Evolution of Swarm Robotics Systems with Novelty Search

Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a predefined objective. The lack of a predefined objective precludes premature convergence caused by a deceptive fitness function. In this paper, we apply novelty search combined with NEAT to the evolution of neural controllers for homogeneous swarms of robots. Our empirical study is conducted in simulation, and we use a common swarm robotics task - aggregation, and a more challenging task - sharing of an energy recharging station. Our results show that novelty search is unaffected by deception, is notably effective in bootstrapping the evolution, can find solutions with lower complexity than fitness-based evolution, and can find a broad diversity of solutions for the same task. Even in non-deceptive setups, novelty search achieves solution qualities similar to those obtained in traditional fitness-based evolution. Our study also encompasses variants of novelty search that work in concert with fitness-based evolution to combine the exploratory character of novelty search with the exploitatory character of objective-based evolution. We show that these variants can further improve the performance of novelty search. Overall, our study shows that novelty search is a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final publication will be available at link.springer.co

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

An approach to evolve and exploit repertoires of general robot behaviours

Recent works in evolutionary robotics have shown the viability of evolution driven by behavioural novelty and diversity. These evolutionary approaches have been successfully used to generate repertoires of diverse and high-quality behaviours, instead of driving evolution towards a single, task-specific solution. Having repertoires of behaviours can enable new forms of robotic control, in which high-level controllers continually decide which behaviour to execute. To date, however, only the use of repertoires of open-loop locomotion primitives has been studied. We propose EvoRBC-II, an approach that enables the evolution of repertoires composed of general closed-loop behaviours, that can respond to the robot's sensory inputs. The evolved repertoire is then used as a basis to evolve a transparent higher-level controller that decides when and which behaviours of the repertoire to execute. Relying on experiments in a simulated domain, we show that the evolved repertoires are composed of highly diverse and useful behaviours. The same repertoire contains sufficiently diverse behaviours to solve a wide range of tasks, and the EvoRBC-II approach can yield a performance that is comparable to the standard tabula-rasa evolution. EvoRBC-II enables automatic generation of hierarchical control through a two-step evolutionary process, thus opening doors for the further exploration of the advantages that can be brought by hierarchical control.info:eu-repo/semantics/acceptedVersio

Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics

Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe

Evolution of behaviour trees for collective transport with robot swarms

Swarm robotics, inspired by natural swarms, studies how simple robots with only local sensing capabilities and no centralised control may cooperate to achieve a common goal in a robust, flexible and scalable way. A robotic system with such properties constitutes an interesting alternative to the platforms currently used in warehouses and distribution plants, where workers are at risk of injury and the space and budget available for complex infrastructure is limited. Swarm behaviours are emergent, which makes the task of designing the controllers of the individual robots particularly challenging. In this work, we propose a method for a swarm of industrial robots to collectively transport items that are too heavy for a single agent to carry. We use artificial evolution to evolve behaviour tree controllers for the swarm agents and we conceive a decentralised coordination strategy based on local messaging. The method is developed and tested in a simulated environment, using a combination of freely available open source libraries. The results show that a homogeneous swarm equipped with our solution is able to successfully find the items placed in the environment and transport them back to a nest region. We suggest further tuning of the evolutionary parameters and the introduction of noise in the simulator in order to improve the observed performance of the controllers in simulation and their expected performance the real worldObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur