2,668 research outputs found
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
Towards a Formal Verification Methodology for Collective Robotic Systems
We introduce a UML-based notation for graphically modeling
systemsā security aspects in a simple and intuitive
way and a model-driven process that transforms graphical
specifications of access control policies in XACML. These
XACML policies are then translated in FACPL, a policy
language with a formal semantics, and the resulting policies
are evaluated by means of a Java-based software tool
The role of the environment in collective perception:A generic complexity measure
We propose a novel generic information-theoretic framework for characterizing the task difficulty in the Collective Perception paradigm. Our formalism builds on the notion of Empowerment - a task-independent, universal and generic utility function, which characterizes the level of perceivable control an embodied agent has over its environment. Series of simulations with an empowerment model of the collective perception scenario revealed a significant correlation between the levels of empowerment and the accuracy demonstrated by a set of standard collective decision-making strategies and a recent state-of-the-art neural network controller on nine benchmark patterns, used previously for assessing swarm performance. The results elucidate the key role of both the agent embodiment and the environmental pattern in characterising task difficulty, and justify the application of empowerment to analytically assess this role, which could help predict swarm performance and support the development of more efficient decision-making strategies
Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning
Swarm systems constitute a challenging problem for reinforcement learning
(RL) as the algorithm needs to learn decentralized control policies that can
cope with limited local sensing and communication abilities of the agents.
While it is often difficult to directly define the behavior of the agents,
simple communication protocols can be defined more easily using prior knowledge
about the given task. In this paper, we propose a number of simple
communication protocols that can be exploited by deep reinforcement learning to
find decentralized control policies in a multi-robot swarm environment. The
protocols are based on histograms that encode the local neighborhood relations
of the agents and can also transmit task-specific information, such as the
shortest distance and direction to a desired target. In our framework, we use
an adaptation of Trust Region Policy Optimization to learn complex
collaborative tasks, such as formation building and building a communication
link. We evaluate our findings in a simulated 2D-physics environment, and
compare the implications of different communication protocols.Comment: 13 pages, 4 figures, version 2, accepted at ANTS 201
Construction task allocation through the collective perception of a dynamic environment
Building structures is a remarkable collective process but its automation remains an open challenge. Robot swarms provide a promising solution to this challenge. However, collective construction involves a number of difficulties regarding efficient robots allocation to the different activities, particularly if the goal is to reach an optimal construction rate. In this paper, we study an abstract construction scenario, where a swarm of robots is engaged in a collective perception process to estimate the density of building blocks around a construction site. The goal of this perception process is to maintain a minimum density of blocks available to the robots for construction. To maintain this density, the allocation of robots to the foraging task needs to be adjusted such that enough blocks are retrieved. Our results show a robust collective perception that enables the swarm to maintain a minimum block density under different rates of construction and foraging. Our approach leads the system to stabilize around a state in which the robots allocation allows the swarm to maintain a tile density that is close to or above the target minimum.info:eu-repo/semantics/publishedDorigo, M. StuĢtzle, T. Blesa, M. J. Blum, C. Hamann, H. Heinrich, M. K. & Strobel, V. (2020). Swarm Intelligence: 12th International Conference, ANTS 2020, Barcelona, Spain, October 26-28, 2020, Proceedings. Cham: Springer International Publishing AG
Evolutionary swarm robotics: a theoretical and methodological itinerary from individual neuro-controllers to collective behaviours
In the last decade, swarm robotics gathered much attention in the research community. By drawing inspiration from social insects and other self-organizing systems, it focuses on large robot groups featuring distributed control, adaptation, high robustness, and flexibility. Various reasons lay behind this interest in similar multi-robot systems. Above all, inspiration comes from the observation of social activities, which are based on concepts like division of labor, cooperation, and communication. If societies are organized in such a way in order to be more efficient, then robotic groups also could benefit from similar paradigms
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agentās structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments
Environment induced emergence of collective behaviour in evolving swarms with limited sensing
Designing controllers for robot swarms is challenging, because human
developers have typically no good understanding of the link between the details
of a controller that governs individual robots and the swarm behavior that is
an indirect result of the interactions between swarm members and the
environment. In this paper we investigate whether an evolutionary approach can
mitigate this problem. We consider a very challenging task where robots with
limited sensing and communication abilities must follow the gradient of an
environmental feature and use Differential Evolution to evolve a neural network
controller for simulated robots. We conduct a systematic study to measure the
flexibility and scalability of the method by varying the size of the arena and
number of robots in the swarm. The experiments confirm the feasibility of our
approach, the evolved robot controllers induced swarm behavior that solved the
task. We found that solutions evolved under the harshest conditions (where the
environmental clues were the weakest) were the most flexible and that there is
a sweet spot regarding the swarm size. Furthermore, we observed collective
motion of the swarm, showcasing truly emergent behavior that was not
represented in- and selected for during evolution.Comment: (1) Three authors contributed equally to this researc
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