Metaheuristics for Robotics

Hamouche Oulhadj is an Associate Professor at the University of Paris-Est Créteil, France. He is an Engineer in Electrical Engineering and has a PhD in Biomedical Engineering. His main research interests are in optimization, pattern recognition and image processing.Boubaker Daachi is a Full Professor in Computer Science at the University of Paris 8, France. He is an Engineer in Computer Science and has a PhD in Robotics. His main research interests are in brain computer interfaces, biometrics, neurofeedback and robotics.Riad Menasri is a Development Engineer at Assystem Technologies, France. Holding a Master's degree in Advanced Systems and Robotics and a PhD in Robotics, his main research interests are in optimization and trajectory planning for robotics applications.

• Preface ixIntroduction xiiiChapter 1. Optimization: Theoretical Foundations and Methods 11.1. The formalization of an optimization problem 11.2. Constrained optimization methods 51.2.1. The method of Lagrange multipliers 91.2.2. Method of the quadratic penalization 111.2.3. Methods of interior penalties 121.2.4. Methods of exterior penalties 131.2.5. Augmented Lagrangian method 141.3. Classification of optimization methods 151.3.1. Deterministic methods 161.3.2. Stochastic methods 181.4. Conclusion 211.5. Bibliography 22Chapter 2. Metaheuristics for Robotics 272.1. Introduction 272.2. Metaheuristics for trajectory planning problems 282.2.1. Path planning 292.2.2. Trajectory generation 432.3. Metaheuristics for automatic control problems 452.4. Conclusion 502.5. Bibliography 50Chapter 3. Metaheuristics for Constrained and Unconstrained Trajectory Planning 533.1. Introduction 533.2. Obstacle avoidance 543.3. Bilevel optimization problem 583.4. Formulation of the trajectory planning problem 593.4.1. Objective functions 603.4.2. Constraints 623.5. Resolution with a bigenetic algorithm 633.6. Simulation with the model of the Neuromate robot 663.6.1. Geometric model of the Neuromate robot 673.6.2. Kinematic model of the Neuromate robot 713.6.3. Simulation results 723.7. Conclusion 833.8. Bibliography 83Chapter 4. Metaheuristics for Trajectory Generation by Polynomial Interpolation 874.1. Introduction 874.2. Description of the problem addressed 884.3. Formalization 914.3.1. Criteria 914.3.2. Constraints 924.4. Resolution 944.4.1. Augmented Lagrangian 954.4.2. Genetic operators 974.4.3. Solution coding 994.5. Simulation results 1004.6. Conclusion 1164.7. Bibliography 118Chapter 5. Particle Swarm Optimization for Exoskeleton Control 1215.1. Introduction 1215.2. The system and the problem under consideration 1235.2.1. Representation and model of the system under consideration 1235.2.2. The problem under consideration 1255.3. Proposed control algorithm 1265.3.1. The standard PSO algorithm 1265.3.2. Proposed control approach 1285.4. Experimental results 1355.5. Conclusion 1425.6. Bibliography 143Conclusion 147Index 153