor suite and begins climbing over the obstacle, whilst the other robot folds itself into a compact structure and finds a crevice it can use to bypass the obstacle. But when the obstacle shifts, both robots send signals to a base to warn of impending mission failure. A copy of the original robot, located at the base, receives these warning signals and initiates a rapid self-replication process by assembling available resources. The resultant robotic offspring are then ready to continue the mission when called upon.

This paper addresses the problem of self-detection by a robot. The paper describes a methodology for autonomous learning of the characteristic delay between motor commands (efferent signals) and observed movements of visual stimuli (afferent signals). The robot estimates its own efferent-afferent delay from self-observation data gathered while performing motor babbling, i.e., random rhythmic movements similar to the primary circular reactions described by Piaget. After the efferent-afferent delay is estimated, the robot imprints on that delay and can later use it to successfully classify visual stimuli as either “self” or “other.” Results from robot experiments performed in environments with increasing degrees of difficulty are reported.

High accuracy is usually difficult to obtain with a robotic arm installed on a mobile base, since the errors of the base are transferred to the manipulator. This paper proposes a method to address this problem through integration of a self-calibration algorithm and low-cost sensors. The self-calibration algorithm might be repeated several times during execution of a mission by the robot and is only based on the internal sensors of the robot, meaning that external observers or reference point transceivers (e.g., ultrasonic transceivers) are not used. The proposed self-calibration system was implemented on a pole climbing robot and effectively improved the positioning accuracy of the climbing arm.

Modular, self-reconfigurable robots are robotic systems that can change their own shape by autonomously rearranging the physical modules from which they are built. In this work, we are interested in how to distributedly execute a specified self-reconfiguration sequence. The sequence is specified using a simple and centralized scripting language, which either could be the outcome of a planner or be hand-coded. The distributed controller generated from this language allows for parallel self-reconfiguration steps and is highly robust to communication errors and loss of local state due to software failures. Furthermore, the self-reconfiguration sequence can automatically be reversed, if desired. We verify our approach and demonstrate its robustness in experiments using physical and the simulated ATRON modules, as well as simulated M-TRAN modules. Overall, the contribution of this work is the combination of the tractability of a centralized scripting language with the robustness and parallelism of distributed controllers in modular robots.

In this paper, we propose novel algorithms for reconfiguring modular robots that are composed of n atoms. Each atom has the shape of a unit cube and can expand/contract each face by half a unit, as well as attach to or detach from faces of neighboring atoms. For universal reconfiguration, atoms must be arranged in 2 × 2 × 2 modules. We respect certain physical constraints: each atom reaches at most constant velocity and can displace at most a constant number of other atoms. We assume that one of the atoms has access to the coordinates of atoms in the target configuration.

Our algorithms involve a total of O(n2) atom operations, which are performed in O(n) parallel steps. This improves on previous reconfiguration algorithms, which either use O(n2) parallel steps or do not respect the constraints mentioned above. In fact, in the settings considered, our algorithms are optimal. A further advantage of our algorithms is that reconfiguration can take place within the union of the source and target configuration space, and only requires local communication.

Motion planning for self-reconfiguring robots can be made efficient by exploiting potential reductions to suitably large subspaces. However, there are no general techniques for identifying suitable restrictions that have a positive effect on planning efficiency. We present two approaches to understanding the structure that is required of the subspaces, which leads to improvement in efficiency of motion planning. This work is presented in the context of a specific motion planning procedure for a hexagonal metamorphic robot. First, we use ideas from spectral graph theory – empirically estimating the algebraic connectivity of the state space – to show that the HMR model is better structured than many alternative motion catalogs. Secondly, using ideas from graph minor theory, we show that the infinite sequence of subspaces generated by configurations containing increasing numbers of subunits is well ordered, indicative of regularity of the space as complexity increases. We hope that these principles could inform future algorithm design for many different types of self-reconfiguring robotics problems.

This paper considers planning and control algorithms that enable a programmable sheet to realize different shapes by autonomous folding. Prior work on self-reconfiguring machines has considered modular systems in which independent units coordinate with their neighbors to realize a desired shape. A key limitation in these prior systems is the typically many operations to make and break connections with neighbors, which lead to brittle performance. We seek to mitigate these difficulties through the unique concept of self-folding origami with a universal fixed set of hinges. This approach exploits a single sheet composed of interconnected triangular sections. The sheet is able to fold into a set of predetermined shapes using embedded actuation.

