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检索条件"主题词=evolutionary robotics"

共 637 条记录，以下是121-130 订阅

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AutoMoDe-Pomodoro: An evolutionary Class of Modular Designs 22

AutoMoDe-Pomodoro: An Evolutionary Class of Modular Designs

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Cambier, Nicolas Ferrante, Eliseo Vrije Univ Amsterdam Computat Intelligence Grp Amsterdam Netherlands Technol Innovat Inst Abu Dhabi U Arab Emirates

ISBN: (纸本)9781450392686

In this paper, we reintroduce evolutionary algorithms into Auto-MoDe, an automatic design approach which optimizes behavioural modules into a probabilistic finite automaton. We evaluate three approaches, with different encodings of the probabilistic finite automaton phenotype, and observe their performances. This work opens modular designs to more advanced evolutionary robotics methods, such as novelty search and embodied evolution.

关键词： swarm robotics evolutionary robotics modular design genetic algorithm

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Emergent Behavior in evolutionary Swarms for Machine Olfaction

Emergent Behavior in Evolutionary Swarms for Machine Olfacti...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： France, Kordel K. Paul, Anirban Banga, Ivneet Prasad, Shalini Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9798400704949

Navigation via olfaction (scent) is one of the most primitive forms of exploration used by organisms. Machine olfaction is a growing field within sensing systems and AI and many of its use cases are motivated by swarm intelligence. With this work, we are specifically interested in demonstrating the collaborative ability that evolutionary optimization can enable in swarm navigation via machine olfaction. We designate each particle of the swarm as a reinforcement learning (RL) agent and show how agent rewards can be directly correlated to maximize the swarm's reward signal. In doing so, we show how different behaviors emerge within swarms depending on which RL algorithms are used. We are motivated by the application of machine olfaction and evaluate multiple swarm permutations against a suite of scent navigation tasks to demonstrate preferences exhibited by the swarm. Our results indicate that swarms can be designed to achieve desired behaviors as a function of the algorithm each agent demonstrates. This paper contributes to the field of cooperative co-evolutionary algorithms by proposing a method by which evolutionary techniques can significantly improve how swarms of simple agents collaborate to solve complex tasks faster than a single large agent can under identical conditions.

关键词： evolutionary robotics reinforcement learning machine olfaction

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Understanding Environmental Influence in an Open-Ended evolutionary Algorithm

Understanding Environmental Influence in an Open-Ended Evolu...

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

作者： Steyven, Andreas Hart, Emma Paechter, Ben Edinburgh Napier Univ Sch Comp 10 Colinton Rd Edinburgh Midlothian Scotland

ISBN: (纸本)9783319458236;9783319458229

It is well known that in open-ended evolution, the nature of the environment plays in key role in directing evolution. However, in evolutionary robotics, it is often unclear exactly how parameterisation of a given environment might influence the emergence of particular behaviours. We consider environments in which the total amount of energy is parameterised by availability and value, and use surface plots to explore the relationship between those environment parameters and emergent behaviour using a variant of a well-known distributed evolutionary algorithm (mEDEA). Analysis of the resulting landscape show that it is crucial for a researcher to select appropriate parameterisations in order that the environment provides the right balance between facilitating survival and exerting sufficient pressure for new behaviours to emerge. To the best of our knowledge, this is the first time such an analysis has been undertaken.

关键词： evolutionary robotics Parameter selection Environment driven evolution Distributed online adaptation

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Decentralized Multi-Robot Mission Planning using evolutionary Computation

Decentralized Multi-Robot Mission Planning using Evolutionar...

引用

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

作者： Dumka, Sugandha Maheshwari, Smiti Kala, Rahul Indian Inst Informat Technol Robot & Artificial Intelligence Laboratry Allahabad Uttar Pradesh India

ISBN: (纸本)9781509060177

The classic problem of robot motion planning asks the robot to go from A to B avoiding obstacles. Missions are challenging problems asking the robot to visit a set of sites to accomplish a mission. The mission planning problems are largely studied as a Travelling Salesman Problem involving combinatorial optimization. In this paper the problem is generalized to any Boolean expression, giving more expressing powers to specify missions like "Visit any one of three coffee machines" or "Visit any two of three instructors", along with other mission sites to be mandatorily visited. The problem is solved using multiple robots in a decentralized manner. The Boolean expression is simplified into an 'OR of AND' format, which gives the flexibility to solve all the AND components and to select the minimum cost solution among them. Each of the AND components is a reduced multi-robot Travelling Salesman Problem solved by using k-medoids clustering and evolutionary computation. The results obtained by this approach are compared with the centralized algorithm and a master slave algorithm which uses a randomized algorithm for robot assignment, and for every such assignment the corresponding optimization problem of visiting the sites is solved for. The comparison depicts that as the problem size and the number of robots increase, the decentralized approach outperforms the rest enormously. The results are also tested on a Pioneer LX robot working in an office environment to carry dummy missions of everyday needs.

关键词： mission planning multi-robotic systems decentralized systems evolutionary robotics evolutionary computation

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Insights in evolutionary exploration of robot morphology spaces 8

Insights in evolutionary exploration of robot morphology spa...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Miras, Karine Gansekoele, Arwin Glette, Kyrre Eiben, A. E. Vrije Univ Amsterdam Dept Comp Sci Amsterdam Netherlands Univ Oslo Dept Informat Oslo Norway

ISBN: (纸本)9781538692769

In a recent study we have encountered an unexpected result regarding the evolutionary exploration of robot morphology spaces. Specifically, we found that an algorithm driven by selection based on morphological novelty explored fewer spots in the space of morphologies than another algorithm based on a combination of morphological novelty and some behavioral criterion (speed of movement). Here we revisit these results, perform new analyses, and obtain new insights. These insights clarify the exploration behavior of these algorithms and provide guidelines for designing selection mechanisms for evolutionary robotics.

关键词： evolutionary robotics morphological evolution search space exploration diversity novelty search

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evolutionary Trained Radial Basis Function Networks for Robot Control

Evolutionary Trained Radial Basis Function Networks for Robo...

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10th International Conference on Control, Automation, robotics and Vision

作者： Vidnerova, Petra Slusny, Stanislav Neruda, Roman Acad Sci Czech Republ Inst Comp Sci Prague Czech Republic

ISBN: (纸本)9781424422869

An emergence of intelligent behaviour within a simple robotic agent is studied in this paper. The radial basis function neural network is used as the control mechanism of the robot. evolutionary algorithm is used to train the agent to perform several tasks. A comparison to multilayer perceptron neural networks and reinforcement learning is made and the results are discussed.

关键词： evolutionary robotics RBF networks genetic algorithms

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Assessing Infotaxis Sensitivity to Model Quality Through evolutionary Computation 6th

Assessing Infotaxis Sensitivity to Model Quality Through Evo...

引用

6th Iberian robotics Conference (Robot)

作者： Macedo, Joao Marques, Lino Costa, Ernesto Univ Coimbra Ctr Informat & Syst Coimbra Portugal Univ Coimbra Inst Syst & Robot P-3030290 Coimbra Portugal

ISBN: (纸本)9783031586750;9783031586767

Locating odour sources with mobile robots is a difficult task that can be applied to locating the sources of pollutants, concealed explosives or victims in disaster scenarios. The existing approaches for locating odour sources can be divided between those that simply seek to reach the chemical source, and those that use gas dispersion models to estimate its location. One of the most popular source estimation approaches is Infotaxis, which has been shown to have great sensitivity to the parameters of its gas distribution model. In this paper, we compare two evolutionary approaches for automatically selecting the values for these parameters along with a Genetic Programming approach for evolving human-readable source-seeking strategies. The comparisons are carried out in three simulated environments with different chemical plumes and the results show that the parameters that best fit the environment do not always lead to the highest performance. Also, depending on the scenario, the tree-based search strategies are able to perform equivalently to Infotaxis, at a lesser computational cost.

关键词： Odour Source Localisation evolutionary robotics Infotaxis Genetic Algorithms

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evolutionary Mission Planning

Evolutionary Mission Planning

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

作者： Kala, Rahul Khan, Abeer Diksha, D. Shelly, S. Sinha, Surabhi Indian Inst Informat Technol Robot & Artificial Intelligence Lab Allahabad Uttar Pradesh India

ISBN: (纸本)9781509060177

The problem of mission planning enables a robot to solve for complex missions and thereafter execute the missions. The problem is seen as an advancement of the classical problem of motion planning wherein the task is to find a trajectory for a robot to go from a configuration A to a configuration B avoiding obstacles. The popular mechanism to solve the problem is using Temporal Logic specifications to specify a mission, and to thereafter use search strategies to solve the mission. The same requires an exponential complexity in terms of propositional variables to search and verify the mission plan. Further, optimality is usually not a criterion in finding a solution, and the transition costs are usually ignored leading to sub-optimal mission plans. As missions get more complex using a large number of propositional variables for specification, it may no longer be possible to use exponential complexity algorithms. The paper proposes the use of evolutionary Computation to solve the same problem. We first develop a new restricted language for mission design. Even though the language is restrictive, it can specify a large number of missions of real life service robotics. Then we design an evolutionary computation framework to solve the mission. Probabilistic Roadmap technique is used to get the transition system and transition costs between regions of interest. The mission planner takes the mission specification and these transition costs to compute a mission plan. The mission plan is executed using a reactive navigator that can avoid any dynamic obstacle, other people and robots.

关键词： evolutionary robotics Genetic Algorithm Robot Motion Planning Service robotics

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Robots playing to win: evolutionary soccer strategies

Robots playing to win: Evolutionary soccer strategies

引用

1997 IEEE International Conference on robotics and Automation (ICRA97) - Teaming to Make an Impact

作者： Agah, A Tanie, K AIST-MITI Tsukuba Japan

ISBN: (纸本)0780336135

Automatic development and learning of robot soccer strategies are presented in this paper. It is shown that using a novel control system, it is possible to allow teams of robots to acquire strategies for playing a better game of soccer through successive generations, utilizing simulated evolution. A number of soccer techniques, as developed through robot games, are discussed. The mechanism presented in the paper is suitable for other tasks requiring multiple robots to interact and cooperate in teams.

关键词： multi-robot systems robot soccer evolutionary robotics multi-agent cooperation and coordination distributed systems

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Multi-Objective vs. Single-Objective evolutionary Algorithms for Hybrid Mobile Robot Optimization

Multi-Objective vs. Single-Objective Evolutionary Algorithms...

引用

IEEE International Symposium on robotics and Manufacturing Automation (IEEE-ROMA)

作者： Lim, S. H. Teo, J. Univ Malaysia Sabah Fac Comp & Informat Evolutionary Comp Lab Kota Kinabalu Malaysia

ISBN: (纸本)9781479957651

In this paper, the comparison of Multi-Objective evolutionary Algorithm (MOEA) and Single-Objective evolutionary Algorithm (SOEA) in designing and optimizing the morphology of a Six Articulated-Wheeled Robot (SAWR) is presented. Results show that both methods are able to produce optimized SAWR which have smaller size with the capability to perform climbing motion. However, one of the solutions from the Pareto-set of MOEA is outperforming the fittest solution from SOEA. The solution is able to achieve the same performance of the fittest solution from SOEA and yet it is smaller in size. Besides that, another advantage of using MOEA is that MOEA is capable to produce a set of Pareto optimal solutions from the smallest SAWR with poor performance to the largest SAWR with robust performance which provide users a choice of solutions for trade-off between the two objectives.

关键词： multi-objective evolutionary algorithm single-objective evolutionary algorithm evolutionary robotics evolved hybrid mobile robot

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