New horizons in evolutionary robotics : extended contributions from the 2009 EvoDeRob workshop /
Doncieux, Stéphane,
New horizons in evolutionary robotics : extended contributions from the 2009 EvoDeRob workshop / Stéphane Doncieux, Nicolas Bredèche and Jean-Baptiste Mouret (eds.). - xv, 225 pages : illustrations ; 24 cm. - Studies in computational intelligence ; 341 . - Studies in computational intelligence ; 341. .
Includes bibliographical references.
Introduction: -- Evolutionary Robotics: Exploring New Horizons / Introduction -- A Brief Introduction to Evolutionary Computation -- When to Use ER Methods? -- Absence of "Optimal" Method -- Knowledge of Fitness Function Primitives -- Knowledge of Phenotype Primitives -- Where and How to Use EA in the Robot Design Process? -- Mature Techniques: Parameter Tuning -- Current Trend: Evolutionary Aided Design -- Current Trend: Online Evolutionary Adaptation -- Long Term Research: Automatic Synthesis -- Frontiers of ER and Perspectives -- Reality Gap -- Fitness Landscape and Exploration -- Genericity of Evolved Solutions -- A Roboticist Point of View -- Discussion -- Good Robotic Engineering Practices -- Good Experimental Sciences Practices -- Invited Position Papers: -- The 'What', 'How' and the 'Why' of Evolutionary Robotics / The What of Embodiment -- The How of Embodiment -- The Why of Embodiment -- Why Consider Topological Change to a Robot's Body Plan? -- Why Evolve Robot Body Plans Initially at a Low Resolution? -- Why Allow Body Plans to Change during Behavior Optimization? -- Why Evolutionary Robotics Will Matter / Joining the Mainstream -- Bridging the Gap -- Realizing the Promise -- Evolutionary Algorithms in the Design of Complex Robotic Systems / Introduction -- Particularities of the Robotic System Design -- Parameters and Evaluation of Robotic Systems -- Evolutionary Algorithms in the Robotic System Design -- Kinematic Design of Robot Manipulators -- Modular Locomotion System Design -- Inverse Model Synthesis -- Multi-objective Task Based Design of Redundant Systems -- Flexible Building Block Design of Compliant Mechanisms -- Conclusion -- Regular Contributions: -- Evolving Monolithic Robot Controllers through Incremental Shaping / Introduction -- Learning Multiple Behaviors with a Monolithic Controller -- Specialization in a Morphologically Homogeneous Robot -- Evolutionary Algorithms to Analyse and Design a Controller for a Flapping Wings Aircraft / Introduction -- Method -- Experimental Setup -- Results -- Discussion and Future Work -- Conclusions -- On Applying Neuroevolutionary Methods to Complex Robotic Tasks / Introduction -- Case Study 1: Augmented Neural Network with Kalman Filter (ANKF) -- The ass Filter -- Evolving ANKF -- Comparison of Number of Parameters to be Optimized for ANKF and Recurrent Neural Networks -- Results Obtained for ANKF on the Double Pole Balancing without Velocities Benchmark -- Case Study 2: Incremental Modification of Fitness Function -- Quadrocopter -- Control Architecture Developed for the Quadrocopter Using the Principles of Behavior Based Systems -- Incremental Modification of Fitness Function -- Experiments and Results -- Task Decomposition with a Definition of a Single Global Fitness Function Is Not Necessarily Sufficient for Solving Complex Robot Tasks -- Conclusion -- Evolutionary Design of a Robotic Manipulator for a Highly Constrained Environment / Introduction -- Case Study -- Genetic Algorithm and Implementation -- Genetic Algorithm -- Genome -- Trajectory Tracking -- Control Law -- Indicators -- Results -- Design with Simple Trajectory -- Design with Complex Trajectory -- Conclusions and Future Works -- Conclusions -- Future Works -- A Multi-cellular Based Self-organizing Approach for Distributed Multi-Robot Systems / Introduction -- Biological Background -- The Approach -- The GRN-Based Dynamics -- Convergence Analysis of System Dynamics -- The Evolutionary Algorithm for Parameter Tuning -- Simulation and Results -- Case Study 1: Multi-robots Forming a Unit Circle -- Case Study 2: Multi-robots Forming a Unit Square -- Case Study 3: Self-reorganization -- Case Study 4: Robustness Tests to Sensory Noise -- Case Study 5: Self-adaptation to Environmental Changes -- Conclusion and Future Works -- Stephane Doncieux, Jean-Baptiste Mouret, Nicolas Bredeche, Vincent Padois -- Josh Bongard -- Kenneth O. Stanley -- Philippe Bidaud -- Joshua E. Auerbach, Josh C. Bongard -- Stephane Doncieux, Mohamed Hamdaoui -- Yohannes Kassahun, Jose de Gea, Jakob Schwendner, Frank Kirchner -- S. Rubrecht, E. Singla, V. Padois, P. Bidaud, M. de Broissia -- Yan Meng, Hongliang Guo, Yaochu Jin -- Part I. 1. 1.1. 1.2. 1.3. 1.3.1. 1.3.2. 1.3.3. 1.4. 1.4.1. 1.4.2. 1.4.3. 1.4.4. 1.5. 1.5.1. 1.5.2. 1.5.3. 1.6. 1.7. 1.7.1. 1.7.2. Part II. 2. 2.1. 2.2. 2.3. 2.4. 2.5. 2.6. 3. 3.1. 3.2. 3.3. 4. 4.1. 4.2. 4.3. 4.4. 4.4.1. 4.4.2. 4.4.3. 4.4.4. 4.4.5. 4.5. Part III. 5. 5.1. 5.2. 5.3. 6. 6.1. 6.2. 6.3. 6.4. 6.5. 6.6. 7. 7.1. 7.2. 7.2.1. 7.2.2. 7.2.3. 7.2.4. 7.3. 7.3.1. 7.3.2. 7.3.3. 7.3.4. 7.3.5. 7.4. 8. 8.1. 8.2. 8.3. 8.3.1. 8.3.2. 8.3.3. 8.3.4. 8.3.5. 8.4. 8.4.1. 8.4.2. 8.5. 8.5.1. 8.5.2. 9. 9.1. 9.2. 9.3. 9.3.1. 9.3.2. 9.3.3. 9.4. 9.4.1. 9.4.2. 9.4.3. 9.4.4. 9.4.5. 9.5. Novelty-Based Multiobjectivization / Introduction -- Related Work -- Novelty Search -- Multi-Objective Evolutionary Algorithms -- Multiobjectivization -- Method -- Experiment -- Fitness Function and Distance between Behaviors -- Variants -- Expected Results -- Experimental Parameters -- Results -- Average Fitness -- Convergence Rate -- Exploration -- Conclusion and Discussion -- Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm / Introduction -- Extending the (1+1)-Online EA -- Limits of (1+1)-Online -- The (1+1)-Restart-Online Algorithm -- Experiments and Results -- Hardware Set-Up -- Experimental Set-Up -- Experimental Results -- Hall-of-Fame Analysis -- Real Robot Experiment -- Conclusion and Perspectives -- Automated Planning Logic Synthesis for Autonomous Unmanned Vehicles in Competitive Environments with Deceptive Adversaries / Introduction -- USV System Architecture -- USV Virtual Sensor Models -- Planning Architecture -- Planning Logic Synthesis -- Test Mission -- Synthesis Scheme -- Planning Logic Components Evolution -- Computational Experiments -- General Setup -- Results -- Conclusions -- Major Feedback Loops Supporting Artificial Evolution in Multi-modular Robotics / Introduction -- Artificial Homeostatic Hormone System -- Artificial Genome -- Feedback 1: Classic Control -- Feedback 2: Learning -- Feedback 3: Evolution -- Feedback 4: Controller Morphogenesis -- Feedback 5: Robot Organism Morphogenesis -- Feedback 6: Body Motion -- Step 1: The First Oscillator -- Step 2: Motion of Bigger Organisms -- Step 3: Motion of More Complex Organisms -- Discussion -- Evolutionary Design and Assembly Planning for Stochastic Modular Robots / Introduction -- Target Structure Evolution -- Stochastic Fluidic Assembly System Model -- Assembly Algorithm -- Conclusion. Jean-Baptiste Mouret -- Jean-Marc Montanier, Nicolas Bredeche -- Petr Svec, Satyandra K. Gupta -- Thomas Schmickl, Jurgen Stradner, Heiko Hamann, Lutz Winkler, Karl Crailsheim -- Michael T. Tolley, Jonathan D. Hiller, Hod Lipson -- 10. 10.1. 10.2. 10.2.1. 10.2.2. 10.2.3. 10.3. 10.3.1. 10.3.2. 10.3.3. 10.3.4. 10.3.5. 10.4. 10.4.1. 10.4.2. 10.4.3. 10.5. 11. 11.1. 11.2. 11.2.1. 11.2.2. 11.3. 11.3.1. 11.3.2. 11.3.3. 11.3.4. 11.3.5. 11.4. 12. 12.1. 12.2. 12.2.1. 12.2.2. 12.3. 12.3.1. 12.3.2. 12.3.3. 12.4. 12.4.1. 12.4.2. 12.5. 13. 13.1. 13.2. 13.2.1. 13.3. 13.4. 13.5. 13.6. 13.7. 13.8. 13.8.1. 13.8.2. 13.8.3. 13.9. 14. 14.1. 14.2. 14.3. 14.4. 14.5.
