Online hyper-evolution of controllers in multirobot systems

In this paper, we introduce online hyper-evolution (OHE) to accelerate and increase the performance of online evolution of robotic controllers. Robots executing OHE use the different sources of feedback information traditionally associated with controller evaluation to find effective evolutionary algorithms and controllers online during task execution. We present two approaches: OHE-fitness, which uses the fitness score of controllers as the criterion to select promising algorithms over time, and OHE-diversity, which relies on the behavioural diversity of controllers for algorithm selection. Both OHE-fitness and OHE-diversity are distributed across groups of robots that evolve in parallel. We assess the performance of OHE-fitness and of OHE-diversity in two foraging tasks with differing complexity, and in five configurations of a dynamic phototaxis task with varying evolutionary pressures. Results show that our OHE approaches: (i) outperform multiple state-of-the-art algorithms as they facilitate controllers with superior performance and faster evolution of solutions, and (ii) can increase effectiveness at different stages of evolution by combining the benefits of multiple algorithms over time. Overall, our study shows that OHE is an effective new paradigm to the synthesis of controllers for robots.info:eu-repo/semantics/acceptedVersio

Evolutionary Robotics

info:eu-repo/semantics/publishedVersio

Evolutionary online behaviour learning and adaptation in real robots

Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.info:eu-repo/semantics/publishedVersio

R-HybrID: Evolution of agent controllers with a hybridisation of indirect and direct encodings

Neuroevolution, the optimisation of artificial neural networks (ANNs) through evolutionary computation, is a promising approach to the synthesis of controllers for autonomous agents. Traditional neuroevolution approaches employ direct encodings, which are limited in their ability to evolve complex or large-scale controllers because each ANN parameter is independently optimised. Indirect encodings, on the other hand, facilitate scalability because each gene can be reused multiple times to construct the ANN, but are biased towards regularity and can become ineffective when irregularity is required. To address such limitations, we introduce a novel algorithm called R-HybrID. In R-HybrID, controllers have both indirectly encoded and directly encoded structure. Because the portion of structure following a specific encoding is under evolutionary control, R-HybrID can automatically find an appropriate encoding combination for a given task. We assess the performance of R-HybrID in three tasks: (i) a high-dimensional visual discrimination task that requires geometric principles to be evolved, (ii) a challenging benchmark for modular robotics, and (iii) a memory task that has proven difficult for current algorithms because it requires effectively accumulating neural structure for cognitive behaviour to emerge. Our results show that R-HybrID consistently outperforms three stateof-the-art neuroevolution algorithms, and effectively evolves complex controllers and behaviours.info:eu-repo/semantics/publishedVersio

A parameter for quantitative analysis of plasticity induced crack closure

Numerical models have been successfully developed to predict plasticity induced crack closure (PICC). However, despite the large research effort a full understanding of the links between physical parameters, residual plastic wake and PICC has not been achieved yet. The plastic extension of material behind crack tip, Δyp, obtained by the integration of vertical plastic deformation perpendicularly to crack flank, is proposed here to quantify the residual plastic field. The values of Δyp and PICC were obtained numerically in a M(T) specimen using the finite element method. An excellent correlation was found between PICC and Δyp which indicates that this parameter controls the phenomenon, and can be used to quantify the effect of physical parameters. An empirical model was developed to predict PICC assuming that the residual plastic field is a set of vertical plastic wedges, that the linear superposition principle applies and that the influence of a particular wedge exponentially decreases with distance to crack tip. The model was applied successfully to predict PICC for different residual plastic fields which provided an additional validation of Δyp as the parameter controlling PICC

Stochastic Optimization for Network-Constrained Power System Scheduling Problem

The stochastic nature of demand and wind generation has a considerable effect on solving the scheduling problem of a modern power system. Network constraints such as power flow equations and transmission capacities also need to be considered for a comprehensive approach to model renewable energy integration and analyze generation system flexibility. Firstly, this paper accounts for the stochastic inputs in such a way that the uncertainties are modeled as normally distributed forecast errors. The forecast errors are then superimposed on the outputs of load and wind forecasting tools. Secondly, it efficiently models the network constraints and tests an iterative algorithm and a piecewise linear approximation for representing transmission losses in mixed integer linear programming (MILP). It also integrates load shedding according to priority factors set by the system operator. Moreover, the different interactions among stochastic programming, network constraints, and prioritized load shedding are thoroughly investigated in the paper. The stochastic model is tested on a power system adopted from Jeju Island, South Korea. Results demonstrate the impact of wind speed variability and network constraints on the flexibility of the generation system. Further analysis shows the effect of loss modeling approaches on total cost, accuracy, computational time, and memory requirement

odNEAT: an algorithm for decentralised online evolution of robotic controllers

Online evolution gives robots the capacity to learn new tasks and to adapt to changing environmental conditions during task execution. Previous approaches to online evolution of neural controllers are typically limited to the optimisation of weights in networks with a prespecified, fixed topology. In this article, we propose a novel approach to online learning in groups of autonomous robots called odNEAT. odNEAT is a distributed and decentralised neuroevolution algorithm that evolves both weights and network topology. We demonstrate odNEAT in three multirobot tasks: aggregation, integrated navigation and obstacle avoidance, and phototaxis. Results show that odNEAT approximates the performance of rtNEAT, an efficient centralised method, and outperforms IM-( mu + 1), a decentralised neuroevolution algorithm. Compared with rtNEAT and IM( mu + 1), odNEAT's evolutionary dynamics lead to the synthesis of less complex neural controllers with superior generalisation capabilities. We show that robots executing odNEAT can display a high degree of fault tolerance as they are able to adapt and learn new behaviours in the presence of faults. We conclude with a series of ablation studies to analyse the impact of each algorithmic component on performance.info:eu-repo/semantics/submittedVersio

Pressure effect in the X-ray intrinsic position resolution in noble gases and mixtures

A study of the gas pressure effect in the position resolution of an interacting X- or gamma-ray photon in a gas medium is performed. The intrinsic position resolution for pure noble gases (Argon and Xenon) and their mixtures with CO2 and CH4 were calculated for several gas pressures (1-10bar) and for photon energies between 5.4 and 60.0 keV, being possible to establish a linear match between the intrinsic position resolution and the inverse of the gas pressure in that energy range. In order to evaluate the quality of the method here described, a comparison between the available experimental data and the calculated one in this work, is done and discussed. In the majority of the cases, a strong agreement is observed

Femtocell deployment in LTE-A networks: A sustainability, economical and capacity analysis

Increasingly mobile data traffic and high quality service demand has driven standard developments and new mobile technologies deployment at an unprecedented level. Long Term Evolution (LTE) standard and its improved version LTE-Advanced (LTE-A) are two technology standards developed to cope with high levels of mobile data traffic demand. However, traffic and revenue disparity still is a reality, suggesting that traditional network deployment methods - based mainly on macro cellular sites - might prove to be cost ineffective in the long term. From another perspective, and increasingly important for mobile network operators, revenue is also a function of each mobile network deployment's sustainability. This work aims to comprehensively elaborate on those matters by presenting four specific scenarios with a comparative analysis of both macro and femtocell deployments (single and both technology networks). For each scenario, capacity, cost effectiveness and expected carbon emissions are the evaluated key indicators. This kind of analysis provides mobile networks operators with relevant information, enabling them to sustainably adapt business and provisioning models as well as network deployment strategies to current and future technological standards, while minimizing capital and operational expenditure (CAPEX/OPEX). The main contribution is that in short term, mixed macro and femtocell deployment scenarios are the most cost effective and sustainable option, while in mid to long term, as data traffic demand rises, femtocell deployments become the most sustainable, not only from economical and environmental points of view, but also from network coverage stand point.info:eu-repo/semantics/acceptedVersio