Videos

Demonstration of the INSPEX system prototype in a real world scenario for real-time 3D detection, location and warning of obstacles in indoor and outdoor environments.

The INSPEX system is embedded on a white cane to help Visually Impaired and Blind (VIB) people detect and avoid obstacles on their path while moving around. The system is connected to the Gosense guidance application Wizigo® and feedback is provided by 3D spatial audio.

This video, has been presented during the final Review meeting demonstration of the H2020 INSPEX project. This video has been edited by François Birot.

This video presents a distributed trajectory generator for formation control of multi-robot systems. The desired formation is defined by its geometric parameters but the position of each robot in the formation is not predefined a priori. The contribution is the design of a distributed algorithm to compute the robots' positions with respect to a given target while maintaining a particular formation which can be reconfigured on-line. A tracking controller ensures the convergence of the robots to their desired positions.

Crosstalk occurs when the laser beam emitted by a LIDAR disturbs the measurement process of another LIDAR. The analysis of the effect of crosstalk is therefore becoming crucial for assessing the performance of LIDAR devices and ensuring the safety of autonomous vehicles. A detailed and replicable methodology for evaluating the impact of crosstalk for LIDARs based on different technologies is proposed and an exhaustive analysis of the experimental results shows how the crosstalk impact can significantly degrade the precision of LIDAR measurements.

For more information, see the paper:

A methodology for analyzing the impact of crosstalk on LIDAR measurements, L. Briñón Arranz, T. Rakotovao, T. Creuzet, C. Karaoguz and O. El-Hamzaoui. In IEEE Sensors conference, Nov. 2021.

Simulation of five agents governed by a time-varying formation control law. The vehicles describe a circular motion tracking a given time-varying center and whose radius follows a time-varying reference. In addition, a cooperative term distributes the agents uniformly along the formation.

For more information, see the paper:

Cooperative Control Design for Time-Varying Formations of Multi-Agent Systems, L. Briñón Arranz, A. Seuret and C. Canudas de Wit. In IEEE Transactions on Automatic Control, Aug. 2014.

Simulation of a circular formation of agents implementing a distributed source-seeking algorithm. The vehicles are able to measure the signal emitted by a source. Thanks to the spatially distributed measurements collected by the agents, the gradient direction of the signal can be estimated. A distributed source-seeking algorithm based on consensus drives the center of the formation to the source location.

For more information, see the paper:

Consensus-based Source-seeking with a Circular Formation of Agents, L. Briñón Arranz and L. Schenato. In Proceedings of the European Control Conference, Zürich, Switzerland, 2013.

Simulation of six agents governed by a cooperative translation control law. The fleet of agents describes a circular motion tracking a time-varying center. Each vehicle computes its own center (blue trajectories). Due to a consensus algorithm with group reference velocity all the centers converge to the same value and follow the same reference velocity. Finally, in order to drive the formation to the source location of a scalar field of interest, the reference velocity is the gradient of that signal strength.

For more information, see the paper:

Cooperative translation control based on consensus with reference velocity: a source-seeking application, L. Briñón Arranz and A. Seuret. In Proceedings of the European Control Conference, Zürich, Switzerland, 2013.

This video describes some of the results of CONNECT and FeedNetBack projects. A fleet of five agents describes a circular motion whose center tracks a time-varying reference (yelow point) and whose radius changes over time. Moreover, thanks to a potential term, the vehicles are uniformly distributed along the formation. The green spheres represent the communication region of the agents.

This video was edited by Jonathan Dumon.

Simulation of five agents governed by a contraction control law with a potential term in order to distribute uniformly the agents along the circle. The blue circles represent the limited communication region of each vehicle.

For more information, see the paper:

Contraction Control of a Fleet Circular Formation of AUVs under Finite Communication Range, L. Briñón Arranz, A. Seuret and C. Canudas de Wit. In Proceedings of the 2010 American Control Conference, Baltimore, USA, 2010.