We describe the planning algorithms underlying these self-folding sheets, forming a new family of reconfigurable robots that fold themselves into origami by actuating edges to fold by desired angles at desired times. Given a flat sheet, the set of hinges, and a desired folded state for the sheet, the algorithms (1) plan a continuous folding motion into the desired state, (2) discretize this motion into a practicable sequence of phases, (3) overlay these patterns and factor the steps into a minimum set of groups, and (4) automatically plan the location of actuators and threads on the sheet for implementing the shape-formation control.

The fields of modular reconfigurable robotics and programmable matter study how to compose functionally useful systems from configurations of modules. In addition to the external shape of a module configuration, the internal arrangement of modules and bonds between them can greatly impact functionally relevant mechanical properties such as load bearing ability. A fast method to evaluate the mechanical property aids the search for an arrangement of modules achieving a desired mechanical property as the space of possible configurations grows combinatorially. We present a fast approximate method where the bonds between modules are represented with stiffness matrices that are general enough to represent a wide variety of systems and follows the natural modular decomposition of the system. The method includes nonlinear modeling such as anisotropic bonds and properties that vary as components flex. We show that the arrangement of two types of bonds within a programmable matter systems enables programming the apparent elasticity of the structure. We also present a method to experimentally determine the stiffness matrix for chain style reconfigurable robots. The efficacy of applying the method is demonstrated on the CKBot modular robot and two programmable matter systems: the Rubik's snake folding chain toy and a right angle tetrahedron chain called RATChET7mm. By allowing the design space to be rapidly explored we open the door to optimizing modular structures for desired mechanical properties such as enhanced load bearing and robustness.

This paper is devoted to the study of systems of entities that are capable of generating other entities of the same kind and, possibly, self-reproducing. The main technical issue addressed is to quantify the requirements that such entities must meet to be able to produce a progeny that is not degenerative, i.e., that has the same reproductive capability as the progenitor. A novel theory that allows an explicit quantification of these requirements is presented. The notion of generation rank of an entity is introduced, and it is proved that the generation process, in most cases, is degenerative in that it strictly and irreversibly decreases the generation rank from parent to descendent. It is also proved that there exists a threshold of rank such that this degeneracy can be avoided if and only if the entity has a generation rank that meets that threshold – this is the von Neumann rank threshold. On the basis of this threshold, an information threshold is derived, which quantifies the minimum amount of information that must be provided to specify an entity such that its descendents are not degenerative. Furthermore, a complexity threshold is obtained, which quantifies the minimum length of the description of that entity in a given language. As an application, self-assembly for a 2 Degrees of Freedom planar robot is considered, and simulation results are presented. A robot arm capable of picking up and placing the components of another arm, in the presence of errors, is considered to have successfully reproduced if these are placed within an allowable tolerance. The example shows that, due to the kinematics of the robot, errors can grow from one generation to the next, until the reproduction process fails eventually. However, error correction (via error sensing and feedback control) can then be used to prevent such degeneracy. The von Neumann generation rank and information thresholds are computed for this example, and are consistent with the simulation results in predicting degeneracy in the case without error correction, and predicting successful self-reproduction in the case with error correction.

This paper presents the development of a self-replicating mobile robot that functions by undergoing stochastic motions. The robot functions hierarchically. There are three stages in this hierarchy: (1) An initial pool of feed modules/parts together with one functional basic robot; (2) a collection of basic robots that are spontaneously formed out of these parts as a result of a chain reaction induced by stochastic motion of the initial seed robot at stage 1; (3) complex formations of joined basic robots from stage 2. In the first part of this paper we demonstrate basic stochastic self-replication in unstructured environments. A single functional robot moves around at random in a sea of stock modules and catalyzes the conversion of these modules into replicas. In the second part of the paper, the robots are upgraded with a layer that enables mechanical connections between robots. The replicas can then connect to each other and aggregate. Finally, self-reconfigurability is presented for two robotic aggregations.

Cellular automata can model the information processing aspects of a self-replicating programmable constructing system (SRPC), but these models do not contain any notion of material parts and are poor at modelling features of a system that depend upon the motion and connectivity of its components. Physical systems with thousands of parts have the disadvantage that they are time consuming to develop. To overcome these limitations, a simulation environment is presented at a level of abstraction that models motion and connectivity. An SRPC which takes a disorganised collection of parts as its input has been implemented in this environment.

This paper presents the results to date of the RepRap project – an ongoing project that has made and distributed freely a replicating rapid prototyper. We give the background reasoning that led to the invention of the machine, the selection of the processes that we and others have used to implement it, the designs of key parts of the machine and how these have evolved from their initial concepts and experiments, and estimates of the machine's reproductive success out in the world up to the time of writing (about 4500 machines in two and a half years).