24 research outputs found
In Vivo computation - Where computing meets nanosytem for smart tumor biosensing
According to World Health Organization, 13.1 million people will die in the world just because of cancer by 2030. Early tumor detection is very crucial to saving the world from this alarming mortality rate. However, it is an insurmountable challenge for the existing medical imaging techniques with limited imaging resolution to detect microscopic tumors. Hence, the need of the hour is to explore novel cross-disciplinary strategies to solve this problem. The rise of nanotechnologies provides a strong belief to solve complex medical problems such as early tumor detection. Nanoparticles with sizes ranging between 1-100 nanometers can be used as contrast agents. Their small sizes enable them to leak out of blood vessels and accumulate within tumors. Moreover, their chemical, optical, magnetic and electronic properties also change at nanoscale, which make them an ideal probing agent to spatially highlight the tumor site. Though, using nanoparticles to target malignant tumors is a promising concept, only 0.7% of the injected nanoparticles reach the tumor according to the statistical results of last 10 years.
In PhD work, we proposed novel in vivo computational frameworks for fast, accurate and robust nanobiosensing. Specifically, the peritumoral region corresponds to the “objective function”; the tumor is the “global optimum”; the region of interest is the “domain” of the objective function; and the nanoswimmers are the “computational agents” (i.e., guesses or optimization variables). First, in externally manipulable in vivo computation, nanoswimmers are used as contrast agents to probe the region of interest. The observable characteristics of these nanoswimmers, under the influence of tumor-induced biological gradients, are utilized by the external tracking system to steer nanoswimmers towards the possible tumor direction. To take it one step ahead, we provide solutions to the real-life constraints of in vivo natural computation such as uniformity of the external steering force and finite life span of the nanoswimmers. To overcome these challenges, we propose a multi-estimate-fusion strategy to obtain a common steering direction for the swarm of nanoswimmers and an iterative memory-driven gradient descent optimization strategy for faster tumor sensitization.
Next, we proposed a parallel framework called autonomous in vivo computation, where the tumor sensitization is highly scalable and tracking-free. We demonstrate that the tumor-triggered biophysical gradients can be leveraged by nanoparticles to collectively move toward the potential tumor hypoxic regions without the aid of any external intervention. Although individual nanoparticles have no target-directed locomotion ability due to limited communication and computation capability, we showed that once passive collaboration is achieved, they can successfully avoid obstacles and detect the tumor.
Finally, to address the respective limitations of externally manipulable and autonomous settings such as constant monitoring and slow detection, we proposed a semi-autonomous in vivo computational framework. We showed that the spot sampling strategy for an autonomous swarm of nanoswimmers can achieve faster tumor sensitization in complex environments. This approach makes the swarm highly scalable along with giving it the freedom from constant monitoring.
The performance of the aforementioned tumor sensitization frameworks is evaluated through comprehensive in silico experiments that mimic the realistic targeting processes in externally manipulable, self-regulatable and semi-autonomous settings. The efficacies of the proposed frameworks are demonstrated through numerical simulations that incorporate various physical constraints with respect to controlling and steering of computational agents, their motion in discretized vascular networks and their motion under the influence of disturbance and noise
Magnetically Driven Micro and Nanorobots
Manipulation and navigation of micro and nanoswimmers in different fluid environments can be achieved by chemicals, external fields, or even motile cells. Many researchers have selected magnetic fields as the active external actuation source based on the advantageous features of this actuation strategy such as remote and spatiotemporal control, fuel-free, high degree of reconfigurability, programmability, recyclability, and versatility. This review introduces fundamental concepts and advantages of magnetic micro/nanorobots (termed here as "MagRobots") as well as basic knowledge of magnetic fields and magnetic materials, setups for magnetic manipulation, magnetic field configurations, and symmetry-breaking strategies for effective movement. These concepts are discussed to describe the interactions between micro/nanorobots and magnetic fields. Actuation mechanisms of flagella-inspired MagRobots (i.e., corkscrew-like motion and traveling-wave locomotion/ciliary stroke motion) and surface walkers (i.e., surface-assisted motion), applications of magnetic fields in other propulsion approaches, and magnetic stimulation of micro/nanorobots beyond motion are provided followed by fabrication techniques for (quasi)spherical, helical, flexible, wire-like, and biohybrid MagRobots. Applications of MagRobots in targeted drug/gene delivery, cell manipulation, minimally invasive surgery, biopsy, biofilm disruption/eradication, imaging-guided delivery/therapy/surgery, pollution removal for environmental remediation, and (bio)sensing are also reviewed. Finally, current challenges and future perspectives for the development of magnetically powered miniaturized motors are discussed
