This paper presents a set membership method (named Interval Analysis Localization (IAL)) to deal with the global localization problem of mobile robots. By using a LIDAR (LIght Detection And Ranging) range sensor, the odometry and a discrete map of an indoor environment, a robot has to determine its pose (position and orientation) in the map without any knowledge of its initial pose. In a bounded error context, the IAL algorithm searches a set of boxes (interval vector), with a cardinality as small as possible that includes the robot’s pose. The localization process is based on constraint propagation and interval analysis tools, such as bisection and relaxed intersection. The proposed method is validated using real data recorded during the CAROTTE challenge, organized by the French ANR (National Research Agency) and the French DGA (General Delegation of Armament). IAL is then compared with the well-known Monte Carlo Localization showing weaknesses and strengths of both algorithms. As it is shown in this paper with the IAL algorithm, interval analysis can be an efficient tool to solve the global localization problem.

This paper presents a navigation approach for autonomous agricultural robots based on LiDAR data. This navigation approach is divided into two parts: a line finding algorithm and a control algorithm. The paper proposes several line finding algorithms (based on PEARL/Ruby approach) that extract lines from a LiDAR data set. Once the lines have been processed from the data set, a control algorithm filters these lines and, using a fuzzy controller, generates the wheel speed commands to move the robot among the crop rows. This navigation approach was tested using a simulator built on ROS middle-ware and Gazebo (the source codes of the simulation are available on GitHub). The results of the simulated experiments show that the proposed approach performs well for a large range of crop configurations (with or without considering weeds, with or without holes in the crop rows…).

The choice of actuation when designing a bipedal robot is an important matter, as it impacts its capacities and its control. In these aspects, electric direct-drive (DD) linear motors are interesting candidates. Indeed, they can reproduce the human muscle system by using monoarticular and bi-articular motors and offer a natural backdrivability for impact mitigation. However, motor efficiency depends on their elongation and their attachment points. The ROBIBIO (RObot humanoïde BI-articulaire BIO-inspiré) project aims to design and build a planar bipedal robot using electric direct-drive linear motors for the actuation. To do so, a simulator was developed to evaluate multiple human-like bipedal robot architectures over a set of motion-captured movements. Furthermore, a numerical optimization was done to find the best motor placement for each architecture.