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This article discusses the dynamic identification of manipulator robots widely used in production facilities. These robots are complex, multi-articulated electromechanical systems that have been studied since the 1950s. This led to the development of a mathematical model representing their dynamic behavior to develop their control, simulation, and design. This model depends on parameters that are not known with sufficient precision. Their identification is essential for the exploitation of the mathematical model. Robot identification is a well-explored and mature field today. This article presents a set of mathematical tools as well as three identification methods illustrated by experimental results.
Planning a robot's movements requires a map and a localization method. SLAM (Simultaneous Localisation and Mapping) algorithms enable these maps to be constructed autonomously while estimating the robot pose. The techniques employed are varied, in terms of the representations produced, algorithmic approaches and sensors used. This article presents the main classes of algorithms and common methods for data correlation, filtering and optimization, as well as their concrete applications.
Robotic solutions can meet the need to compensate for deficiencies, but it is essential to take into account the clinical and usage purpose, and to co-design them with users to ensure they are adapted and to test their acceptability. In this article, we will address the development process of these systems, particularly as applied to navigation assistance for power wheelchairs, where the goal is to avoid obstacles, but also to integrate more complex functionalities such as semi-autonomous assistance or social navigation. In this context, the physical human-robot interface and intention detection are of crucial importance.
Robots are taking an increasingly important place in Society, which is why, in order to guarantee sustainable robotic development, it is necessary to think as early as possible about their eco-design. Thus, this article makes a focus on the environmental impacts of robots and the means that exist or are being developed to potentially reduce them. Two main classes of techniques to reduce some environmental impacts of robots are highlighted : techniques to reduce energy consumption and replacement of robot bodies by parts made of materials with low environmental impact.
Compared to traditional rigid robots, continuum robots (CRs) are made up of flexible bodies that undergo large-scale deformation. Their movements are achieved by controlling the deformations of their constituent bodies.Their intrinsic flexibility means they can be used in many fields where safe interaction with the environment is required. There are several types of CRs, including continuum parallel robots (CPRs), which consist of a parallel assembly of several flexible bodies.In this article, CPRs will be studied, going through their different designs, their models, as well as investigating their stability properties.
This article discusses robotic control methods adapted to the continuous physical interaction between human and robot to accomplish common tasks. The article describes so-called "compliant" control methods (impedance or admittance), which exploit the robot’s dynamic model and adapt to the humman’s forces interaction. The principle of compliance adaptation is also detailed and applied to a typical use case of object co-manipulation (with or without uncertainties).
The robotization of large spaces requires in-depth expertise in robotic behavior, follow-up on rigorous methodologies, and a precise understanding of the impact of processes on robots. This document examines various robotic architectures, assesses robot performance in terms of accuracy and repeatability, and presents concrete examples to structure needs. It also explores the scientific challenges associated with integrating robots into large spaces, the available measurement tools, and highlights the specificities of architectures based on the tasks to be performed. Finally, a methodology is proposed for effective implementation, emphasizing the challenges to be addressed and the future prospects for robotics in these environments.
The principles of the Light Detection and Ranging (LiDAR) sensor when applied to intelligent vehicles are presented in this article. LiDAR provide 3D information on their immediate environment by emitting a laser beam that reflects in objects nearby, allowing for the measurement of their distance. In this article the principles are presented to describe the way the perceived data is generated, it includes examples of the mainstream LiDAR. The processing of the acquired data using different perception algorithms is included to provide an understanding of the techniques used to classify and track the objects of interests. The purpose is to convey to the reader the basic principles of this sensor that is not only used as part of autonomous vehicles but also in driving assistance functions.
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