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检索条件"主题词=evolutionary robotics"

共 635 条记录，以下是471-480 订阅

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Localization Uncertainty-driven Adaptive Framework for Controlling Ground Vehicle Robots

Localization Uncertainty-driven Adaptive Framework for Contr...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Kent, Daniel McKinley, Philip K. Radha, Hayder Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Dept Comp Sci & Engn E Lansing MI USA

ISBN: (纸本)9781728162126

Modern localization techniques allow ground vehicle robots to determine their position with centimeter-level accuracy under nominal conditions, enabling them to utilize fixed maps to navigate their environments. However, when localization measurements become unavailable, the position accuracy will drop and uncertainty will increase. While research and development on localization estimation seeks to reduce the severity of these outages, the question of what actions a robot should take under high localization uncertainty is still unresolved, and can vary on a platform-by-platform and mission-by-mission basis. In this paper, we exploit localization uncertainty measures to adapt system control parameters in real time. Offline, we optimize non-linear activation functions whose control parameters and relevant weights are trained and learned using evolutionary Algorithm (EA). Subsequently, in real time, we apply the optimized adaptation functions to the controller look-ahead distance and intermediate linear and angular velocity commands, which we identify as the most sensitive to localization error. evolutionary runs are conducted in which a simulated target vehicle is tasked with following a randomly generated path while minimizing cross-track error, with time varying localization uncertainty added. These runs produce situation-dependent weights for parameters to the adaptation functions, which are transferred to the physical platform, a 1:5-scale autonomous vehicle. In simulation, our system was able to reduce cross-track error, which in certain cases exceeds 250 centimeters on non-adapted systems, to below 15 centimeters on average using EA-derived weights and parameters applied to our proposed adaptation system. Evaluation on the physical platform demonstrates that without the adaptation module in place, the platform is unable to successfully follow the path;with the adaptation module, the platform automatically adjusts its velocity and look-ahead distance to compensat

关键词： autonomous vehicle localization path planning uncertainty evolutionary robotics Robot Operating System

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A MARKOV-CHAIN-BASED MODEL FOR SUCCESS PREDICTION OF EVOLUTION IN COMPLEX ENVIRONMENTS

A MARKOV-CHAIN-BASED MODEL FOR SUCCESS PREDICTION OF EVOLUTI...

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International Conference on evolutionary Computation Theory and Applications (ECTA 2011) / International Conference on Fuzzy Computation Theory and Applications (FCTA 2011)

作者： Koenig, Lukas Mostaghim, Sanaz Schmeck, Hartmut Karlsruhe Inst Technol Inst AIFB Kaiserstr 89 D-76128 Karlsruhe Germany

ISBN: (纸本)9789898425836

In this paper, a theoretical and experimental study of the influence of environments on the selection process in evolutionary swarm robotics is conducted. The theoretical selection model is based on Markov chains. It is proposed to predict the success rate of evolutionary runs which are based on a selection mechanism depending on implicit environmental properties as well as an explicit fitness function. In the experiments, the interaction of explicit and implicit selection is studied and a comparison with the model prediction is performed. The results indicate that the model prediction is accurate for the studied cases.

关键词： evolutionary robotics Swarm robotics Success prediction Mathematical model Markov chain Selection

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Action-Selection and Crossover Strategies for Self-Modeling Machines 07

Action-Selection and Crossover Strategies for Self-Modeling ...

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Annual Conference of Genetic and evolutionary Computation Conference

作者： Bongard, Josh Univ Vermont Dept Comp Sci Burlington VT 05405 USA

ISBN: (纸本)9781595936974

In previous work [7] a computational framework was demonstrated that employs evolutionary algorithms to automatically model a given system. This is accomplished by alternating the evolution of models with the evolutionary search for new training data. Theory predicts [23] that the best. new training data is that which induces maximum disagreement across the current model set. Here it is demonstrated that in a robot application this is not the case, and alternative fitness functions are developed that seek other, better training data. Also, it is shown that although crossover successfully reduces the mean error of the model set a, it compromises the ability of the framework to Find new, informative, training data. This has implications for how to create adaptive, self-modeling machines, and suggests how competitive. processes in the. brain underlie the generation of intelligent behavior.

关键词： evolutionary robotics self-modeling artificial intelligence

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Bridging the Reality Gap - A Dual Simulator Approach to the Evolution of Whole-Body Motion for the Nao Humanoid Robot 8

Bridging the Reality Gap - A Dual Simulator Approach to the ...

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8th International Joint Conference on Computational Intelligence

作者： Eaton, Malachy Univ Limerick Dept Comp Sci & Informat Syst Limerick Ireland

ISBN: (纸本)9789897582011

We describe a novel approach to the evolution of whole-body behaviours in the Nao humanoid robot using a multi-simulator approach to the alleviation of the reality gap issue. The initial evolutionary process takes place in the V-REP simulator. Once a viable whole-body motion has been evolved, this evolved motion is subsequently transferred for testing onto another simulation platform - Webots. Only when the evolved kicking behaviour has been demonstrated to also be viable on the Webots platform is this behaviour then transferred onto the real Nao robot for testing. This eliminates the time-consuming process of transferring behaviours onto the real robot which have little chance of successfully crossing the reality gap, and also minimises the potential for damage to the real Nao robot and/or it's environment. By using this novel approach of employing two different simulators, each with its own individual strengths and weaknesses, we reduce the likelihood that any individual behaviour will be able to exploit individual simulators' weaknesses, as the other simulator should pick up on this weak point. Using this procedure we have successfully evolved ball kicking behaviour in simulation, which has transferred with reasonable fidelity onto to the real Nao humanoid.

关键词： evolutionary Algorithms Humanoid robotics Ball Kicking evolutionary robotics evolutionary Humanoid robotics Whole-Body-Motion

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The Environment and Body-Brain Complexity

The Environment and Body-Brain Complexity

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2nd Genetic and evolutionary Computation Conference (GECCO)

作者： Spanellis, Christina Stewart, Brooke Nitschke, Geoff Univ Cape Town Dept Comp Sci Cape Town South Africa

ISBN: (纸本)9781450383509

An open question for both natural and artificial evolutionary systems is how, and under what environmental and evolutionary conditions complexity evolves. This study investigates the impact of increasingly complex task environments on the evolution of robot complexity. Specifically, the impact of evolving body-brain couplings on locomotive task performance, where robot evolution was directed by either body-brain exploration (novelty search) or objective-based (fitness function) evolutionary search. Results indicated that novelty search enabled the evolution of increased robot body-brain complexity and efficacy given specific environment conditions. The key contribution is thus the demonstration that body-brain exploration is suitable for evolving robot complexity that enables high fitness robots in specific environments.

关键词： evolutionary robotics Body-Brain Coevolution Neural Complexity Morphological Complexity Novelty Search

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Genetic Evolution of Hierarchical Behavior Structures 07

Genetic Evolution of Hierarchical Behavior Structures

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Annual Conference of Genetic and evolutionary Computation Conference

作者： Woolley, Brian G. Peterson, Gilbert L. USAF Inst Technol Dept Elect & Comp Engn Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781595936974

The development of coherent and dynamic behaviors for mobile robots is an exceedingly complex endeavor ruled by task objectives, environmental dynamics and the interactions within the behavior structure. This paper discusses the use of genetic programming techniques and the unified behavior framework to develop effective control hierarchies using interchangeable behaviors and arbitration components. Given the number of possible variations provided by the framework, evolutionary programming is used to evolve the overall behavior design. Competitive evolution of the behavior population incrementally develops feasible Solutions for the domain through competitive ranking. By developing and implementing many simple behaviors independently and then evolving a complex behavior structure suited to the domain, this approach allows for the reuse of elemental behaviors and cases the complexity of development for a given domain. Additionally. this approach has the ability to locate a behavior structure which a developer may not have previously considered, and whose ability exceeds expectations. The evolution of the behavior structure is demonstrated using agents in the Robocode environment, with the evolved structures performing up to 122 percent better than one crafted by an expert.

关键词： evolutionary robotics Genetic Programming Behavior-Based robotics Unified Behavior Framework

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Evolving Soft Robotic Jamming Grippers

Evolving Soft Robotic Jamming Grippers

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2nd Genetic and evolutionary Computation Conference (GECCO)

作者： Fitzgerald, Seth G. Delaney, Gary W. Howard, David Maire, Frederic CSIRO Engn Fac Robot & Autonomous Syst Grp QUTEER SchData61 Brisbane Qld Australia CSIRO Data61 Melbourne Vic Australia CSIRO Robot & Autonomous Syst Grp Data61 Brisbane Qld Australia QUT Engn Fac EER Sch Brisbane Qld Australia

ISBN: (纸本)9781450383509

Jamming Grippers are a novel class of soft robotic actuators that can robustly grasp and manipulate objects of arbitrary shape. They are formed by placing a granular material within a flexible skin connected to a vacuum pump and function by pressing the unjammed gripper against a target object and evacuating the air to transition the material to a jammed (solid) state, gripping the target object. However, due to the complex interactions between grain morphology and target object shape, much uncertainty still remains regarding optimal constituent grain shapes for specific gripping applications. We address this challenge by utilising a modern evolutionary Algorithm, NSGA-III, combined with a Discrete Element Method soft robot model to perform a multi-objective optimisation of grain morphology for use in jamming grippers for a range of target object sizes. Our approach optimises the microscopic properties of the system to elicit bespoke functional granular material performance driven by the complex relationship between the individual particle morphologies and the related emergent behaviour of the bulk state. Results establish the important contribution of grain morphology to gripper performance and the critical role of local surface curvature and the length scale governed by the relative sizes of the grains and target object.

关键词： evolutionary robotics Granular Materials Soft robotics NSGA-III Multi-Objective Optimization Discrete Element Method Computational Modelling

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Optimization of Multi-Robot Sumo Fight Simulation by a Genetic Algorithm to Identify Dominant Robot Capabilities

Optimization of Multi-Robot Sumo Fight Simulation by a Genet...

引用

IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Lehner, Joel Enrico Simi, Radovan Domberger, Rolf Hanne, Thomas Univ Appl Sci & Arts Northwestern Switzerland Olten Switzerland Univ Appl Sci & Arts Northwestern Switzerland Basel Switzerland

ISBN: (纸本)9781728121536

This paper analyzes the multirobot sumo fight simulation. This simulation is based on a computational model of several sumo fighters, which physically interact while trying to move the opponent out of the arena (lost fight). The problem is optimized using a genetic algorithm (GA), where the capabilities of not only one particular robot but of all robots simultaneously are improved. In this particular problem setup, the problem definition changes depending on the optimization path, because all robots also get better, competing against each other. The influence of different operators of the GA is investigated and compared. This paper raises the questions, which genetically controlled capabilities (e.g. size, speed) are dominant over time and how they can be identified by a sensitivity analysis using a GA. The results shed light on which parameters are dominant. This experiment typically opens up interesting fields of further research, especially about how to address optimization problems, where the optimization process influences the search space and how to eliminate the factor of randomness.

关键词： multi-robot sumo fight simulation evolutionary robotics genetic algorithm sensitivity analysis

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Sampling based Mission Planning for Multiple Robots

Sampling based Mission Planning for Multiple Robots

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IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Kala, Rahul Indian Inst Informat Technol Robot & Artificial Intelligence Lab Allahabad Uttar Pradesh India

ISBN: (纸本)9781509006229

Robots of the future would be sophisticated enough to do all kinds of tasks for the human master, while operating a team of robots would ensure that the tasks are done as efficiently as possible. This paper presents a solution to a very common mission wherein a team of robots needs to visit a number of mission sights and carry some operation there. The sights may have their own preference of robots. The real world workspace is first converted into a roadmap using the Probabilistic Roadmap algorithm. The adopted technique for roadmap generation uses a hybrid of narrow corridor sampler, obstacle based sampler and uniform sampler;with hybrid edge connection technique that aims to first connect disconnected roadmaps and then introduces redundant cycles and also ensures a good coverage of the configuration space. The roadmap is used to compute a cost matrix between every mission sight. This cost matrix is used by an optimization algorithm which deputes different mission sights to different robots as well as dictates the order of visit of the missions by each robot. Local optimization is used to quickly compute the optimal plan. The navigation of the robots is done using a Fuzzy Logic based navigator. Experiments show that the robots are able to coordinate with each other and complete the mission as a team very effectively.

关键词： evolutionary robotics Robot Mission Planning Fuzzy navigation Mission Optimization Sampling based robotics

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Emergence of Specialised Collective Behaviors in Evolving Heterogeneous Swarms 18th

Emergence of Specialised Collective Behaviors in Evolving He...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： van Diggelen, Fuda de Carlo, Matteo Cambier, Nicolas Ferrante, Eliseo Eiben, Guszti Vrije Univ Amsterdam Boelelaan 1111 Amsterdam Netherlands

ISBN: (纸本)9783031700675;9783031700682

Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful for solving complex tasks and for survival. This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task. In this work, we evolve a swarm of simulated robots with phenotypic plasticity to study the emergence of specialized collective behavior during an emergent perception task. Phenotypic plasticity is realized in the form of heterogeneity of behavior by dividing the genotype into two components, with a different neural network controller associated to each component. The whole genotype, which expresses the behavior of the whole group through the two components, is subject to evolution with a single fitness function. We analyze the obtained behaviors and use the insights provided by these results to design an online regulatory mechanism. Our experiments show four main findings: 1) Heterogeneity improves both robustness and scalability;2) The sub-groups evolve distinct emergent behaviors. 3) The effectiveness of the whole swarm depends on the interaction between the two sub-groups, leading to a more robust performance than with singular sub-group behavior. 4) The online regulatory mechanism improves overall performance and scalability.

关键词： Swarm robotics evolutionary robotics Heterogeneous swarm

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