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A dynamic colour perception system for autonomous robot navigation on unmarked roads

NEUROCOMPUTING 2018年 275卷 2251-2263页

作者： Narayan, Aparajit Tuci, Elio Labrosse, Frederic Alkilabi, Muhanad H. Mohammed Aberystwyth Univ Aberystwyth Dyfed Wales Middlesex Univ London England Univ Karbala Karbala Iraq

Navigation on unmarked and possible poorly delineated roads where the boundaries between the road and the non-road surfaces are not clearly indicated is a particularly challenging task for autonomous vehicles. The results of this study show that fairly robust navigation strategies can be generated by a robot equipped with a dynamic active-vision based control system represented by an artificial neural network synthesized using evolutionary computation techniques. In the experiments described in this paper, a simulated Pioneer robot is required to visually navigate multiple poorly delineated roads that differ in terms of variations in luminance and/or chrominance between the road and the adjacent non-road areas. Low resolution camera images are processed by a mechanism that continuously adjusts the contribution of each component of a three dimensional colour model (e.g., R, G and B) to the generation of the robot perceptual experience. We show that the best controller can successfully drive a simulated Pioneer robot in environments with colour characteristics never encountered during the design phase, and operate with colour models never used during training. We show that the dynamic differential weighting of the colour components is underpinned by a complex pattern of neural activity that allows the robot to successfully adapt its perceptual system to the colour characteristics of different visual scenes. We also show that the controller can be easily ported onto real hardware, by showing the results of a series of tests with a physical Pioneer robot required to navigate various poorly delineated pedestrian roads. (c) 2017 Published by Elsevier B.V.

关键词： Active-vision Road-following evolutionary robotics

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Multi-phase sumo maneuver learning

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ROBOTICA 2004年第1期22卷 61-75页

作者： Liu, JM Zhang, SW Hong Kong Baptist Univ Dept Comp Sci Kowloon Hong Kong Peoples R China Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Beijing Peoples R China

In this paper, we demonstrate a multi-phase genetic programming (MPGP) approach to an autonomous robot learning task, where a sumo wrestling robot is required to execute specialized pushing maneuvers in response to different opponents' postures. The sumo robot used has a very simple, minimalist hardware configuration. This example differs from the earlier studies in evolutionary robotics in that the former is carried out on-line during the performance of a robot, whereas the latter is concerned with the evolution of a controller in a simulated environment based on extended genetic algorithms. As illustrated in several sumo maneuver learning experiments, strategic maneuvers with respect to some possible changes in the shape and size of an opponent can readily emerge from the on-line MPGP learning sessions.

关键词： multi-phase genetic programming (MPGP) autonomous robots sumo tasks maneuver learning evolutionary robotics

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Evolved Machines Shed Light on Robustness and Resilience

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PROCEEDINGS OF THE IEEE 2014年第5期102卷 899-914页

作者： Bongard, Josh Lipson, Hod Univ Vermont Dept Comp Sci Burlington VT 05405 USA Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

In biomimetic engineering, we may take inspiration from the products of biological evolution: we may instantiate biologically realistic neural architectures and algorithms in robots, or we may construct robots with morphologies that are found in nature. Alternatively, we may take inspiration from the process of evolution: we may evolve populations of robots in simulation and then manufacture physical versions of the most interesting or more capable robots that evolve. If we follow this latter approach and evolve both the neural and morphological subsystems of machines, we can perform controlled experiments that provide unique insight into how bodies and brains can work together to produce adaptive behavior, regardless of whether such bodies and brains are instantiated in a biological or technological substrate. In this paper, we review selected projects that use such methods to investigate the synergies and tradeoffs between neural architecture, morphology, action, and adaptive behavior.

关键词： Embodied cognition evolutionary robotics

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Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots

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ARTIFICIAL LIFE 2009年第4期15卷 465-484页

作者： Ampatzis, Christos Tuci, Elio Trianni, Vito Christensen, Anders Lyhne Dorigo, Marco European Space Agcy Adv Concepts Team Estec NL-2200 AG Noordwijk Netherlands ISTC CNR I-00185 Rome Italy DCTI ISCTE P-1649026 Lisbon Portugal Univ Libre Bruxelles CoDE IRIDIA B-1050 Brussels Belgium

This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamic interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research work in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination.

关键词： Self-assembly role allocation neural network artificial evolution evolutionary robotics

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Uflibrary: A simulation library implementing the utility function in autonomous robots

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2007年第3期16卷 507-536页

作者： Peterson, Jimmy Wahde, Mattias Chalmers Univ Technol Dept Appl Mech S-41296 Gothenburg Sweden

A simulation software package (UF Library) implementing the utility function (UF) method for behavior selection in autonomous robots, is introduced and described by means of an example involving a simple exploration robot equipped with a repertoire of five different behaviors. The UF Library (as indeed the UF method itself) is aimed at providing a rapid yet reliable and generally applicable procedure for generating behavior selection systems for autonomous robots, while at the same time minimizing the amount of hand-coding related to the activation of behaviors. It is demonstrated how the UF Library allows a user to rapidly implement individual behaviors and to set up and carry out simulations of a robot in its arena, in order to generate and optimize, by means of an evolutionary algorithm,the behavior selection system of the robot.

关键词： behavior-based robotics utility function behavioral organization autonomous robots evolutionary robotics

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Active Categorical Perception of Object Shapes in a Simulated Anthropomorphic Robotic Arm

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2010年第6期14卷 885-899页

作者： Tuci, Elio Massera, Gianluca Nolfi, Stefano Italian Natl Res Council Inst Cognit Sci & Technol I-00185 Rome Italy Univ Plymouth Plymouth PL4 8AA Devon England

Active perception refers to a theoretical approach to the study of perception grounded on the idea that perceiving is a way of acting, rather than a process whereby the brain constructs an internal representation of the world. The operational principles of active perception can be effectively tested by building robot-based models in which the relationship between perceptual categories and the body-environment interactions can be experimentally manipulated. In this paper, we study the mechanisms of tactile perception in a task in which a neuro-controlled anthropomorphic robotic arm, equipped with coarse-grained tactile sensors, is required to perceptually categorize spherical and ellipsoid objects. We show that best individuals, synthesized by artificial evolution techniques, develop a close to optimal ability to discriminate the shape of the objects as well as an ability to generalize their skill in new circumstances. The results show that the agents solve the categorization task in an effective and robust way by self-selecting the required information through action and by integrating experienced sensory-motor states over time.

关键词： Artificial neural networks categorical perception evolutionary robotics

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On building a person: benchmarks for robotic personhood

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2020年第4期32卷 581-600页

作者： Barresi, John Dalhousie Univ Dept Psychol & Neurosci 1355 Oxford StPOB 15000 Halifax NS B3H 4R2 Canada

I present criteria or benchmarks of personhood that robotic agents must pass for them to match us in social intelligence and indicate some current successes and difficulties for the future with respect to these criteria. I focus on five problematic areas: 1) How can inorganic material substances mimic organic processes?;2) How can robots evolve adaptations similar to those of human persons?;3) How can robots develop into adult personhood?;4) How can social identities of robots transform in an ever evolving robotic society?;5) How can robots display intellectual and moral ideals that can advance knowledge and social goals?

关键词： Personhood turing test social intelligence social robotics developmental robotics evolutionary robotics

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Optimisation of water demand forecasting by artificial intelligence with short data sets

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BIOSYSTEMS ENGINEERING 2019年 177卷 59-66页

作者： Gonzalez Perea, Rafael Camacho Poyato, Emilio Montesinos, Pilar Rodriguez Diaz, Juan Antonio Univ Cordoba Dept Agron Campus RabanalesEdif Vinci E-14071 Cordoba Spain Univ Cordoba Dept Rural Engn Campus RabanalesEdif Vinci E-14071 Cordoba Spain

