On the evolution of homogeneous two-robot teams: clonal versus aclonal approaches

This study compares two different evolutionary approaches (clonal and aclonal) to the design of homogeneous two-robot teams (i.e. teams of morphologically identical agents with identical controllers) in a task that requires the agents to specialise to different roles. The two approaches differ mainly in the way teams are formed during evolution. In the clonal approach, a team is formed from a single genotype within one population of genotypes. In the aclonal approach, a team is formed from multiple genotypes within one population of genotypes. In both cases, the goal is the synthesis of individual generalist controllers capable of integrating role execution and role allocation mechanisms for a team of homogeneous robots. Our results diverge from those illustrated in a similar comparative study, which supports the superiority of the aclonal versus the clonal approach. We question this result and its theoretical underpinning, and we bring new empirical evidence showing that the clonal outperforms the aclonal approach in generating homogeneous teams required to dynamically specialise for the benefit of the team. The results of our study suggest that task-specific elements influence the evolutionary dynamics more than the genetic relatedness of the team members. We conclude that the appropriateness of the clonal approach for role allocation scenarios is mainly determined by the specificity of the collective task, including the evaluation function, rather than by the way in which the solutions are evaluated during evolution

Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport

Simulated road following using neuroevolution

This paper describes a methodology wherein genetic algorithms were used to evolve neural network controllers for application in automatic road driving. The simulated controllers were capable of dynamically varying the mixture of colour components in the input image to ensure the ability to perform well across the entire range of possible environments. During the evolution phase, they were evaluated in a set of environments carefully designed to encourage the development of flexible and general-purpose solutions. Successfully evolved controllers were capable of navigating simulated roads across challenging test environments, each with different geometric and colour distribution properties. These controllers proved to be more robust and adaptable compared to the previous work done using this evolutionary approach. This was due to their improved dynamic colour perception capabilities, as they were now able to demonstrate feature extraction in three (red, green and blue) colour channels

Design and analysis of proximate mechanisms for cooperative transport in real robots

This paper describes a set of experiments in which a homogeneous group of real e-puck robots is required to coordinate their actions in order to transport cuboid objects that are too heavy to be moved by single robots. The agents controllers are dynamic neural networks synthesised through evolutionary computation techniques. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agent displacement on the x/y plane. In this object transport scenario, this sensor generates useful feedback on the consequences of the robot actions, helping the robots to perceive whether their pushing forces are aligned with the object movement. The results of our experiments indicated that the best evolved controller can effectively operate on real robots. The group transport strategies turned out to be robust and scalable to effectively operate in a variety of conditions in which we vary physical characteristics of the object and group cardinality. From a biological perspective, the results of this study indicate that the perception of the object movement could explain how natural organisms manage to coordinate their actions to transport heavy items

Evolutionary coordination system for fixed-wing communications unmanned aerial vehicles

A system to coordinate the movement of a group of un- manned aerial vehicles that provide a network backbone over mobile ground-based vehicles with communication needs is presented. Using evo- lutionary algorithms, the system evolves flying manoeuvres that position the aerial vehicles by fulfilling two key requirements; i) they maximise net coverage and ii) they minimise the power consumption. Experimental results show that the proposed coordination system is able to offer a de- sirable level of adaptability with respect to the objectives set, providing useful feedback for future research directions

Enhancing active vision system categorization capability through uniform local binary patterns

Previous research in Neuro-Evolution controlled Active Vision Systems has shown its potential to solve various shape categorization and discrimination problems. However, minimal investigation has been done in using this kind of evolved system in solving more complex vision problems. This is partly due to variability in lighting conditions, reflection, shadowing etc., which may be inherent to these kinds of problems. It could also be due to the fact that building an evolved system for these kinds of problems may be too computationally expensive. We present an Active Vision System controlled Neural Network trained by a Genetic Algorithm that can autonomously scan through an image pre-processed by Uniform Local Binary Patterns [8]. We demonstrate the ability of this system to categorize more complex images taken from the camera of a Humanoid (iCub) robot. Preliminary investigation results show that the proposed Uniform Local Binary Pattern [8] method performed better than the gray-scale averaging method of [1] in the categorization tasks. This approach provides a framework that could be used for further research in using this kind of system for more complex image problems

Robot swarm democracy: the importance of informed individuals against zealots

Abstract: In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents

Microwave-Hydrogen Peroxide Assisted Anaerobic Treatment as an Effective Method for Short-Chain Fatty Acids Production from Tannery Sludge

Tannery sludge is disposed of in landfills as it is considered a special residue by the Italian legislation, creating pollution and waste. This paper aims at evaluating the performance of the anaerobic fermentation process to obtain short-chain fatty acids (SCFAs) from this waste. The assessment of the most appropriate conditions, in terms of pH, temperature, initial total solids (TSs) content, and application of oxidizing-thermal pretreatment has been developed. The batch test trials revealed that the combined microwave and hydrogen peroxide (MW-H2O2) pretreatment followed by thermophilic conditions gave the best results, in terms of the acidification yield (0.31 gCOD(SCFA)/g VS0) and maximal SCFA concentration (above 26 g CODSCFA/L). In the tests conducted without pretreatment, the mesophilic temperature should be preferred since the acidification performances were comparable to or even better than their thermophilic counterparts. The SCFA composition analysis showed that in mesophilic fermentation, tannery sludge can generate up to 50% acetic acid (CODAc/CODSCFA), if previously pretreated (MW-H2O2). This research acts as a forerunner for the appropriate handling of this resource, to employ it for the development of a new tannery industry focused on a circular approach, rather than to simply dispose of it in landfills

Challenges and opportunities of water quality monitoring and multi-stakeholder management in small islands: the case of Santa Cruz, Galápagos (Ecuador)

Sustainable water resources management roots in monitoring data reliability and a full engagement of all institutions involved in the water sector. When competences and interests are overlapping, however, coordination may be difficult, thus hampering cooperative actions. This is the case of Santa Cruz Island (Galápagos, Ecuador). A comprehensive assessment on water quality data (physico-chemical parameters, major elements, trace elements and coliforms) collected since 1985 revealed the need of optimizing monitoring efforts to fill knowledge gaps and to better target decision-making processes. A Water Committee (Comité de la gestión del Agua) was established to foster the coordinated action among stakeholders and to pave the way for joint monitoring in the island that can optimize the efforts for water quality assessment and protection. Shared procedures for data collection, sample analysis, evaluation and data assessment by an open-access geodatabase were proposed and implemented for the first time as a prototype in order to improve accountability and outreach towards civil society and water users. The overall results reveal the high potential of a well-structured and effective joint monitoring approach within a complex, multi-stakeholder framework.publishedVersio

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe