- Economics of swarm robotics in UK agriculture
- swarm robotics research papers
- Cost and complexity of swarm robots
Self-assembly at the macroscopic scale. We have reviewed half a century of research on the design of systems displaying physical self-assembly of macroscopic components.
Economics of swarm robotics in UK agriculture
We report on the experience gained in the design of 21 such systems, exhibiting components ranging from passive mechanical parts to mobile robots. We present a taxonomy of the systems and discuss design principles and functions. Finally, we summarize the main achievements and indicate potential directions for future research.
Simple learning rules to cope with changing environments. We consider an agent that must choose repeatedly among several actions. Each action has a certain probability of giving the agent an energy reward, and costs may be associated with switching between actions. The agent does not know which action has the highest reward probability, and the probabilities change randomly over time. We study two learning rules that have been widely used to model decision-making processes in animals - one deterministic and the other stochastic.
Over relatively short periods of time, both rules are successful in enabling agents to exploit their environment. Moreover, under a range of effective learning rates, both rules are equivalent, and can be expressed by a third rule that requires the agent to select the action for which the current run of unsuccessful trials is shortest.
swarm robotics research papers
However, the performance of both rules is relatively poor over longer periods of time, and under most circumstances no better than the performance an agent could achieve without knowledge of the environment. We propose a simple extension to the original rules that enables agents to learn about and effectively exploit a changing environment for an unlimited period of time.
Cooperation in groups of solitary or social individuals evolution of self-assembly. We simulate a system of simple, insect-like robots that can move autonomously and grasp objects as well as each other. We use artificial evolution to produce solitary transport and group transport behaviors. We show that robots, even though not aware of each other, can be effective in group transport.
Group transport can even be performed by robots that behave as in solitary transport. Still, robots engaged in group transport can benefit from behaving differently from robots engaged in solitary transport. Moreover, we provide evidence that self-assembly can provide adaptive value to individuals that compete in an artificial evolution based on task performance. Self-assembly with swarm-bot. We report on experiments in which we study the process that leads a group of robots to self-assemble.
In particular, we present results of experiments in which we vary the number of robots up to 16 physical robots, and up to in simulation , their starting configurations, and the properties of the terrain on which self-assembly takes place. In view of the very successful experimental results, swarm-bot qualifies as the current state of the art in autonomous self-assembly. Autonomous Self-assembly in a Swarm-bot Proc. Collective choices in house-hunting ants. Many decisions involve a trade-off between commitment and flexibility. We show here that the collective decisions ants make over new nest sites are sometimes sufficiently flexible that the ants can change targets even after an emigration has begun.
Our findings suggest that, in this context, the ants' procedures are such that they can sometimes avoid 'negative information cascades' which might lock them into a poor choice. The ants are more responsive to belated good news of a higher quality nest than they are when the nest they had initially chosen degraded to become worse than an alternative.
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Our study confirms, in a new way, that ant colonies can be very powerful 'search engines'. Division of labor in self-organized groups.
Cost and complexity of swarm robots
In social insect colonies, many tasks are performed by higher-order group or team entities, whose task solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher-order entities. We report on an experimental study in which a team of physical robots performs a foraging task.
They make little use of memory and take actions purely on the basis of local information. Our study advances the current state of the art in swarm robotics with regard to the number of real-world robots engaging in teamwork up to 12 robots in the most challenging experiment.
To the best of our knowledge, in this paper we present the first self-organised system of robots that displays a dynamical hierarchy of teamwork with cooperation also occurring among higher-order entities. Our study shows that teamwork requires neither individual recognition nor differences between individuals.
This result might also contribute to the ongoing debate on the role of these characteristics in the division of labour in social insects. Cooperative transport of objects of different shapes and sizes. We examine the ability of a swarm robotic system to transport cooperatively objects of different shapes and sizes. We simulate a group of autonomous mobile robots that can physically connect to each other and to the transported object. Controllers - artificial neural networks - are synthesised by an evolutionary algorithm.
They are trained to let the robots self-assemble, that is, organise into collective physical structures, and transport the object towards a target location. We quantify the performance and the behaviour of the group. We show that the group can cope fairly well with objects of different geometries as well as with sudden changes in the target location. Moreover, we show that larger groups, which are made of up to 16 robots, make possible the transport of heavier objects.
Finally, we discuss the limitations of the system in terms of task complexity, scalability, and fault tolerance, and point out potential directions for future research. Towards group transport by swarms of robots Int. Object transport by modular robots that self-assemble. We present a first attempt to accomplish a simple object manipulation task using the self-reconfigurable robotic system swarm-bot. The modules are autonomous in perception, control, action, and power. We present quantitative results, obtained with six physical modules, that confirm the utility of self-assembling robots in a concrete task.
Self-assembly with a super-mechano Colony. We show that a control algorithm for autonomous self-assembly can be ported from a source multi-robot platform i. Although there are substantial differences between the two robotic platforms, it is possible to qualitatively reproduce the functionality of the source platform on the target platform. Therefore, the transfer does neither require modifications in the hardware nor an extensive redesign of the control. The results of a set of experiments demonstrate that a controller that was developed for the source platform lets robots of the target platform self-assemble with high reliability.
Evolving self-organizing behaviors. In this paper, we introduce a self-assembling and self-organizing artifact, called a swarm-bot, composed of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other. We discuss the challenges involved in controlling a swarm-bot and address the problem of synthesizing controllers for the swarm-bot using artificial evolution.
Specifically, we study aggregation and coordinated motion of the swarm-bot using a physics-based simulation of the system. Experiments, using a simplified simulation model of the s-bots, show that evolution can discover simple but effective controllers for both the aggregation and the coordinated motion of the swarm-bot. Springer, Cham. Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. However, several of the heterogeneous characteristics that make them ideal for certain future applications—robot autonomy, decentralized control, collective emergent behavior—hinder the evolution of the technology from academic institutions to real-world problems.
Blockchain, an emerging technology originated in the Bitcoin field, demonstrates that by combining peer-to-peer networks with cryptographic algorithms a group of agents can reach an agreement on a particular state of affairs and record that agreement without the need for a controlling authority. The combination of blockchain with other distributed systems, such as robotic swarm systems, can provide the necessary capabilities to make robotic swarm operations more secure, autonomous, flexible and even profitable.
This work explains how blockchain technology can provide innovative solutions to four emergent issues in the swarm robotics research field. New security, decision making, behavior differentiation and business models for swarm robotic systems are described by providing case scenarios and examples. Finally, limitations and possible future problems that arise from the combination of these two technologies are described. The last couple of years have seen a torrent of innovation focused on the creation of blockchain technologies and protocols.
Dorigo, M. Swarm Robotics LNCS, vol. Sahin, E. Swarm Robotics WS Kennedy, J. Beni, G. Colorni, A. Min, T. Minhui, W. Hongxing, W. Vaughan, R. Swarm Intelligence, — Google Scholar. Zhiguo, S. CAAI T. Desai, J.