A distributed self-reconfiguration algorithm for cylindrical lattice-based modular robots

International audienceModular self-reconfigurable robots are composed of independent connected modules which can self-rearrange their connectivity using processing, communication and motion capabilities, in order to change the overall robot structure. In this paper, we consider rolling cylindrical modules arranged in a two-dimensional vertical hexagonal lattice. We propose a parallel, asynchronous and fully decentralized distributed algorithm to self-reconfigure robots from an initial configuration to a goal one. We evaluate our algorithm on the millimeter-scale cylindrical robots, developed in the Claytronics project, through simulation of large ensembles composed of up to ten thousand modules. We show the effectiveness of our algorithm and study its performance in terms of communications, movements and execution time. Our observations indicate that the number of communications, the number of movements and the execution time of our algorithm is highly predictable. Furthermore, we observe execution times that are linear in the size of the goal shape

An Approach to the Bio-Inspired Control of Self-reconfigurable Robots

Self-reconfigurable robots are robots built by modules which can move in relationship to each other. This ability of changing its physical form provides the robots a high level of adaptability and robustness. Given an initial configuration and a goal configuration of the robot, the problem of self-regulation consists on finding a sequence of module moves that will reconfigure the robot from the initial configuration to the goal configuration. In this paper, we use a bio-inspired method for studying this problem which combines a cluster-flow locomotion based on cellular automata together with a decentralized local representation of the spatial geometry based on membrane computing ideas. A promising 3D software simulation and a 2D hardware experiment are also presented.National Natural Science Foundation of China No. 6167313

Factories of the Future

Engineering; Industrial engineering; Production engineerin

Enzyme Powered Nanomotors Towards Biomedical Applications

[eng] The advancements in nanotechnology enabled the development of new diagnostic tools and drug delivery systems based on nanosystems, which offer unique features such as large surface area to volume ratio, cargo loading capabilities, increased circulation times, as well as versatility and multifunctionality. Despite this, the majority of nanomedicines do not translate into clinics, in part due to the biological barriers present in the body. Synthetic nano- and micromotors could be an alternative tool in nanomedicine, as the continuous propulsion force and potential to modulate the medium may aid tissue penetration and drug diffusion across biological barriers. Enzyme-powered motors are especially interesting for biomedical applications, owing to their biocompatibility and use of bioavailable substrates as fuel for propulsion. This thesis aims at exploring the potential applications of urease-powered nanomotors in nanomedicine. In the first work, we evaluated these motors as drug delivery systems. We found that active urease- powered nanomotors showed active motion in phosphate buffer solutions, and enhanced in vitro drug release profiles in comparison to passive nanoparticles. In addition, we observed that the motors were more efficient in delivering drug to cancer cells and caused higher toxicity levels, due to the combination of boosted drug release and local increase of pH produced by urea breakdown into ammonia and carbon dioxide. One of the major goals in nanomedicine is to achieve localized drug action, thus reducing side-effects. A commonly strategy to attain this is the use moieties to target specific diseases. In our second work, we assessed the ability of urease-powered nanomotors to improve the targeting and penetration of spheroids, using an antibody with therapeutic potential. We showed that the combination of active propulsion with targeting led to a significant increase in spheroid penetration, and that this effect caused a decrease in cell proliferation due to the antibody’s therapeutic action. Considering that high concentrations of nanomedicines are required to achieve therapeutic efficiency; in the third work we investigated the collective behavior of urease-powered nanomotors. Apart from optical microscopy, we evaluated the tracked the swarming behavior of the nanomotors using positron emission tomography, which is a technique widely used in clinics, due to its noninvasiveness and ability to provide quantitative information. We showed that the nanomotors were able to overcome hurdles while swimming in confined geometries. We observed that the nanomotors swarming behavior led to enhanced fluid convection and mixing both in vitro, and in vivo within mice’s bladders. Aiming at conferring protecting abilities to the enzyme-powered nanomotors, in the fourth work, we investigated the use of liposomes as chassis for nanomotors, encapsulating urease within their inner compartment. We demonstrated that the lipidic bilayer provides the enzymatic engines with protection from harsh acidic environments, and that the motility of liposome-based motors can be activated with bile salts. Altogether, these results demonstrate the potential of enzyme-powered nanomotors as nanomedicine tools, with versatile chassis, as well as capability to enhance drug delivery and tumor penetration. Moreover, their collective dynamics in vivo, tracked using medical imaging techniques, represent a step-forward in the journey towards clinical translation.[spa] Recientes avances en nanotecnología han permitido el desarrollo de nuevas herramientas para el diagnóstico de enfermedades y el transporte dirigido de fármacos, ofreciendo propiedades únicas como encapsulación de fármacos, el control sobre la biodistribución de estos, versatilidad y multifuncionalidad. A pesar de estos avances, la mayoría de nanomedicinas no consiguen llegar a aplicaciones médicas reales, lo cual es en parte debido a la presencia de barreras biológicas en el organismo que limitan su transporte hacia los tejidos de interés. En este sentido, el desarrollo de nuevos micro- y nanomotores sintéticos, capaces de autopropulsarse y causar cambios locales en el ambiente, podrían ofrecer una alternativa para la nanomedicina, promoviendo una mayor penetración en tejidos de interés y un mejor transporte de fármacos a través de las barreras biológicas. En concreto, los nanomotores enzimáticos poseen un alto potencial para aplicaciones biomédicas gracias a su biocompatibilidad y a la posibilidad de usar sustancias presentes en el organismo como combustible. Los trabajos presentados en esta tesis exploran el potenical de nanomotores, autopropulsados mediante la enzima ureasa, para aplicaciones biomédicas, y investigan su uso como vehículos para transporte de fármacos, su capacidad para mejorar penetración de tejidos diana, su versatilidad y movimiento colectivo. En conjunto, los resultados presentados en esta tesis doctoral demuestran el potencial del uso de nanomotores autopropulsados mediante enzimas como herramientas biomédicas, ofreciendo versatilidad en su diseño y una alta capacidad para promover el transporte de fármacos y la penetración en tumores. Por último, su movimiento colectivo observado in vivo mediante técnicas de imagen médicas representan un significativo avance en el viaje hacia su aplicación en medicina

Factories of the Future

Engineering; Industrial engineering; Production engineerin

On the Interplay between Mechanical and Computational Intelligence in Robot Hands

Researchers have made tremendous advances in robotic grasping in the past decades. On the hardware side, a lot of robot hand designs were proposed, covering a large spectrum of dexterity (from simple parallel grippers to anthropomorphic hands), actuation (from underactuated to fully actuated), and sensing capabilities (from only open/close states to tactile sensing). On the software side, grasping techniques also evolved significantly, from open-loop control, classical feedback control, to learning-based policies. However, most of the studies and applications follow the one-way paradigm that mechanical engineers/researchers design the hardware first and control/learning experts write the code to use the hand. In contrast, we aim to study the interplay between the mechanical and computational aspects in robotic grasping. We believe both sides are important but cannot solve grasping problems on their own, and both sides are highly connected by the laws of physics and should not be developed separately. We use the term "Mechanical Intelligence" to refer to the ability realized by mechanisms to appropriately respond to the external inputs, and we show that incorporating Mechanical Intelligence with Computational Intelligence is beneficial for grasping. The first part of this thesis is to derive hand underactuation mechanisms from grasp data. The mechanical coordination in robot hands, which is one type of Mechanical Intelligence, corresponds to the concept of dimensionality reduction in Machine Learning. However, the resulted low-dimensional manifolds need to be realizable using underactuated mechanisms. In this project, we first collect simulated grasp data without accounting for underactuation, apply a dimensionality reduction technique (we term it "Mechanically Realizable Manifolds") considering both pre-contact postural synergies and post-contact joint torque coordination, and finally build robot hands based on the resulted low-dimensional models. We also demonstrate a real-world application on a free-flying robot for the International Space Station. The second part is about proprioceptive grasping for unknown objects by taking advantage of hand compliance. Mechanical compliance is intrinsically connected to force/torque sensing and control. In this work, we proposed a series-elastic hand providing embodied compliance and proprioception, and an associated grasping policy using a network of proportional-integral controllers. We show that, without any prior model of the object and with only proprioceptive sensing, a robot hand can make stable grasps in a reactive fashion. The last part is about developing the Mechanical and Computational Intelligence jointly --- to co-optimize the mechanisms and control policies using deep Reinforcement Learning (RL). Traditional RL treats robot hardware as immutable and models it as part of the environment. In contrast, we move the robot hardware out of the environment, express its mechanics as auto-differentiable physics and connect it with the computational policy to create a unified policy (we term this method "Hardware as Policy"), which allows RL algorithms to back-propagate gradients w.r.t both hardware and computational parameters and optimize them in the same fashion. We present a mass-spring toy problem to illustrate this idea, and also a real-world design case of an underactuated hand. The three projects we present in this thesis are meaningful examples to demonstrate the interplay between the mechanical and computational aspects of robotic grasping. In the Conclusion part, we summarize some high-level philosophies and suggestions to integrate Mechanical and Computational Intelligence, as well as the high-level challenges that still exist when pushing this area forward

Shape formation by self-disassembly in programmable matter systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 225-236).Programmable matter systems are composed of small, intelligent modules able to form a variety of macroscale objects with specific material properties in response to external commands or stimuli. While many programmable matter systems have been proposed in fiction, (Barbapapa, Changelings from Star Trek, the Terminator, and Transformers), and academia, a lack of suitable hardware and accompanying algorithms prevents their full realization. With this thesis research, we aim to create a system of miniature modules that can form arbitrary structures on demand. We develop autonomous 12mm cubic modules capable of bonding to, and communicating with, four of their immediate neighbors. These modules are among the smallest autonomous modular robots capable of sensing, communication, computation, and actuation. The modules employ unique electropermanent magnet connectors. The four connectors in each module enable the modules to communicate and share power with their nearest neighbors. These solid-state connectors are strong enough for a single inter-module connection to support the weight of 80 other modules. The connectors only consume power when switching on or off; they have no static power consumption. We implement a number of low-level communication and control algorithms which manage information transfer between neighboring modules. These algorithms ensure that messages are delivered reliably despite challenging conditions. They monitor the state of all communication links and are able to reroute messages around broken communication links to ensure that they reach their intended destinations. In order to accomplish our long-standing goal of programmatic shape formation, we also develop a suite of provably-correct distributed algorithms that allow complex shape formation. The distributed duplication algorithm that we present allows the system to duplicate any passive object that is submerged in a collection of programmable matter modules. The algorithm runs on the processors inside the modules and requires no external intervention. It requires 0(1) storage and O(n) inter-module messages per module, where n is the number of modules in the system. The algorithm can both magnify and produce multiple copies of the submerged object. A programmable matter system is a large network of autonomous processors, so these algorithms have applicability in a variety of routing, sensor network, and distributed computing applications. While our hardware system provides a 50-module test-bed for the algorithms, we show, by using a unique simulator, that the algorithms are capable of operating in much larger environments. Finally, we perform hundreds of experiments using both the simulator and hardware to show how the algorithms and hardware operate in practice.by Kyle William Gilpin.Ph.D

Automated design of modular field robots

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1995.Includes bibliographical references (leaves 80-84).by Nathaniel Rutman.M.S