Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Scalability study for robotic hand platform

The goal of this thesis project was to determine the lower limit of scale for the RIT robotic grasping hand. This was accomplished using a combination of computer simulation and experimental studies. A force analysis was conducted to determine the size of air muscles required to achieve appropriate contact forces at a smaller scale. Input variables, such as the actuation force and tendon return force, were determined experimentally. A dynamic computer model of the hand system was then created using Recurdyn. This was used to predict the contact (grasping) force of the fingers at full-scale, half-scale, and quarter-scale. Correlation between the computer model and physical testing was achieved for both a life-size and half-scale finger assembly. To further demonstrate the scalability of the hand design, both half and quarter-scale robotic hand rapid prototype assemblies were built using 3D printing techniques. This thesis work identified the point where further miniaturization would require a change in the manufacturing process to micro-fabrication. Several techniques were compared as potential methods for making a production intent quarter-scale robotic hand. Investment casting, Swiss machining, and Selective Laser Sintering were the manufacturing techniques considered. A quarter-scale robotic hand tested the limits of each technology. Below this scale, micro-machining would be required. The break point for the current actuation method, air muscles, was also explored. Below the quarter-scale, an alternative actuation method would also be required. Electroactive Polymers were discussed as an option for the micro-scale. In summary, a dynamic model of the RIT robotic grasping hand was created and validated as scalable at full and half-scales. The model was then used to predict finger contact forces at the quarter-scale. The quarter-scale was identified as the break point in terms of the current RIT robotic grasping hand based on both manufacturing and actuation. A novel, prototype quarter-scale robotic hand assembly was successfully built by an additive manufacturing process, a high resolution 3D printer. However, further miniaturization would require alternate manufacturing techniques and actuation mechanisms

Splittable metamorphic carrier robots

International audienceMetamorphic modular robots are versatile systems composed of a set of independent modules. These modules are able to deliberately change their overall topology in order to adapt to new circumstances, perform new tasks, or recover from damage. The modules considered in this paper are cubic shapes, and we assume that each of them has a separate computational resources and it is equipped with a specialized sensors to perceive the environment. In this paper, we demonstrate the ability of these robots to evolve the topology of the whole structure in order to achieve, surround and transport target objects dispersed in the environment. While performing its task, the robot may be splitted into several parts in order to cope with environmental variations. Our work integrates a simplified model of biological hormone system to generate inputs for a finite-state machine (FSM) that controls the evolution process

Spatio-temporal patterns act as computational mechanisms governing emergent behavior in robotic swarms

Our goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their self-coordinating emergent behavior, has proven ineffective, largely due to the swarm's inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micro-macro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm's emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Emergent behaviors in a bio-inspired platform controlled by a physical cellular automata cluster

This work illustrates behavior patterns and trajectories of a bio-inspired artificial platform induced by a cellular automata (CA)-based control strategy. The platform embeds both CA control as physical electronic architecture and a distributed hardware layer as effectors. In this work, we test both the functionality of the novel hardware’s components as well as the device’s capabilities in locomotion tasks. We also observe the trajectories and patterns emerging from different initial states of the CA excitation and hardware configurations. Two main result sets emerge from this study: the first set illustrates different trajectories according to different initial excitation of the physical CA controller layer. The second set suggests the potential of the developed platform for generating complex patterns of control, as well as indicating emergent characteristics similar to those common to morphological computation approaches in generating localized perturbations without affecting or notifying the central controller

Actuation, Sensing And Control For Micro Bio Robots

The continuing trend in miniaturization of technology, advancements in micro and nanofabrication and improvements in high-resolution imaging has enabled micro- and meso-scale robots that have many applications. They can be used for micro-assembly, directed drug delivery, microsurgery and high-resolution measurement. In order to create microrobots, microscopic sensors, actuators and controllers are needed. Unique challenges arise when building microscale robots. For inspiration, we look toward highly capable biological organisms, which excel at these length scales. In this dissertation we develop technologies that combine biological components and synthetic components to create actuation, sensing and assembly onboard microrobots. For actuation, we study the dynamics of synthetic micro structures that have been integrated with single-cell biological organisms to provide un-tethered onboard propulsion to the microrobot. For sensing, we integrate synthetically engineered sensor cells to enable a system capable of detecting a change in the local environment, then storing and reporting the information. Furthermore, we develop a bottom-up fabrication method using a macroscopic magnetic robot to direct the assembly of inorganic engineered micro structures. We showcase the capability of this assembly method by demonstrating highly-specified, predictable assembly of microscale building blocks in a semi-autonomous experiment. These magnetic robots can be used to program the assembly of passive building blocks, with the building blocks themselves having the potential to be arbitrarily complex. We extend the magnetic robot actuation work to consider control algorithms for multiple robots by exploiting spatial gradients of magnetic fields. This thesis makes contributions toward actuation, sensing and control of autonomous micro systems and provides technologies that will lead to the development of swarms of microrobots with a suite of manipulation and sensing capabilities working together to sense and modify the environment

Review on Nanorobot as a Nanomachine and Biomedicine

Nanorobotics is the technology of producing robots or machines with very small scale or Miniscale of a nanometer (10-9 meters), machines constructed at the molecular level (Nano machines) may Be used to detect or identify and cure the human body of its various diseases like cancer. Nano robots are Very good accuracy they perform a specific task with great accuracy and precision at very small scale or Nanoscale dimension. A recent discovery in the field of drug Delivery is target therapy, which improves the diagnostic tests and Medical devices. Nanotechnology is going to revolutionize the world. According to the National Nanotechnology Initiative (NNI). Nowadays these nano robots play a vital role in the field of Bio Medicine. In the pharma-world, the applications of Nanotechnology mean drugs containing nano-sized active ingredients. They are well used to cure HIV, Cancer, Surgery, Bloodstream, gene therapy, Kidney stone removal and other harmful disease they Can restore lost tissue at the cellular level, useful for monitoring, Diagnosing and fighting sickness. The main purpose is to cure many dreadful Diseases in human body

A Scalable Strategy for Open Loop Magnetic Control of Microrobots Using Critical Points

A novel scalable strategy for open loop control of ferromagnetic microrobots on a plane using a scalable array of electromagnets is presented. Instead of controlling the microrobot directly, we create equilibrium points in the magnetic force field that are stable and attractive on the plane in which the microrobot is to be controlled. The microrobot moves into these equilibrium points rapidly in presence of low viscous forces, and thus controlling the equilibrium points let us control the microrobot precisely. An unit/cell in the array of electromagnets allows precise control of the microrobot in the unit/cell’s domain. Motion synthesis across multiple overlapping domains allows control of the microrobot in large regions across the array. We perform numerical analysis and demonstrate the control of the ferromagnetic microrobot using the proposed method through simulations