"Evolutionary Algorithms (EAs) now provide mature optimization tools that have successfully been applied to many problems, from designing antennas to complete robots, and provided many human-competitive results. In robotics, the integration of EAs within the engineer's toolbox made tremendous progress in the last 20 years and proposes new methods to address challenging problems in various setups: modular robotics, swarm robotics, robotics with non-conventional mechanics (e.g. high redundancy, dynamic motion, multi-modality), etc. This book takes its roots in the workshop on "New Horizons in Evolutionary Design of Robots" that brought together researchers from Computer Science and Robotics during the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2009) in Saint Louis (USA). This book features extended contributions from the workshop, thus providing various examples of current problems and applications, with a special emphasis on the link between Computer Science and Robotics. It also provides a comprehensive and up-to-date introduction to Evolutionary Robotics after 20 years of maturation as well as thoughts and considerations from several major actors in the field. This book offers a comprehensive introduction to the current trends and challenges in Evolutionary Robotics for the next decade."--Publisher's website.
3642182712 9783642182716
Evolutionary robotics
629.892
New horizons in evolutionary robotics : extended contributions from the 2009 EvoDeRob workshop / Stéphane Doncieux, Nicolas Bredèche and Jean-Baptiste Mouret (eds.). - xv, 225 pages : illustrations ; 24 cm. - Studies in computational intelligence ; 341 . - Studies in computational intelligence ; 341. .
Includes bibliographical references.
Introduction: -- Evolutionary Robotics: Exploring New Horizons / Introduction -- A Brief Introduction to Evolutionary Computation -- When to Use ER Methods? -- Absence of "Optimal" Method -- Knowledge of Fitness Function Primitives -- Knowledge of Phenotype Primitives -- Where and How to Use EA in the Robot Design Process? -- Mature Techniques: Parameter Tuning -- Current Trend: Evolutionary Aided Design -- Current Trend: Online Evolutionary Adaptation -- Long Term Research: Automatic Synthesis -- Frontiers of ER and Perspectives -- Reality Gap -- Fitness Landscape and Exploration -- Genericity of Evolved Solutions -- A Roboticist Point of View -- Discussion -- Good Robotic Engineering Practices -- Good Experimental Sciences Practices -- Invited Position Papers: -- The 'What', 'How' and the 'Why' of Evolutionary Robotics / The What of Embodiment -- The How of Embodiment -- The Why of Embodiment -- Why Consider Topological Change to a Robot's Body Plan? -- Why Evolve Robot Body Plans Initially at a Low Resolution? -- Why Allow Body Plans to Change during Behavior Optimization? -- Why Evolutionary Robotics Will Matter / Joining the Mainstream -- Bridging the Gap -- Realizing the Promise -- Evolutionary Algorithms in the Design of Complex Robotic Systems / Introduction -- Particularities of the Robotic System Design -- Parameters and Evaluation of Robotic Systems -- Evolutionary Algorithms in the Robotic System Design -- Kinematic Design of Robot Manipulators -- Modular Locomotion System Design -- Inverse Model Synthesis -- Multi-objective Task Based Design of Redundant Systems -- Flexible Building Block Design of Compliant Mechanisms -- Conclusion -- Regular Contributions: -- Evolving Monolithic Robot Controllers through Incremental Shaping / Introduction -- Learning Multiple Behaviors with a Monolithic Controller -- Specialization in a Morphologically Homogeneous Robot -- Evolutionary Algorithms to Analyse and Design a Controller for a Flapping Wings Aircraft / Introduction -- Method -- Experimental Setup -- Results -- Discussion and Future Work -- Conclusions -- On Applying Neuroevolutionary Methods to Complex Robotic Tasks / Introduction -- Case Study 1: Augmented Neural Network with Kalman Filter (ANKF) -- The ass Filter -- Evolving ANKF -- Comparison of Number of Parameters to be Optimized for ANKF and Recurrent Neural Networks -- Results Obtained for ANKF on the Double Pole Balancing without Velocities Benchmark -- Case Study 2: Incremental Modification of Fitness Function -- Quadrocopter -- Control Architecture Developed for the Quadrocopter Using the Principles of Behavior Based Systems -- Incremental Modification of Fitness Function -- Experiments and Results -- Task Decomposition with a Definition of a Single Global Fitness Function Is Not Necessarily Sufficient for Solving Complex Robot Tasks -- Conclusion -- Evolutionary Design of a Robotic Manipulator for a Highly Constrained Environment / Introduction -- Case Study -- Genetic Algorithm and Implementation -- Genetic Algorithm -- Genome -- Trajectory Tracking -- Control Law -- Indicators -- Results -- Design with Simple Trajectory -- Design with Complex Trajectory -- Conclusions and Future Works -- Conclusions -- Future Works -- A Multi-cellular Based Self-organizing Approach for Distributed Multi-Robot Systems / Introduction -- Biological Background -- The Approach -- The GRN-Based Dynamics -- Convergence Analysis of System Dynamics -- The Evolutionary Algorithm for Parameter Tuning -- Simulation and Results -- Case Study 1: Multi-robots Forming a Unit Circle -- Case Study 2: Multi-robots Forming a Unit Square -- Case Study 3: Self-reorganization -- Case Study 4: Robustness Tests to Sensory Noise -- Case Study 5: Self-adaptation to Environmental Changes -- Conclusion and Future Works -- Stephane Doncieux, Jean-Baptiste Mouret, Nicolas Bredeche, Vincent Padois -- Josh Bongard -- Kenneth O. Stanley -- Philippe Bidaud -- Joshua E. Auerbach, Josh C. Bongard -- Stephane Doncieux, Mohamed Hamdaoui -- Yohannes Kassahun, Jose de Gea, Jakob Schwendner, Frank Kirchner -- S. Rubrecht, E. Singla, V. Padois, P. Bidaud, M. de Broissia -- Yan Meng, Hongliang Guo, Yaochu Jin -- Part I. 1. 1.1. 1.2. 1.3. 1.3.1. 1.3.2. 1.3.3. 1.4. 1.4.1. 1.4.2. 1.4.3. 1.4.4. 1.5. 1.5.1. 1.5.2. 1.5.3. 1.6. 1.7. 1.7.1. 1.7.2. Part II. 2. 2.1. 2.2. 2.3. 2.4. 2.5. 2.6. 3. 3.1. 3.2. 3.3. 4. 4.1. 4.2. 4.3. 4.4. 4.4.1. 4.4.2. 4.4.3. 4.4.4. 4.4.5. 4.5. Part III. 5. 5.1. 5.2. 5.3. 6. 6.1. 6.2. 6.3. 6.4. 6.5. 6.6. 7. 7.1. 7.2. 7.2.1. 7.2.2. 7.2.3. 7.2.4. 7.3. 7.3.1. 7.3.2. 7.3.3. 7.3.4. 7.3.5. 7.4. 8. 8.1. 8.2. 8.3. 8.3.1. 8.3.2. 8.3.3. 8.3.4. 8.3.5. 8.4. 8.4.1. 8.4.2. 8.5. 8.5.1. 8.5.2. 9. 9.1. 9.2. 9.3. 9.3.1. 9.3.2. 9.3.3. 9.4. 9.4.1. 9.4.2. 9.4.3. 9.4.4. 9.4.5. 