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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)
Probabilistic and Distributed Control of a Large-Scale Swarm of Autonomous Agents
We present a novel method for guiding a large-scale swarm of autonomous
agents into a desired formation shape in a distributed and scalable manner. Our
Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC)
algorithm adopts an Eulerian framework, where the physical space is partitioned
into bins and the swarm's density distribution over each bin is controlled.
Each agent determines its bin transition probabilities using a
time-inhomogeneous Markov chain. These time-varying Markov matrices are
constructed by each agent in real-time using the feedback from the current
swarm distribution, which is estimated in a distributed manner. The PSG-IMC
algorithm minimizes the expected cost of the transitions per time instant,
required to achieve and maintain the desired formation shape, even when agents
are added to or removed from the swarm. The algorithm scales well with a large
number of agents and complex formation shapes, and can also be adapted for area
exploration applications. We demonstrate the effectiveness of this proposed
swarm guidance algorithm by using results of numerical simulations and hardware
experiments with multiple quadrotors.Comment: Submitted to IEEE Transactions on Robotic
Random Finite Set Theory and Optimal Control of Large Collaborative Swarms
Controlling large swarms of robotic agents has many challenges including, but
not limited to, computational complexity due to the number of agents,
uncertainty in the functionality of each agent in the swarm, and uncertainty in
the swarm's configuration. This work generalizes the swarm state using Random
Finite Set (RFS) theory and solves the control problem using Model Predictive
Control (MPC) to overcome the aforementioned challenges. Computationally
efficient solutions are obtained via the Iterative Linear Quadratic Regulator
(ILQR). Information divergence is used to define the distance between the swarm
RFS and the desired swarm configuration. Then, a stochastic optimal control
problem is formulated using a modified L2^2 distance. Simulation results using
MPC and ILQR show that swarm intensities converge to a target destination, and
the RFS control formulation can vary in the number of target destinations. ILQR
also provides a more computationally efficient solution to the RFS swarm
problem when compared to the MPC solution. Lastly, the RFS control solution is
applied to a spacecraft relative motion problem showing the viability for this
real-world scenario.Comment: arXiv admin note: text overlap with arXiv:1801.0731
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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)
Consensus reaching in swarms ruled by a hybrid metric-topological distance
Recent empirical observations of three-dimensional bird flocks and human
crowds have challenged the long-prevailing assumption that a metric interaction
distance rules swarming behaviors. In some cases, individual agents are found
to be engaged in local information exchanges with a fixed number of neighbors,
i.e. a topological interaction. However, complex system dynamics based on pure
metric or pure topological distances both face physical inconsistencies in low
and high density situations. Here, we propose a hybrid metric-topological
interaction distance overcoming these issues and enabling a real-life
implementation in artificial robotic swarms. We use network- and
graph-theoretic approaches combined with a dynamical model of locally
interacting self-propelled particles to study the consensus reaching pro- cess
for a swarm ruled by this hybrid interaction distance. Specifically, we
establish exactly the probability of reaching consensus in the absence of
noise. In addition, simulations of swarms of self-propelled particles are
carried out to assess the influence of the hybrid distance and noise
Synthesis of formation control for an aquatic swarm robotics system
Formations are the spatial organization of objects or entities according to some
predefined pattern. They can be found in nature, in social animals such as fish
schools, and insect colonies, where the spontaneous organization into emergent
structures takes place. Formations have a multitude of applications such as in
military and law enforcement scenarios, where they are used to increase operational
performance. The concept is even present in collective sports modalities such as
football, which use formations as a strategy to increase teams efficiency.
Swarm robotics is an approach for the study of multi-robot systems composed
of a large number of simple units, inspired in self-organization in animal societies.
These have the potential to conduct tasks too demanding for a single robot operating alone. When applied to the coordination of such type of systems, formations
allow for a coordinated motion and enable SRS to increase their sensing efficiency
as a whole.
In this dissertation, we present a virtual structure formation control synthesis
for a multi-robot system. Control is synthesized through the use of evolutionary
robotics, from where the desired collective behavior emerges, while displaying key-features such as fault tolerance and robustness. Initial experiments on formation
control synthesis were conducted in simulation environment. We later developed
an inexpensive aquatic robotic platform in order to conduct experiments in real world conditions.
Our results demonstrated that it is possible to synthesize formation control for
a multi-robot system making use of evolutionary robotics. The developed robotic
platform was used in several scientific studies.As formações consistem na organização de objetos ou entidades de acordo com
um padrão pré-definido. Elas podem ser encontradas na natureza, em animais
sociais tais como peixes ou colónias de insetos, onde a organização espontânea
em estruturas se verifica. As formações aplicam-se em diversos contextos, tais
como cenários militares ou de aplicação da lei, onde são utilizadas para aumentar
a performance operacional. O conceito está também presente em desportos coletivos tais como o futebol, onde as formações são utilizadas como estratégia para
aumentar a eficiência das equipas.
Os enxames de robots são uma abordagem para o estudo de sistemas multi-robô
compostos de um grande número de unidades simples, inspirado na organização
de sociedades animais. Estes têm um elevado potencial na resolução de tarefas demasiado complexas para um único robot. Quando aplicadas na coordenação deste
tipo de sistemas, as formações permitem o movimento coordenado e o aumento da
sensibilidade do enxame como um todo.
Nesta dissertação apresentamos a síntese de controlo de formação para um sistema multi-robô. O controlo é sintetizado através do uso de robótica evolucionária,
de onde o comportamento coletivo emerge, demonstrando ainda funcionalidadeschave tais como tolerância a falhas e robustez. As experiências iniciais na síntese de controlo foram realizadas em simulação. Mais tarde foi desenvolvida uma
plataforma robótica para a condução de experiências no mundo real.
Os nossos resultados demonstram que é possível sintetizar controlo de formação
para um sistema multi-robô, utilizando técnicas de robótica evolucionária. A
plataforma desenvolvida foi ainda utilizada em diversos estudos científicos
Body swarm interface (BOSI) : controlling robotic swarms using human bio-signals
Traditionally robots are controlled using devices like joysticks, keyboards, mice and other
similar human computer interface (HCI) devices. Although this approach is effective and
practical for some cases, it is restrictive only to healthy individuals without disabilities,
and it also requires the user to master the device before its usage. It becomes complicated and non-intuitive when multiple robots need to be controlled simultaneously with these traditional devices, as in the case of Human Swarm Interfaces (HSI).
This work presents a novel concept of using human bio-signals to control swarms of
robots. With this concept there are two major advantages: Firstly, it gives amputees and
people with certain disabilities the ability to control robotic swarms, which has previously
not been possible. Secondly, it also gives the user a more intuitive interface to control
swarms of robots by using gestures, thoughts, and eye movement.
We measure different bio-signals from the human body including Electroencephalography
(EEG), Electromyography (EMG), Electrooculography (EOG), using off the shelf
products. After minimal signal processing, we then decode the intended control action
using machine learning techniques like Hidden Markov Models (HMM) and K-Nearest
Neighbors (K-NN). We employ formation controllers based on distance and displacement
to control the shape and motion of the robotic swarm. Comparison for ground truth for
thoughts and gesture classifications are done, and the resulting pipelines are evaluated with both simulations and hardware experiments with swarms of ground robots and aerial vehicles
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