Content uploaded by M. Anthony Lewis
Author content
All content in this area was uploaded by M. Anthony Lewis on May 20, 2015
Content may be subject to copyright.
The Behavioral Self-organization Of Nanorobots Using Local Rules
Abstract
Not Available
... Numerous research findings and algorithms have been presented with the purpose of targeted drug delivery in cancer patience with the use of nanoparticle swarms. In 1992, Lewis and Bekey published landmark research on implementation of a nanobot swarm with chemical sensors to destroy malignant brain tissue [59]. Chandrasekaran and Hougen et al [60], present a swarm of bionanobots that use quorum sensing to co-ordinate. ...
... The techniques are compared and evaluated based on the convergence time (time taken by the algorithm to eliminate the tumour cells) and the convergence rate (number of cancer cells an algorithm eliminates versus the iterations required) by changing the number of nano bees. From the studies, it is found that the algorithm in [8] had yielded much better results than in [59] for all scenarios. ...
An in-vitro nano-swarm has the potential to revolutionize the medical field, from the diagnosis of ailments to targeted delivery of drugs. A nanobot swarm uses swarm intelligence to allow simple devices to communicate in a decentralized, distributed manner, within any given environment to achieve a common goal or arrive at an optimal solution quickly. This review discusses the different factors to consider and components necessary to deploy an in-vitro nano-swarm for targeted drug delivery to eliminate cancerous cells in the brain. It also discusses some swarming techniques that can be implemented for the decentralized control and acoustic communication between individuals in the nanobot swarm. Finally, there is discussion about the different challenges faced while deploying a nanobot swarm in the brain. This review hopes to give direction to other researchers through a comprehensive compilation about different practices and advancements in the field.
... Cavalcanti et al. suggested several nanorobot architectures for medical nanorobotics [4,5,6,7]. In [8] swarm intelligence and chemical signaling techniques for nanorobots have been applied to the problem of drug delivery. Inspired by the theory from [8], Chandrasekaran and Hougen developed control techniques for nanorobot swarm intelligence [9], where the motion planning strategy is used to achieve swarm intelligent behaviors to achieve particular tasks. ...
... In [8] swarm intelligence and chemical signaling techniques for nanorobots have been applied to the problem of drug delivery. Inspired by the theory from [8], Chandrasekaran and Hougen developed control techniques for nanorobot swarm intelligence [9], where the motion planning strategy is used to achieve swarm intelligent behaviors to achieve particular tasks. ...
paper, a control algorithm is proposed for pH sensitive nanorobots to deliver drugs in the tumor area while navigating in the blood stream environment. The nanorobots are able to communicate with their neighbors using the Particle Swarm Optimization algorithm. Furthermore, the obstacle avoidance algorithm allows the nanorobots to avoid collision with the blood cells. Each nanorobot can measure the pH value at its current position using sensors. Through cooperation, the nanorobots can drive the swarm to the tumor, which is defined by a certain pH value (less than 7.4). When the nanorobots locate the tumor cells, they release the drug which will raise the pH value of the cell until it is destroyed. The graphical interface simulations have shown the effectiveness of the proposed algorithm.
... Obviously, the form-factor of these nanodevices will be of prime importance, again yielding THz band communication as one of the most suitable communication paradigms. The number of mobile medical nanodevices is expected to be very large (up to a billion according to some estimations [93]) for some applications (e.g., for tissue engineering or detecting bacteria via swarms of sub-millimeter-scale nanodevices). ...
Recent developments in nanotechnology herald nanometer-sized devices expected to bring light to a number of groundbreaking applications. Communication with and among nanodevices will be needed for unlocking the full potential of such applications. As the traditional communication approaches cannot be directly applied in nanocommunication, several alternative paradigms have emerged. Among them, electromagnetic nanocommunication in the terahertz (THz) frequency band is particularly promising, mainly due to the breakthrough of novel materials such as graphene. For this reason, numerous research efforts are nowadays targeting THz band nanocommunication and consequently nanonetworking. As it is expected that these trends will continue in the future, we see it beneficial to summarize the current status in these research domains. In this survey, we therefore aim to provide an overview of the current THz nanocommunication and nanonetworking research. Specifically, we discuss the applications envisioned to be supported by nanonetworks operating in the THz band, together with the requirements such applications pose on the underlying nanonetworks. Subsequently, we provide an overview of the current contributions on the different layers of the protocol stack, as well as the available channel models and experimentation tools. Finally, we identify a number of open research challenges and outline several future research directions.
... The work was conducted using the Khepera robotics platform [191]. Nanorobots, that are a popular topic of interest nowadays, was discussed by [192], in 1992. The authors dealt with the idea of using pheromones in order to organize nanorobots. ...
Swarm Robotics is an emerging field of adapting the phenomenon of natural swarms to robotics. It is a study of robots that are aimed to mimic natural swarms, like ants and birds, to form a system that is scalable, flexible, and robust. These robots show self-organization, autonomy, cooperation, and coordination amongst themselves. The cost and design complexity factor is aimed to keep low, hence trying to form systems that are very much similar to natural swarms. The robots operate without any central entity to control them, and the communication amongst the robots can either be direct (robot-to-robot) or indirect (robot-to-environment). Swarm robotics has a wide range of application fields, from simple household tasks to military missions. This paper reviews the swarm robotics approach from its history to its future. It discusses the basic idea of swarm robotics, its important features, simulators, projects, real life applications and some future ideas.
Swarm Robotics is an emerging field of adapting the phenomenon of natural swarms to robotics and a study of robots to mimic natural swarms, like ants and birds, to form a scalable, flexible, and robust system. These robots show self-organization, autonomy, cooperation, and coordination amongst themselves. Additionally, their cost and design complexity factor must be as low as possible to reach systems similar to natural swarms. Further, the communication amongst the robots can either be direct (robot-to-robot) or indirect (robot-to-environment) and without any central entity to control them. Swarm robotics has a wide range of application fields, from simple household tasks to military missions. This paper reviews the swarm robotics approaches from its history to its future based on 217 references. It highlights the prominent pioneers of swarm robotics and enlights the initial swarm robotics methods. The presence of swarm robotics is shown based on simulators, projects, and real-life applications. For the future, this paper presents visions and ideas of swarm robotics.
In the last few years, it was proposed to deliver drugs using Nano-robots for treating cancer. This paper compares between two recent and efficient algorithms for delivering Nano-robots to cancer area. These algorithms are Jaya algorithm and Directed Particle Swarm Optimization (DPSO) algorithm. In this paper, we also propose a new hybrid algorithm that combines Jaya and DPSO to speed up the process of Nano-robots delivery. The proposed algorithm is called Directed Jaya (DJaya) algorithm. Experiments have proved that the efficiency of DJaya is higher than both Jaya and DPSO. We show experimentally that DJaya starts delivering Nano-robots earlier than DPSO to facilitate the initiation of the drug release. Also, DJaya finishes delivering Nano-robots earlier than Jaya to complete the drug dose. In addition to this, DJaya groups the Nano-robots together in the target area like DPSO to speed up the drug release process. We finally propose a new strategy for destroying cancer cells efficiently with relatively small number of Nano-robots. This strategy can save 40% of Nano-robots.
Recently, it has been proposed to use Nano-robots in cancer treatment. These Nano-robots are injected in the human body. Then they travel through the blood vessels searching for cancer cells. After reaching these cells, the Nano-robots release the drug to destroy only the infected cells while leaving the uninfected ones untouched. In this way we can overcome the drawbacks of the traditional methods for treating cancer (chemo-therapy and radio-therapy). In this paper we apply a powerful algorithm called Teaching Learning Based Optimization (TLBO) to deliver a swarm of Nano-robots to their target. Then we compare this algorithm with the Directed Particle Swarm Optimization (DPSO) algorithm for the same problem.