9.5. Novelty-Based Multiobjectivization / Introduction -- Related Work -- Novelty Search -- Multi-Objective Evolutionary Algorithms -- Multiobjectivization -- Method -- Experiment -- Fitness Function and Distance between Behaviors -- Variants -- Expected Results -- Experimental Parameters -- Results -- Average Fitness -- Convergence Rate -- Exploration -- Conclusion and Discussion -- Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm / Introduction -- Extending the (1+1)-Online EA -- Limits of (1+1)-Online -- The (1+1)-Restart-Online Algorithm -- Experiments and Results -- Hardware Set-Up -- Experimental Set-Up -- Experimental Results -- Hall-of-Fame Analysis -- Real Robot Experiment -- Conclusion and Perspectives -- Automated Planning Logic Synthesis for Autonomous Unmanned Vehicles in Competitive Environments with Deceptive Adversaries / Introduction -- USV System Architecture -- USV Virtual Sensor Models -- Planning Architecture -- Planning Logic Synthesis -- Test Mission -- Synthesis Scheme -- Planning Logic Components Evolution -- Computational Experiments -- General Setup -- Results -- Conclusions -- Major Feedback Loops Supporting Artificial Evolution in Multi-modular Robotics / Introduction -- Artificial Homeostatic Hormone System -- Artificial Genome -- Feedback 1: Classic Control -- Feedback 2: Learning -- Feedback 3: Evolution -- Feedback 4: Controller Morphogenesis -- Feedback 5: Robot Organism Morphogenesis -- Feedback 6: Body Motion -- Step 1: The First Oscillator -- Step 2: Motion of Bigger Organisms -- Step 3: Motion of More Complex Organisms -- Discussion -- Evolutionary Design and Assembly Planning for Stochastic Modular Robots / Introduction -- Target Structure Evolution -- Stochastic Fluidic Assembly System Model -- Assembly Algorithm -- Conclusion. Jean-Baptiste Mouret -- Jean-Marc Montanier, Nicolas Bredeche -- Petr Svec, Satyandra K. Gupta -- Thomas Schmickl, Jurgen Stradner, Heiko Hamann, Lutz Winkler, Karl Crailsheim -- Michael T. Tolley, Jonathan D. Hiller, Hod Lipson -- 10. 10.1. 10.2. 10.2.1. 10.2.2. 10.2.3. 10.3. 10.3.1. 10.3.2. 10.3.3. 10.3.4. 10.3.5. 10.4. 10.4.1. 10.4.2. 10.4.3. 10.5. 11. 11.1. 11.2. 11.2.1. 11.2.2. 11.3. 11.3.1. 11.3.2. 11.3.3. 11.3.4. 11.3.5. 11.4. 12. 12.1. 12.2. 12.2.1. 12.2.2. 12.3. 12.3.1. 12.3.2. 12.3.3. 12.4. 12.4.1. 12.4.2. 12.5. 13. 13.1. 13.2. 13.2.1. 13.3. 13.4. 13.5. 13.6. 13.7. 13.8. 13.8.1. 13.8.2. 13.8.3. 13.9. 14. 14.1. 14.2. 14.3. 14.4. 14.5.
"Evolutionary Algorithms (EAs) now provide mature optimization tools that have successfully been applied to many problems, from designing antennas to complete robots, and provided many human-competitive results. In robotics, the integration of EAs within the engineer's toolbox made tremendous progress in the last 20 years and proposes new methods to address challenging problems in various setups: modular robotics, swarm robotics, robotics with non-conventional mechanics (e.g. high redundancy, dynamic motion, multi-modality), etc. This book takes its roots in the workshop on "New Horizons in Evolutionary Design of Robots" that brought together researchers from Computer Science and Robotics during the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2009) in Saint Louis (USA). This book features extended contributions from the workshop, thus providing various examples of current problems and applications, with a special emphasis on the link between Computer Science and Robotics. It also provides a comprehensive and up-to-date introduction to Evolutionary Robotics after 20 years of maturation as well as thoughts and considerations from several major actors in the field. This book offers a comprehensive introduction to the current trends and challenges in Evolutionary Robotics for the next decade."--Publisher's website.
3642182712 9783642182716
Evolutionary robotics
629.892