Characterization and Validation of a Novel Robotic System for Fluid-Mediated Programmable Stochastic Self-Assembly

Several self-assembly systems have been developed in recent years, where depending on the capabilities of the building blocks and the controlability of the environment, the assembly process is guided typically through either a fully centralized or a fully distributed control approach. In this work, we present a novel experimental system for studying the range of fully centralized to fully distributed control strategies. The system is built around the floating 3-cm-sized Lily robots, and comprises a water-filled tank with peripheral pumps, an overhead camera, an overhead projector, and a workstation capable of controlling the fluidic flow field, setting the ambient luminosity, communicating with the robots over radio, and visually tracking their trajectories. We carry out several experiments to characterize the system and validate its capabilities. First, a statistical analysis is conducted to show that the system is governed by reaction diffusion dynamics, and validate the applicability of the standard chemical kinetics modeling. Additionally, the natural tendency of the system for structure formation subject to different flow fields is investigated and corresponding implications on guiding the self-assembly process are discussed. Finally, two control approaches are studied: 1) a fully distributed control approach and 2) a distributed approach with additional central supervision exhibiting an improved performance. The formation time statistics are compared and a discussion on the generalization of the method is provided

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

HoverBots: Precise Locomotion Using Robots That Are Designed for Manufacturability

Scaling up robot swarms to collectives of hundreds or even thousands without sacrificing sensing, processing, and locomotion capabilities is a challenging problem. Low-cost robots are potentially scalable, but the majority of existing systems have limited capabilities, and these limitations substantially constrain the type of experiments that could be performed by robotics researchers. As an alternative to increasing the quantity of robots by reducing their functionality, we have developed a new technology that delivers increased functionality at low-cost. In this study, we present a comprehensive literature review on the most commonly used locomotion strategies of swarm robotic systems. We introduce a new type of low-friction locomotion—active low-friction locomotion—and we show its first implementation in the HoverBot system. The HoverBot system consists of an air levitation and magnet table, and a HoverBot agent. HoverBot agents are levitating circuit boards that we have equipped with an array of planar coils and a Hall-effect sensor. The HoverBot agent uses its coils to pull itself toward magnetic anchors that are embedded into a levitation table. These robots use active low-friction locomotion; consist of only surface-mount components; circumvent actuator calibration; are capable of odometry by using a single Hall-effect sensor; and perform precise movement. We conducted three hours of experimental evaluation of the HoverBot system in which we observed the system performing more than 10,000 steps. We also demonstrate formation movement, random collision, and straight collisions with two robots. This study demonstrates that active low-friction locomotion is an alternative to wheeled and slip-stick locomotion in the field of swarm robotics

Self Assembly of Modular Robots with Finite Number of Modules Using Graph Grammar

We wish to design decentralized algorithms for self-assembly of robotic modules that have 100% yield even if the number of available building blocks is limited, and specically when the number of available building blocks is identical to the number of blocks required by the structure. In contrast to self-assembly at the nano and micro scales where abundant building blocks are available, modular robotic systems need to self-assemble from a limited number of modules. In particular, when self-assembly is used for reconguration, it is desirable that the new conformation includes all of the available modules. We propose a suite of algorithms that (1) generate a reversible graph grammar, i.e., generates rules for a desired structure that allow the structure not only to assemble, but also to disassemble, and (2) have a set of structures that are growing in parallel converge to a single structure using broadcast communication. We show that by omitting a reversal rule for the last attached module, self-assembly eventually completes, and that communication can drastically speed up this process. We verify our results by running simulations on Matlab and Player/Stage 2D simulato