383 resources related to Evolutionary Robotics
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
ISIE focuses on advancements in knowledge, new methods, and technologies relevant to industrial electronics, along with their applications and future developments.
The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
The scope of the 2020 IEEE/ASME AIM includes the following topics: Actuators, Automotive Systems, Bioengineering, Data Storage Systems, Electronic Packaging, Fault Diagnosis, Human-Machine Interfaces, Industry Applications, Information Technology, Intelligent Systems, Machine Vision, Manufacturing, Micro-Electro-Mechanical Systems, Micro/Nano Technology, Modeling and Design, System Identification and Adaptive Control, Motion Control, Vibration and Noise Control, Neural and Fuzzy Control, Opto-Electronic Systems, Optomechatronics, Prototyping, Real-Time and Hardware-in-the-Loop Simulation, Robotics, Sensors, System Integration, Transportation Systems, Smart Materials and Structures, Energy Harvesting and other frontier fields.
The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.
Papers on application, design, and theory of evolutionary computation, with emphasis given to engineering systems and scientific applications. Evolutionary optimization, machine learning, intelligent systems design, image processing and machine vision, pattern recognition, evolutionary neurocomputing, evolutionary fuzzy systems, applications in biomedicine and biochemistry, robotics and control, mathematical modelling, civil, chemical, aeronautical, and industrial engineering applications.
IEEE Transactions on Industrial Informatics focuses on knowledge-based factory automation as a means to enhance industrial fabrication and manufacturing processes. This embraces a collection of techniques that use information analysis, manipulation, and distribution to achieve higher efficiency, effectiveness, reliability, and/or security within the industrial environment. The scope of the Transaction includes reporting, defining, providing a forum for discourse, and informing ...
2009 ICCAS-SICE, 2009
In this paper, experience repository based particle swarm optimization (ERPSO) is proposed for effectively applying particle swarm optimization (PSO) to evolutionary robotics application. The ERPSO uses a concept experience repository to store previous position and fitness of particles to accelerate convergence speed of PSO. We applied the ERPSO to find parameter of gait of a quadruped robot that produces fast ...
2010 World Automation Congress, 2010
Open-ended evolution is considered to be caused by several factors, one of which would be co-evolution. Competitive co-evolution can give rise to the “Red Queen effect”, where the fitness landscape of each population is continuously changed by the competing population. Therefore, if such changes are captured, co-evolutionary progress would be measured. In this paper, we investigated features of competitive co-evolutionary ...
2018 Annual IEEE International Systems Conference (SysCon), 2018
This paper compares fuzzy and neural controllers when trying to cross the reality gap in evolutionary robotics. Reality gap is one of the most relevant open questions in evolutionary robotics for it restricts its use in practical and complex applications of robotics. Controllers are compared by navigation metrics for differential drive robots (a Pioneer 3-DX). Based on the metrics, similarity ...
2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC), 2010
Evolutionary Robotics is a collection of heuristics where robotic control systems are developed by following the example of natural evolution. An evolutionary run is performed by mutating the robots' controllers randomly and selecting for some desired behavioral properties. Overall, these properties should be improved over time leading to a stable increase of fitness. However, random mutations on critical controller parts ...
Artificial Life, 2017
<para>One of the major challenges of evolutionary robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction of the sensory inputs and motor actions as a tool to tackle this problem. Abstraction in robots is simply the use of preprocessed sensory inputs and low-level closed-loop control systems that execute ...
Hideyuki Takagi - Interactive Evolutionary Computation
Creative AI through Evolutionary Computation
Blast from the past: Revisiting Evolutionary Strategies for the Design of Engineered Systems
A Survey of Representations for Evolutionary Computation 2
Evolutionary Strategies for Difficult Engineering Design Problems
Some Thoughts on a Gap Between Theory and Practice of Evolutionary Algorithms - WCCI 2012
A Survey of Representations for Evolutionary Computation 1
Metaheuristics for Multiobjective Optimization
Research Trends in Evolutionary Multi-Objective Optimization: Past, Present and Future
What Can Evolutionary Computation Do for You?
Multiobjective Quantum-inspired Evolutionary Algorithm and Preference-based Solution Selection Algorithm
Dynamic Selection of Evolutionary Algorithm Operators Based on Online Learning and Fitness Landscape Metrics
Advances on Many-objective Evolutionary Optimization - IEEE WCCI 2012
Evolutionary Computation - A Technology Inspired by Nature
Dario Floreano: The Evolutionary Analysis & Synthesis of Intelligent Living Systems
Ethical Considerations 200 Years After Frankenstein: IEEE TechEthics Virtual Panel
Achieving Balance Between Convergence and Diversity in Evolutionary Multi-Objective Optimization - Ke Li
Francisco Herrera: Evolutionary fuzzy systems for data science & big data: Why & What For?
Mengjie Zhang: Evolutionary Deep Learning for Image Analysis
In this paper, experience repository based particle swarm optimization (ERPSO) is proposed for effectively applying particle swarm optimization (PSO) to evolutionary robotics application. The ERPSO uses a concept experience repository to store previous position and fitness of particles to accelerate convergence speed of PSO. We applied the ERPSO to find parameter of gait of a quadruped robot that produces fast gait and ERPSO showed best performance among original PSO and PSO variants. ERPSO has fast convergence property which reduces the evaluation of fitness of parameters in a real environment.
Open-ended evolution is considered to be caused by several factors, one of which would be co-evolution. Competitive co-evolution can give rise to the “Red Queen effect”, where the fitness landscape of each population is continuously changed by the competing population. Therefore, if such changes are captured, co-evolutionary progress would be measured. In this paper, we investigated features of competitive co-evolutionary fitness landscapes on a predator-prey problem in computer simulations. The results suggest to us that fitness landscapes on competitive co-evolutionary robotics have no correlation with respect to the genetic data obtained at each generation in the evolutionary runs.
This paper compares fuzzy and neural controllers when trying to cross the reality gap in evolutionary robotics. Reality gap is one of the most relevant open questions in evolutionary robotics for it restricts its use in practical and complex applications of robotics. Controllers are compared by navigation metrics for differential drive robots (a Pioneer 3-DX). Based on the metrics, similarity between the controllers could be verified. This similarity allows the conclusion that Fuzzy Logic can also be used in evolutionary robotics. Furthermore, when compared with a Neural Network controller, the fuzzy control strategy results in smoother trajectories. Another relevant result is that the use of a Fuzzy Logic approach makes the evolutionary process faster, only requiring 30 generations, while the Neural Network approach requires approximately 70 generations. Finally, the fuzzy controller allows the user to include known characteristics of the system by a human specialist. This cannot be achieved with the use of Neural Networks. However, the neural network can be indicated in complex systems or tasks, where the designer has very little or no information about the system.
Evolutionary Robotics is a collection of heuristics where robotic control systems are developed by following the example of natural evolution. An evolutionary run is performed by mutating the robots' controllers randomly and selecting for some desired behavioral properties. Overall, these properties should be improved over time leading to a stable increase of fitness. However, random mutations on critical controller parts can lead to a rapid degradation lowering the performance of evolution. This paper presents an approach to reduce the loss of desirable behavior during an evolution process. A notion of age is introduced as a quality criterion to indicate the contribution of parts of a controller to the robot's overall behavior. To preserve the behavior evolved so far, mutations are channeled to affect controller parts with a lower age more than those with a higher age. As a result, controller parts that contribute to a good behavior are stabilized and the evolved desirable behavior is maintained. Experiments have been performed in a decentralized online evolutionary scenario with controllers based on finite state machines (FSMs). The results show an improvement in the number of successful evolutions and the number of successfully evolved robots compared to previous studies.
<para>One of the major challenges of evolutionary robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction of the sensory inputs and motor actions as a tool to tackle this problem. Abstraction in robots is simply the use of preprocessed sensory inputs and low-level closed-loop control systems that execute higher-level motor commands. To demonstrate the impact abstraction could have, we evolved two controllers with different levels of abstraction to solve a task of forming an asymmetric triangle with a homogeneous swarm of micro air vehicles. The results show that although both controllers can effectively complete the task in simulation, the controller with the lower level of abstraction is not effective on the real vehicle, due to the reality gap. The controller with the higher level of abstraction is, however, effective both in simulation and in reality, suggesting that abstraction can be a useful tool in making evolved behavior robust to the reality gap. Additionally, abstraction aided in reducing the computational complexity of the simulation environment, speeding up the optimization process. Preeminently, we show that the optimized behavior exploits the environment (in this case the identical behavior of the other robots) and performs input shaping to allow the vehicles to fly into and maintain the required formation, demonstrating clear sensory-motor coordination. This shows that the power of the genetic optimization to find complex correlations is not necessarily lost through abstraction as some have suggested.</para>
The ability to sense the relative position of one's own body parts is referred to as proprioception. This sense allows humans to interact with their environment without direct observation. Evolutionary Robotics is a field of study that investigates the automatic development of robotic controllers and morphologies. This paper proposes the idea of providing robotic controllers with a form of proprioception. This was achieved by providing robotic controllers with simulated location information during execution. This study compared controllers with and without proprioception, both in simulation and the real world. The controllers with proprioception outperformed those without, in all trials.
This paper studies the Lego NXT platform's suitability for evolutionary robotics. It is shown that the low-cost Lego NXT educational set is indeed adequate for simple experiments in evolutionary robotics. This is demonstrated by an experiment, where an artificial neural network-based controller capable of behaving meaningfully in a Lego sumo wrestling context is evolved on physical Lego NXT robots without the aid of simulation. A detailed description of the experiment is provided, and the practical aspects of actually conducting evolutionary robotics on the platform are studied. Earlier research suggests that using evolutionary robotics in education could provide a good and concrete example of the principles and mechanisms of evolution. The research described here utilizes only standard Lego NXT Educational kits, making conducting the experiments possible for a very wide audience. To the authors' knowledge this is the first time the non-simulated Lego NXT is used to conduct artificial neural network-based evolutionary robotics.
In this paper, we describe influence of viewpoints of observation in an interactive evolutionary robotics system. We have been proposed a behavior learning system ICS (Interactive Classifier System) using interactive evolutionary computation. In this system, a mobile robot is able to quickly learn rules by direct teaching of a human operator. ICS is a novel evolutionary robotics approach using a classifier system. We classify teaching methods into internal observation and external one, and investigate influence of observation methods. We have experiments based on our teaching methods in two kinds of tasks. We found that teaching methods from different viewpoints of observation change teaching efficiency because of the difference between a robot's recognition and an operator's one in an environment.
Evolutionary robotics offers an efficient and easy-to-use framework for automatically building behaviors for an autonomous robot. However, a major drawback of this approach relies in the difficulty to define the fitness function (i.e. the learning setup) in order to get satisfying results. Recent works addressed this issue either by decomposing the learning task or by endowing the agent with such capabilities that should make the goal easier to achieve. Literature in evolutionary approach shows that modifying the very nature of genetic operators and/or fitness during the course of evolution may lead to better results for complex problems. In the scope of this short paper, we are interested in the reformulation of a straightforward complex fitness function into more subtle versions using different approaches
Evolutionary robotics is an exciting new area of research with the potential to provide ways to build robots that are beyond our current design abilities. Instead of building robots from the ground up, as with other approaches, robot controllers are evolved using algorithms inspired by biological evolution. The purpose of this work is to develop a tool for the evolution of mobile robot controllers. The controllers are evolved in a realistic simulation. The evolutionary algorithms are provided by the GALib library. Testing demonstrates that the system is capable of producing useful robot controllers. Users can configure the system so that a variety of controller types can be evolved. With a system such as this, users can conduct a range of experiments without having to create custom software.
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