Metaheuristics for Robotics. Hamouche Oulhadj

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Metaheuristics for Robotics - Hamouche Oulhadj


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      Preface

      Optimization deals with methods that make it possible to optimize the use, operation and performance conditions of a system, whether it is physical or related to human activity. Situated at the crossroads of several disciplines, namely applied mathematics, computer science and artificial intelligence, optimization makes it possible to quickly find a solution to numerous problems which would otherwise be more difficult to solve relying solely on restricted mathematical analysis.

      Based on heuristics or even metaheuristics at a higher level, optimization methods provide operational solutions, which are not necessarily optimal solutions but eligible suboptimal solutions, so-called acceptable solutions, because they demonstrate the level of performance required to reach a goal without conflicting with associated constraints. A number of problems whose resolution is based on optimization methods can be provided as examples:

       — in finance, tax optimization is a means to minimize legal taxation;

       — in databases, query optimization makes it possible to improve the accessibility of shared data, in particular by reducing transaction time;

       — in telecommunications networks, routing optimization is a method for finding suitable paths for data exchange;

       — in robotics, optimization allows, to take just one example, the identification of the best configurations (joint variables) that a robot must undergo in order to efficiently perform a task;

       — in computer science, optimizing the code of a program makes it possible to reduce the memory space occupied and to increase the convergence time.

      The applications outlined above are inherently very complex, which raises the problems of implementing accurate mathematical methods that provide admissible solutions without having to mobilize huge computational resources. In these circumstances, we are limited to considering approximated solutions, which are suboptimal solutions, but acceptable, ones since they guarantee goal realization while satisfying constraints.

      In this book, we will address issues specifically related to the field of medical robotics. The focus of the applications being considered is on trajectory planning for redundant manipulative arms (articulated robots) within the context of surgical gesture assistance, and robust control for effort compensation or physical assistance in disability situations (exoskeleton). These applications are presented in detail, with the aim of understanding with the utmost clarity the problems to be solved, as well as the choices made to find effective solutions within a reasonable time frame.

      Hamouche OULHADJ

      Boubaker DAACHI

      Riad MENASRI

      November 2019

      Introduction

      This work is part of a collection of books, published by ISTE and Wiley, devoted to metaheuristics and their applications. Known for being specific and particular algorithms, what practical interest do metaheuristics have to make them increasingly attractive to engineers, researchers and scientists from various areas interested in different fields of application? There are two important arguments that provide us with obvious answers: on the one hand, the scope of application of metaheuristics is constantly gaining momentum, without apparently being concerned with any limitations; on the other hand, these resolution methods possess a high level of abstraction, which makes them adaptable to a wide range of engineering problems. Furthermore, a small number of necessary adjustments that do not change the nature of algorithms are usually sufficient to solve new optimization problems without any particular links existing between them.

      In practice, there may be several existing solutions to an optimization problem, of which only one of these solutions is generally optimal. All others are suboptimal solutions, but are still eligible, so-called acceptable, solutions because they guarantee the completion of an objective without violating associated constraints. However, the notion of optimizing acceptability may appear to be overly abstract: how can the level of solution operability be identified when the level of appreciation of that solution may vary not only from one user to another, but also with the margin of error tolerated by each type of application? Clearly, there is no absolute answer to this question, because it is ultimately each individual who decides how to define the level of acceptability for a solution, based on individual needs and the quality of the results sought for the application to be addressed.

      Today, metaheuristics have become almost unavoidable in numerous areas of engineering due to the difficulties that have to be overcome to properly solve common optimization problems. These difficulties generally lie in the complex nature of the systems under study: the number of constraints and decision variables to be taken into account can be very high, computational times can be very long and non-differentiable objective functions can be highly multimodal or even too complex to be mathematically formalized with accuracy. The field of robotics is by nature a very broad field of application. In fact, these very relevant algorithms can be found in many applications of robotics:

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