Cancer is one of the most dangerous diseases in this century. There are two traditional methods for treating cancer (radio-therapy and chemo-therapy). Using these methods causes very harmful side effects on healthy organs. Because of these side effects, doctors sometimes need to decrease the drug dose or delay the therapy. Some researchers proposed to use Nano-robots to deliver anti-cancer drugs to only cancer cells without touching the healthy tissues. One of the most recently proposed algorithms for delivering Nano-robots to cancer area is Directed Particle Swarm Optimization (DPSO) [1]. This algorithm can efficiently deliver all Nano-robots to the target area in a very short time. In this paper, we deeply discuss this algorithm and we also propose a new method for controlling this algorithm by tuning its direct step.
Enhancing the energy efficiency and maximizing the networking lifetime are the major challenges in Wireless Sensor Networks (WSN).Swarm Intelligence based algorithms are very efficient in solving nonlinear design problems with real-world applications.In this paper a Swarm based Fruit Fly Optimization Algorithm (FFOA) with the concept of K-Medoid clustering and swapping is implemented to increase the energy efficiency and lifetime of WSN. A comparative analysis is performed in terms of cluster compactness,cluster error and convergence. MATLAB Simulation results show that K-Medoid Swapping and Bunching Fruit Fly optimization (KMSB-FFOA) outperforms FFOA and K-Medoid Fruit Fly Optimization Algorithm (KM-FFOA).
Interdigitated finger (comb) structures are demonstrated to be effective for exciting electrostatically the resonance of polysilicon microstructures parallel to the plane of the substrate. Linear plates suspended by a folded-cantilever truss and torsional plates suspended by spiral and serpentine springs are fabricated from a 2 μm-thick phosphorus-doped low-pressure chemical-vapor-deposited (LPCVD) polysilicon film. Resonance is observed visually, with frequencies ranging from 18 kHz to 80 kHz and quality factors from 20 to 130. Simple beam theory is adequate for calculating the resonance frequencies, using a Young's modulus of 140 GPa and neglecting residual strain in the released structures.
A new type of actuator for a microminiature pump is presented. The pressure of air in a cavity, raised by resistive heating, deflects a thin silicon membrane. The dynamics of membrane deflection is studied experimentally, the results being in excellent agreement with simulation. We conclude that the device is suitable as an actuator in a micro-miniature pump.
This paper reports on the state of our research toward a general coordination mechanism for distributed intelligent systems. In our view, a coordination framework or organization is a particular set of settled and unsettled questions about belief and action that agents have about other agents. Organizational change means opening and/or settling some different set of questions, giving individual agents new problems to solve and, more importantly, different assumptions about the beliefs and actions of other agents. To test these ideas we are developing a testbed called the Intelligent Coordination Experiment (ICE) in which we implement our coordination mechanisms.
Using the conventional MOS planar process, miniature cantilever and doubly supported mechanical beams are fabricated from
polycrystalline silicon. Poly‐Si micromechanical beams having thicknesses of 230 nm to 2.3 μm and separated by 550 nm to 3.5μm
from the substrate are made in a wide range of lengths and widths. Two static mechanical properties are investigated: the
dependences of maximum free‐standing length and beam deflection on the thickness of the beam. By annealing the poly‐Si prior
to beam formation, both of these properties are improved. Nonuniform internal stress in the poly‐Si is apparently responsible
for the beam deflection.
A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
We describe the design, fabrication and operation of several micromotors that have been produced using integrated-circuit processing. Both rotors and stators for these motors, which are driven by electrostatic forces, are formed from polycrystalline silicon 1.0–1.5 μm thick. The diameters of the rotors in the motors tested are between 60 and 120 μm. Motors with several friction-reducing designs have been fabricated using phosphosilicate glass (PSG) as a sacrificial material and either one or three polysilicon depositions.
1st issued as an Oxford Univ. Press Paperback