An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics

Steyven, Andreas; Hart, Emma; Paechter, Ben

doi:10.1145/3071178.3071232

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…The experiment includes 14 simulated scenarios (2 normal scenarios, 8 degraded motor scenarios including 4 faulty and 4 repaired, 4 wind disturbance scenarios including 2 faulty and 2 repaired). Simulated faults were chosen as representative of faults commonly found in real-world UAV deployments, such as densely distributed forests/buildings and extreme weather conditions, which can affect robot communication, spatial distributions and system reliability [11] [27]. The task for the swarm in all scenarios was distributed biased flocking with human-desired heading-direction "East".…”

Section: Discussionmentioning

confidence: 99%

Trust Repair in Human-Swarm Teams+

Cai

Lewis

et al. 2019

2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Swarm robots are coordinated via simple control laws to generate emergent behaviors such as flocking, rendezvous, and deployment. Human-swarm teaming has been widely proposed for scenarios, such as human-supervised teams of unmanned aerial vehicles (UAV) for disaster rescue, UAV and ground vehicle cooperation for building security, and soldier-UAV teaming in combat. Effective cooperation requires an appropriate level of trust, between a human and a swarm. When an UAV swarm is deployed in a real-world environment, its performance is subject to real-world factors, such as system reliability and wind disturbances. Degraded performance of a robot can cause undesired swarm behaviors, decreasing human trust. This loss of trust, in turn, can trigger human intervention in UAVs' task executions, decreasing cooperation effectiveness if inappropriate. Therefore, to promote effective cooperation we propose and test a trust-repairing method (Trust-repair) restoring performance and human trust in the swarm to an appropriate level by correcting undesired swarm behaviors. Faulty swarms caused by both external and internal factors were simulated to evaluate the performance of the Trustrepair algorithm in repairing swarm performance and restoring human trust. Results show that Trust-repair is effective in restoring trust to a level intermediate between normal and faulty conditions.

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Section: Discussionmentioning

confidence: 99%

Trust Repair in Human-Swarm Teams+

Cai

Lewis

et al. 2019

2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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“…Statistical analysis was conducted based on the method in [21] using a significance level of 5%. The distributions of two results were checked using a Shapiro-Wilk test.…”

Section: Methodsmentioning

confidence: 99%

Evolution of a functionally diverse swarm via a novel decentralised quality-diversity algorithm

Hart

Steyven

Paechter

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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The presence of functional diversity within a group has been demonstrated to lead to greater robustness, higher performance and increased problem-solving ability in a broad range of studies that includes insect groups, human groups and swarm robotics. Evolving group diversity however has proved challenging within Evolutionary Robotics, requiring reproductive isolation and careful attention to population size and selection mechanisms. To tackle this issue, we introduce a novel, decentralised, variant of the MAP-Elites illumination algorithm which is hybridised with a well-known distributed evolutionary algorithm (mEDEA). The algorithm simultaneously evolves multiple diverse behaviours for multiple robots, with respect to a simple token-gathering task. Each robot in the swarm maintains a local archive defined by two pre-specified functional traits which is shared with robots it come into contact with. We investigate four different strategies for sharing, exploiting and combining local archives and compare results to mEDEA. Experimental results show that in contrast to previous claims, it is possible to evolve a functionally diverse swarm without geographical isolation, and that the new method outperforms mEDEA in terms of the diversity, coverage and precision of the evolved swarm.

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“…in swarm robotics. For example, evolutionary algorithms are used in [30] to adjust the parameters of a robot's ruleset during the course of a 'lifetime' to cope with a dynamically changing environment. [31] use a distributed algorithm based on an artificial-immune system algorithm (AIS) within a swarm robotics application to enable robots to adapt their individual foraging strategies over time based on available resources, environmental conditions and behaviours of other robots to maximise foraging.…”

Section: Bio-inspired Computationmentioning

confidence: 99%

For Flux Sake: The Confluence of Socially- and Biologically-Inspired Computing for Engineering Change in Open Systems

Pitt¹,

Hart²

2017

2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W)

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This position paper is concerned with the challenge of engineering multi-scale and long-lasting systems, whose operation is regulated by sets of mutually-agreed, conventional rules. The core of the problem is that there are multiple, inter-dependent dimensions of flux, with numerous contextual factors to take into account. These dimensions of flux include, on the one hand, the set of rules itself; and on the other, the system components (population), their social network, and the operating environment. However, there appears to be no 'one size fits all' optimum ruleset for all combinations of population, social network and environment; nor (given the contextual factors) is there a planning-type algorithm that can compute an 'ideal' ruleset for any particular combination of population, social network and environment. These features of the problem suggest that recent advances in machine learning and evolutionary computation can provide the instruments for facilitating self-adaptation of a rule-based system over different timescales. This paper proposes that the integration of concepts from socially-and biologically-inspired computing can pave the way for eventual development of a computational framework that will enable principled (methodological) development of sustainable adaptive rule-based systems.

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An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics

Cited by 5 publications

References 20 publications

Trust Repair in Human-Swarm Teams+

Trust Repair in Human-Swarm Teams+

Evolution of a functionally diverse swarm via a novel decentralised quality-diversity algorithm

For Flux Sake: The Confluence of Socially- and Biologically-Inspired Computing for Engineering Change in Open Systems

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