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Light modulation of electric field driven semiconductor micromotors
The future micro/nanorobots require high degrees of freedom in motion control to perform complex tasks by individuals or by a swarm. It remains a great challenge to control the motions of an individual nanomachine amidst many, to switch the operation modes facilely, and it is even more difficult to actuate several components of a nanomachine coordinately for purposed actions. This high degree of versatility is essential for the future micro/nanorobots and requires investigation of innovative actuation mechanisms. In this dissertation, we report our recent finding about a new approach combining two types of stimulation to achieve such goal. The micromotors being studied are made of semiconductor silicon nanowires. Mechanical motion of the motors is driven by several types of AC electric field. Meanwhile, the electrical property of the nanowires can be locally and instantaneously modulated by visible light illumination in a reversible manner. We demonstrate that visible light is able to change the electric polarization of semiconductor nanowires under AC electric field, and reflected by the dramatic change of mechanical motions with very rich configurations. Under a rotating electric field, the rotation speed of semiconductor Si nanowires in electric fields can instantly increase, decrease, and even reverse the orientation by light illumination in the visible to infrared regime at various AC E-field frequencies. Under a linear AC electric field, instantaneous change of alignment direction and speed of semiconductor nanowires is observed under visible-light exposure. With theoretical analysis and simulation, the working principle can be attributed to the optically tuned imaginary-part (out-phase) and real-part (in-phase) electrical polarization of a semiconductor nanowire in aqueous suspension. Based on the understanding of this system, we further propose a new approach to control the semiconductor micromotor via light tunable dielectrophoresis. Localized control of collective behavior in a highly density silicon nanowire suspension is also investigated. Finally, we demonstrated how to utilize the mechanical motion at microscale for practical application of biosensing.Materials Science and Engineerin
Evolutionary Robot Swarms Under Real-World Constraints
Tese de doutoramento em Engenharia Electrotécnica
e de Computadores, na especialidade de Automação e Robótica, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraNas últimas décadas, vários cientistas e engenheiros têm vindo a estudar as estratégias provenientes da natureza. Dentro das arquiteturas biológicas, as sociedades que vivem em enxames revelam que agentes simplistas, tais como formigas ou pássaros, são capazes de realizar tarefas complexas usufruindo de mecanismos de cooperação. Estes sistemas abrangem todas as condições necessárias para a sobrevivência, incorporando comportamentos de cooperação, competição e adaptação. Na “batalha” sem fim em prol do progresso dos mecanismos artificiais desenvolvidos pelo homem, a ciência conseguiu simular o primeiro comportamento em enxame no final dos anos oitenta. Desde então, muitas outras áreas, entre as quais a robótica, beneficiaram de mecanismos de tolerância a falhas inerentes da inteligência coletiva de enxames.
A área de investigação deste estudo incide na robótica de enxame, consistindo num domínio particular dos sistemas robóticos cooperativos que incorpora os mecanismos de inteligência coletiva de enxames na robótica. Mais especificamente, propõe-se uma solução completa de robótica de enxames a ser aplicada em contexto real. Nesta ótica, as operações de busca e salvamento foram consideradas como o caso de estudo principal devido ao nível de complexidade associado às mesmas. Tais operações ocorrem tipicamente em cenários dinâmicos de elevadas dimensões, com condições adversas que colocam em causa a aplicabilidade dos sistemas robóticos cooperativos. Este estudo centra-se nestes problemas, procurando novos desafios que não podem ser ultrapassados através da simples adaptação da literatura da especialidade em algoritmos de enxame, planeamento, controlo e técnicas de tomada de decisão.
As contribuições deste trabalho sustentam-se em torno da extensão do método Particle Swarm Optimization (PSO) aplicado a sistemas robóticos cooperativos, denominado de Robotic Darwinian Particle Swarm Optimization (RDPSO). O RDPSO consiste numa arquitetura robótica de enxame distribuída que beneficia do particionamento dinâmico da população de robôs utilizando mecanismos evolucionários de exclusão social baseados na sobrevivência do mais forte de Darwin. No entanto, apesar de estar assente no caso de estudo do RDPSO, a aplicabilidade dos conceitos aqui propostos não se encontra restrita ao mesmo, visto que todos os algoritmos parametrizáveis de enxame de robôs podem beneficiar de uma abordagem idêntica.
Os fundamentos em torno do RDPSO são introduzidos, focando-se na dinâmica dos robôs, nos constrangimentos introduzidos pelos obstáculos e pela comunicação, e nas suas propriedades evolucionárias. Considerando a colocação inicial dos robôs no ambiente como algo fundamental para aplicar sistemas de enxames em aplicações reais, é assim introduzida uma estratégia de colocação de robôs realista. Para tal, a população de robôs é dividida de forma hierárquica, em que são utilizadas plataformas mais robustas para colocar as plataformas de enxame no cenário de forma autónoma. Após a colocação dos robôs no cenário, é apresentada uma estratégia para permitir a criação e manutenção de uma rede de comunicação móvel ad hoc com tolerância a falhas. Esta estratégia não considera somente a distância entre robôs, mas também a qualidade do nível de sinal rádio frequência, redefinindo assim a sua aplicabilidade em cenários reais. Os aspetos anteriormente mencionados estão sujeitos a uma análise detalhada do sistema de comunicação inerente ao algoritmo, para atingir uma implementação mais escalável do RDPSO a cenários de elevada complexidade. Esta elevada complexidade inerente à dinâmica dos cenários motivaram a ultimar o desenvolvimento do RDPSO, integrando para o efeito um mecanismo adaptativo baseado em informação contextual (e.g., nível de atividade do grupo).
Face a estas considerações, o presente estudo pode contribuir para expandir o estado-da-arte em robótica de enxame com algoritmos inovadores aplicados em contexto real. Neste sentido, todos os métodos propostos foram extensivamente validados e comparados com alternativas, tanto em simulação como com robôs reais. Para além disso, e dadas as limitações destes (e.g., número limitado de robôs, cenários de dimensões limitadas, constrangimentos reais limitados), este trabalho contribui ainda para um maior aprofundamento do estado-da-arte, onde se propõe um modelo macroscópico capaz de capturar a dinâmica inerente ao RDPSO e, até certo ponto, estimar analiticamente o desempenho coletivo dos robôs perante determinada tarefa.
Em suma, esta investigação pode ter aplicabilidade prática ao colmatar a lacuna que se faz sentir no âmbito das estratégias de enxames de robôs em contexto real e, em particular, em cenários de busca e salvamento.Over the past decades, many scientists and engineers have been studying nature’s best and time-tested
patterns and strategies. Within the existing biological architectures, swarm societies revealed that
relatively unsophisticated agents with limited capabilities, such as ants or birds, were able to cooperatively
accomplish complex tasks necessary for their survival. Those simplistic systems embrace all
the conditions necessary to survive, thus embodying cooperative, competitive and adaptive behaviours.
In the never-ending battle to advance artificial manmade mechanisms, computer scientists simulated
the first swarm behaviour designed to mimic the flocking behaviour of birds in the late eighties.
Ever since, many other fields, such as robotics, have benefited from the fault-tolerant mechanism
inherent to swarm intelligence.
The area of research presented in this Ph.D. Thesis focuses on swarm robotics, which is a particular
domain of multi-robot systems (MRS) that embodies the mechanisms of swarm intelligence
into robotics. More specifically, this Thesis proposes a complete swarm robotic solution that can be
applied to real-world missions. Although the proposed methods do not depend on any particular application,
search and rescue (SaR) operations were considered as the main case study due to their
inherent level of complexity. Such operations often occur in highly dynamic and large scenarios, with
harsh and faulty conditions, that pose several problems to MRS applicability. This Thesis focuses on
these problems raising new challenges that cannot be handled appropriately by simple adaptation of
state-of-the-art swarm algorithms, planning, control and decision-making techniques.
The contributions of this Thesis revolve around an extension of the Particle Swarm Optimization
(PSO) to MRS, denoted as Robotic Darwinian Particle Swarm Optimization (RDPSO). The RDPSO
is a distributed swarm robotic architecture that benefits from the dynamical partitioning of the whole
swarm of robots by means of an evolutionary social exclusion mechanism based on Darwin’s survival-of-the-fittest.