Irrigated agriculture is one of the key factors responsible for decreasing freshwater availability in recent years. Thus, the development of new tools which will help Irrigation District managers in their daily decision making process about the use of water and energy is essential. On the other hand, the new era of Big Data and information and communications technologies (ICT) has made it possible to have a larger amount of information available, leading to the development of new prediction tools. However, the quality and quantity of this information in many fields such as irrigated agriculture is limited. Consequently, the way in which the development of new predictive models is addressed must be reformulated. Thus, in this work, a new methodology to provide short-term forecasting of daily irrigation water demand when data availability is limited has been developed by coupling dynamic Artificial Neural Networks (ANN) architecture, the Bayesian framework and Genetic Algorithms (GA). The methodology was applied in the Bembezar MD Irrigation District (Southern Spain). The developed model improved the prediction accuracy by between 3% and 11% with respect to previous work. The best ANN model had a Standard Error Prediction (SEP) and a determination coefficient (R-2) of 8.7% and 96%, respectively. The accuracy of the model developed makes it a powerful tool for the daily management of irrigation districts. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： evolutionary robotics Optimal forecasting models Short time series Irrigation

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Evolving a Behavioral Repertoire for a Walking Robot

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evolutionary COMPUTATION 2016年第1期24卷 59-88页

作者： Cully, A. Mouret, J. -B. Univ Paris 06 Sorbonne Univ UMR 7222 ISIR F-75005 Paris France CNRS UMR 7222 ISIR F-75005 Paris France

Numerous algorithms have been proposed to allow legged robots to learn to walk. However, most of these algorithms are devised to learn walking in a straight line, which is not sufficient to accomplish any real-world mission. Here we introduce the Transferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded by evolutionary processes, TBR-Evolution is substantially faster than independently evolving each controller. Our technique relies on two methods: (1) novelty search with local competition, which searches for both high-performing and diverse solutions, and (2) the transferability approach, which combines simulations and real tests to evolve controllers for a physical robot. We evaluate this new technique on a hexapod robot. Results show that with only a few dozen short experiments performed on the robot, the algorithm learns a repertoire of controllers that allows the robot to reach every point in its reachable space. Overall, TBR-Evolution introduced a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.

关键词： evolutionary algorithms evolutionary robotics mobile robotics behavioral repertoire exploration novelty search hexapod robot

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Joint Mechanical Design and Flight Control Optimization of a Nature-Inspired Unmanned Aerial Vehicle via Collaborative Co-Evolution

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IEEE robotics AND AUTOMATION LETTERS 2021年第2期6卷 2044-2051页

作者： Sufiyan, Danial Win, Luke Soe Thura Win, Shane Kyi Hla Soh, Gim Song Foong, Shaohui Singapore Univ Technol & Design SUTD Engn Prod Dev Pillar Singapore 487372 Singapore

In this work, a collaborative co-evolution approach is adopted to solve a joint physical design and feedback control optimization problem of a nature-inspired Unmanned Aerial Vehicle (UAV). Unlike traditional multirotors and fixed-wing aircraft, lift is achieved by spinning its entire body with attached aerofoils around a central axis and positional control is attained through regulation of 2 sets of independent aerodynamic surfaces and thrusters. The collaborative co-evolution process consists of 2 'species,' the first consisting of the mechanical design variables and the second consisting of Proportional-Integral-Derivative (PID) and central pattern generator (CPG) controller variables. Each species have their own respective individual evolutionary Algorithm (EA) solvers, Covariance Matrix Adaptation-evolutionary Strategy (CMA-ES) and Parameter Exploring Policy Gradients (PEPG). In each optimization iteration, the parameters of one species is combined with representatives with the highest fitness from the other species and fed into a shared model for fitness evaluation, with each species taking turns to send a representative. Detailed performance comparison in trajectory tracking and power consumption between the proposed jointly optimized system against a design-only optimized, control-only optimized and unoptimized baseline were conducted. It was found that configurations with optimized designs would draw on average 18% less power than the non-optimized designs, and configurations with optimized controllers reduce error by 56% on average. The best performing configuration is the one with jointly optimized mechanical design and controller which outperforms all other configurations individually and collectively.

关键词： Aerial systems mechanics and control evolutionary robotics methods and tools for robot system design

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