31 research outputs found
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
A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots
Re-configurable modular robotic (RMR) systems are advantageous for their
reconfigurability and versatility. A new modular robot can be built for a
specific task by using modules as building blocks. However, constructing a
kinematic model for a newly conceived robot requires significant work. Due to
the finite size of module-types, models of all module-types can be built
individually and stored in a database beforehand. With this priori knowledge,
the model construction process can be automated by detecting the modules and
their corresponding interconnections. Previous literature proposed theoretical
frameworks for constructing kinematic models of modular robots, assuming that
such information was known a priori. While well-devised mechanisms and built-in
sensors can be employed to detect these parameters automatically, they
significantly complicate the module design and thus are expensive. In this
paper, we propose a vision-based method to identify kinematic chains and
automatically construct robot models for modular robots. Each module is affixed
with augmented reality (AR) tags that are encoded with unique IDs. An image of
a modular robot is taken and the detected modules are recognized by querying a
database that maintains all module information. The poses of detected modules
are used to compute: (i) the connection between modules and (ii) joint angles
of joint-modules. Finally, the robot serial-link chain is identified and the
kinematic model constructed and visualized. Our experimental results validate
the effectiveness of our approach. While implementation with only our RMR is
shown, our method can be applied to other RMRs where self-identification is not
possible
A Game-theoretic Formulation of the Homogeneous Self-Reconfiguration Problem
In this paper we formulate the homogeneous two- and three-dimensional
self-reconfiguration problem over discrete grids as a constrained potential
game. We develop a game-theoretic learning algorithm based on the
Metropolis-Hastings algorithm that solves the self-reconfiguration problem in a
globally optimal fashion. Both a centralized and a fully distributed algorithm
are presented and we show that the only stochastically stable state is the
potential function maximizer, i.e. the desired target configuration. These
algorithms compute transition probabilities in such a way that even though each
agent acts in a self-interested way, the overall collective goal of
self-reconfiguration is achieved. Simulation results confirm the feasibility of
our approach and show convergence to desired target configurations.Comment: 8 pages, 5 figures, 2 algorithm
Stimulus Pulse-Based Distributed Control for the Locomotion of a UBot Modular Robot
A distributed control algorithm based on a stimulus pulse signal is proposed in this paper for the locomotion of a Modular Self-reconfigurable Robot (MSRR). This approach can adapt effectively to the dynamic changes in the MSRR's topological configuration: the functional role of the configuration can be recognized through local topology detection, dynamic ID address allocation and local topology matching, such that the features of the entire configuration can be identified and thereby the corresponding stimulus signals can be chosen to control the whole system for coordinated locomotion. This approach has advantages over centralized control in terms of flexibility and robustness, and communication efficiency is not limited by the module number, which can realize coordinated locomotion control conveniently (especially for configurations made up of massive modules and characterized by a chain style or a quadruped style)
Recommended from our members
An extendible reconfigurable robot based on hot melt adhesives
The ability to physically enlarge one’s own body structures plays an important role in robustness and adaptability of biological systems. It is, however, a significant challenge for robotic systems to autonomously extend their bodies. To address this challenge, this paper presents an approach using Hot Melt Adhesives (HMAs) to assemble and integrate extensions into the robotic body. HMAs are thermoplastics with temperature dependent adhesiveness and bonding strength. We exploit this property of HMAs to connect passive external objects to the robot’s own body structures, and investigate the characteristics of the approach. In a set of elementary configurations, we analyze to which extent a robot can self-reconfigure using the proposed method. We found that the extension limit depends on the mechanical properties of the extension, and the reconfiguration algorithm. A five-axis robot manipulator equipped with specialized HMA handling devices is employed to demonstrate these findings in four experiments. It is shown that the robot can construct and integrate extensions into its own body, which allow it to solve tasks that it could not achieve in its initial configuration.This work was supported by the Swiss National Science Foundation Professorship Grant No. PP00P2123387/1, and the ETH Zurich Research Grant ETH-23-10-3.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s10514-015-9428-