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19th European Conference on the Applications of evolutionary Computation (EvoApplications)

作者： Duarte, Miguel Gomes, Jorge Costa, Vasco Oliveira, Sancho Moura Christensen, Anders Lyhne BioMachines Lab Lisbon Portugal Inst Telecomunicacoes Lisbon Portugal Inst Univ Lisboa ISCTE IUL Lisbon Portugal Fac Ciencias BioISI Lisbon Portugal

ISBN: (纸本)9783319311531

Control design is one of the prominent challenges in the field of swarm robotics. evolutionary robotics is a promising approach to the synthesis of self-organized behaviors for robotic swarms but it has, so far, only produced been shown in relatively simple collective behaviors. In this paper, we explore the use of a hybrid control synthesis approach to produce control for a swarm of aquatic surface robots that must perform an intruder detection task. The robots have to go to a predefined area, monitor it, detect and follow intruders, and manage their energy levels by regularly recharging at a base station. The hybrid controllers used in our experiments rely on evolved behavior primitives that are combined through a manually programmed high-level behavior arbitrator. In simulation, we show how simple modifications to the behavior arbitrator can result in different swarm behaviors that use the same underlying behavior primitives, and we show that the composed behaviors are scalable with respect to the swarm size. Finally, we demonstrate the synthesized controller in a real swarm of robots, and show that the behavior successfully transfers from simulation to reality.

关键词： Swarm robotics evolutionary robotics Hybrid control Self-organization Aquatic robots

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Evolving Controllers for Robots with Multimodal Locomotion 14th

Evolving Controllers for Robots with Multimodal Locomotion

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14th International Conference on the Simulation of Adaptive Behavior (SAB)

作者： Ramos, Rita Duarte, Miguel Oliveira, Sancho Moura Christensen, Anders Lyhne Inst Telecomunicacoes BioMachines Lab Lisbon Portugal IUL ISCTE Lisbon Portugal

ISBN: (纸本)9783319434889;9783319434872

Animals have inspired numerous studies on robot locomotion, but the problem of how autonomous robots can learn to take advantage of multimodal locomotion remains largely unexplored. In this paper, we study how a robot with two different means of locomotion can effective learn when to use each one based only on the limited information it can obtain through its onboard sensors. We conduct a series of simulation-based experiments using a task where a wheeled robot capable of jumping has to navigate to a target destination as quickly as possible in environments containing obstacles. We apply evolutionary techniques to synthesize neural controllers for the robot, and we analyze the evolved behaviors. The results show that the robot succeeds in learning when to drive and when to jump. The results also show that, compared with unimodal locomotion, multimodal locomotion allows for simpler and higher performing behaviors to evolve.

关键词： evolutionary robotics Multimodal locomotion Navigation task

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Self-Repair Techniques Point to Robots That Design Themselves

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COMMUNICATIONS OF THE ACM 2016年第2期59卷 15-17页

作者： Edwards, Chris

Robots are being taught to brainstorm alternatives when damaged.

关键词： ROBOTS RESEARCH REPAIRING COMPUTERS ALGORITHMS DESIGN & construction evolutionary robotics PERFORMANCE robotics research CONTROL systems THREE-dimensional printing

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Cooperative Coevolution of Control for a Real Multirobot System

Cooperative Coevolution of Control for a Real Multirobot Sys...

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14th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Gomes, Jorge Duarte, Miguel Mariano, Pedro Christensen, Anders Lyhne BioMachines Lab Lisbon Portugal Inst Telecomunicacoes Lisbon Portugal Univ Lisbon BioISI Fac Ciencias Lisbon Portugal Inst Univ Lisboa ISCTE IUL Lisbon Portugal

ISBN: (纸本)9783319458236;9783319458229

The potential of cooperative coevolutionary algorithms (CCEAs) as a tool for evolving control for heterogeneous multirobot teams has been shown in several previous works. The vast majority of these works have, however, been confined to simulation-based experiments. In this paper, we present one of the first demonstrations of a real multirobot system, operating outside laboratory conditions, with controllers synthesised by CCEAs. We evolve control for an aquatic multirobot system that has to perform a cooperative predator-prey pursuit task. The evolved controllers are transferred to real hardware, and their performance is assessed in a non-controlled outdoor environment. Two approaches are used to evolve control: a standard fitness-driven CCEA, and novelty-driven coevolution. We find that both approaches are able to evolve teams that transfer successfully to the real robots. Novelty-driven coevolution is able to evolve a broad range of successful team behaviours, which we test on the real multirobot system.

关键词： Cooperative coevolution evolutionary robotics Novelty search Reality gap Heterogeneous multirobot systems

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On-line Evolution of Foraging Behaviour in a Population of Real Robots 19th

On-line Evolution of Foraging Behaviour in a Population of R...

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19th European Conference on the Applications of evolutionary Computation (EvoApplications)

作者： Heinerman, Jacqueline Zonta, Alessandro Haasdijk, Evert Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands Univ Padua Padua Italy

ISBN: (纸本)9783319311531

This paper describes a study in evolutionary robotics conducted completely in hardware without using simulations. The experiments employ on-line evolution, where robot controllers evolve on-the-fly in the robots' environment as the robots perform their tasks. The main issue we consider is the feasibility of tackling a non-trivial task in a realistic timeframe. In particular, we investigate whether a population of six robots can evolve foraging behaviour in one hour. The experiments demonstrate that this is possible and they also shed light on some of the important features of our evolutionary system. Further to the specific results we also advocate the system itself. It provides an example of a replicable and affordable experimental set-up for other researches to engage in research into on-line evolution in a population of real robots.

关键词： evolutionary robotics Neural networks Distributed on-line learning Embodied evolution Foraging

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Rapid Phenotypic Landscape Exploration Through Hierarchical Spatial Partitioning

Rapid Phenotypic Landscape Exploration Through Hierarchical ...

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14th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Smith, Davy Tokarchuk, Laurissa Wiggins, Geraint Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

ISBN: (纸本)9783319458236;9783319458229

Exploration of the search space through the optimisation of phenotypic diversity is of increasing interest within the field of evolutionary robotics. Novelty search and the more recent MAP-Elites are two state of the art evolutionary algorithms which diversify low dimensional phenotypic traits for divergent exploration. In this paper we introduce a novel alternative for rapid divergent search of the feature space. Unlike previous phenotypic search procedures, our proposed Spatial, Hierarchical, Illuminated Neuro-Evolution (SHINE) algorithm utilises a tree structure for the maintenance and selection of potential candidates. SHINE penalises previous solutions in more crowded areas of the landscape. Our experimental results show that SHINE significantly outperforms novelty search and MAP-Elites in both performance and exploration. We conclude that the SHINE algorithm is a viable method for rapid divergent search of low dimensional, phenotypic landscapes.

关键词： Algorithm design Phenotypic diversity Neuroevolution evolutionary robotics

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Evolving Individual and Collective Behaviours for the Kilobot Robot 14

Evolving Individual and Collective Behaviours for the Kilobo...

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14th IEEE International Workshop on Advanced Motion Control (AMC)

作者： Beckerleg, Mark Zhang, Chan AUT Univ Sch Engn Auckland New Zealand

ISBN: (纸本)9781479984640

This paper demonstrates how individual and collective behaviours of robots can be evolved using a novel technique of applying a genetic algorithm on a lookup table based chromosome. The evolved behaviours are: orbiting a stationary robot;navigation between three robots;follow the leader using six robots;and robot dispersal where the robots move away from each other. These behaviours are based on those used by Harvard University when demonstrating some of the collective behaviours that can be implemented by the Kilobot robot. With careful selection of the lookup tables and fitness functions all the above behaviours can be successfully evolved.

关键词： genetic algorithms evolving collective behaviours evolving lookup tables based robotic controller evolutionary Computation evolutionary robotics Kilobot

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Design and Analysis of Proximate Mechanisms for Cooperative Transport in Real Robots 10th

Design and Analysis of Proximate Mechanisms for Cooperative ...

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10th International Conference on Swarm Intelligence (ANTS)

作者： Alkilabi, Muhanad H. Mohammed Narayan, Aparajit Tuci, Elio Aberystwyth Univ Comp Sci Dept Aberystwyth Dyfed Wales Kerbala Univ Comp Sci Dept Kerbala Iraq

ISBN: (纸本)9783319444277;9783319444260

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.

关键词： Swarm robotics Cooperative transport evolutionary robotics

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Bee-Inspired Landmark Recognition in Robotic Navigation

Bee-Inspired Landmark Recognition in Robotic Navigation

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Genetic and evolutionary Computation Conference (GECCO)

作者： Cumbo, Kodi Heck, Samantha Tanimoto, Ian DeVault, Travis Heckendorn, Robert Soule, Terence Univ Idaho Dept Comp Sci Moscow ID 83844 USA

ISBN: (纸本)9781450343237

Research has shown that honeybees have a remarkable ability to use landmarks to travel miles to find - and return to - a food source [1, 2]. The long-term goal of this research is to use physical robots, incorporating a variety of image processing approaches, to examine this ability. The results may also improve our ability to develop effective landmark guidance systems for robots. This research examines whether robots using a simple genetic algorithm (GA) and neural network (NN) that are trained to search for a target object adopt the use of landmarks to aid in the search even when landmarks are not explicitly considered. This is a first step towards more complex experiments in less controlled environments. The simplified environment used in these experiments is controllable, making it easier to determine and understand the robot's behavior before moving to a more complex external environment. The robot uses a feed-forward, episodic NN for navigation - there is no inherent use of a memory bank of images. It is trained using Learning from Demonstration (LfD) to follow a simple search pattern independent of landmarks, but visual cues that could act as landmarks are present in the environment. Our results show that even under these conditions visual cues, i.e. landmarks, are incorporated in the learned search.

关键词： evolutionary robotics neural networks navigation

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A Comparison of Multiobjective Algorithms in Evolving Quadrupedal Gaits 1

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14th International Conference on the Simulation of Adaptive Behavior (SAB)

作者： Moore, Jared M. McKinley, Philip K. Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (数字)9783319434889

ISBN: (纸本)9783319434889;9783319434872

Robotic systems, whether physical or virtual, must balance multiple objectives to operate effectively. Beyond performance metrics such as speed and turning radius, efficiency of movement, stability, and other objectives contribute to the overall functionality of a system. Optimizing multiple objectives requires algorithms that explore and balance improvements in each. In this paper, we evaluate and compare two multiobjective algorithms, NSGA-II and the recently proposed Lexicase selection, investigating distance traveled, efficiency, and vertical torso movement for evolving gaits in quadrupedal animats. We explore several variations of Lexicase selection, including different parameter configurations and weighting strategies. A control treatment evolving solely on distance traveled is also presented as a baseline. All three algorithms (NSGA-II, Lexicase, and Control) produce effective locomotion in the quadrupedal animat, but differences arise in performance and efficiency of movement. The NSGA-II algorithm significantly outperforms Lexicase selection in all three objectives, while Lexicase selection significantly outperforms the control in two of the three objectives.

关键词： evolutionary robotics Multiobjective algorithms Genetic algorithms Computational evolution Lexicase selection NSGA-II

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