Nevertheless, although currently applied solely to the RDPSO case study, the applicability
of all concepts herein proposed is not restricted to it, since all parameterized swarm robotic
algorithms may benefit from a similar approach The RDPSO is then proposed and used to devise the applicability of novel approaches. The fundamentals
around the RDPSO are introduced by focusing on robots’ dynamics, obstacle avoidance,
communication constraints and its evolutionary properties. Afterwards, taking the initial deployment
of robots within the environment as a basis for applying swarm robotics systems into real-world applications,
the development of a realistic deployment strategy is proposed. For that end, the population
of robots is hierarchically divided, wherein larger support platforms autonomously deploy
smaller exploring platforms in the scenario, while considering communication constraints and obstacles.
After the deployment, a way of ensuring a fault-tolerant multi-hop mobile ad hoc communication
network (MANET) is introduced to explicitly exchange information needed in a collaborative realworld
task execution. Such strategy not only considers the maximum communication range between
robots, but also the minimum signal quality, thus refining the applicability to real-world context. This
is naturally followed by a deep analysis of the RDPSO communication system, describing the dynamics
of the communication data packet structure shared between teammates. Such procedure is a
first step to achieving a more scalable implementation by optimizing the communication procedure
between robots. The highly dynamic characteristics of real-world applications motivated us to ultimate
the RDPSO development with an adaptive strategy based on a set of context-based evaluation
metrics.
This thesis contributes to the state-of-the-art in swarm robotics with novel algorithms for realworld
applications. All of the proposed approaches have been extensively validated in benchmarking
tasks, in simulation, and with real robots. On top of that, and due to the limitations inherent to those
(e.g., number of robots, scenario dimensions, real-world constraints), this Thesis further contributes
to the state-of-the-art by proposing a macroscopic model able to capture the RDPSO dynamics and,
to some extent, analytically estimate the collective performance of robots under a certain task. It is
the author’s expectation that this Ph.D. Thesis may shed some light into bridging the reality gap
inherent to the applicability of swarm strategies to real-world scenarios, and in particular to SaR operations.FCT - SFRH/BD /73382/201
Medical Imaging of Microrobots: Toward In Vivo Applications
Medical microrobots (MRs) have been demonstrated for a variety of non-invasive biomedical applications, such as tissue engineering, drug delivery, and assisted fertilization, among others. However, most of these demonstrations have been carried out in in vitro settings and under optical microscopy, being significantly different from the clinical practice. Thus, medical imaging techniques are required for localizing and tracking such tiny therapeutic machines when used in medical-relevant applications. This review aims at analyzing the state of the art of microrobots imaging by critically discussing the potentialities and limitations of the techniques employed in this field. Moreover, the physics and the working principle behind each analyzed imaging strategy, the spatiotemporal resolution, and the penetration depth are thoroughly discussed. The paper deals with the suitability of each imaging technique for tracking single or swarms of MRs and discusses the scenarios where contrast or imaging agent's inclusion is required, either to absorb, emit, or reflect a determined physical signal detected by an external system. Finally, the review highlights the existing challenges and perspective solutions which could be promising for future in vivo applications
A comprehensive survey on hybrid communication in context of molecular communication and terahertz communication for body-centric nanonetworks
With the huge advancement of nanotechnology over the past years, the devices are shrinking into micro-scale, even nano-scale. Additionally, the Internet of nano-things (IoNTs) are generally regarded as the ultimate formation of the current sensor networks and the development of nanonetworks would be of great help to its fulfilment, which would be ubiquitous with numerous applications in all domains of life. However, the communication between the devices in such nanonetworks is still an open problem. Body-centric nanonetworks are believed to play an essential role in the practical application of IoNTs. BCNNs are also considered as domain specific like wireless sensor networks and always deployed on purpose to support a particular application. In these networks, electromagnetic and molecular communications are widely considered as two main promising paradigms and both follow their own development process. In this survey, the recent developments of these two paradigms are first illustrated in the aspects of applications, network structures, modulation techniques, coding techniques and security to then investigate the potential of hybrid communication paradigms. Meanwhile, the enabling technologies have been presented to apprehend the state-of-art with the discussion on the possibility of the hybrid technologies. Additionally, the inter-connectivity of electromagnetic and molecular body-centric nanonetworks is discussed. Afterwards, the related security issues of the proposed networks are discussed. Finally, the challenges and open research directions are presented
An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots
The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so
Swarm Robotics
Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties