Laboratoire d’analyse et d’architecture des systèmes
A.OLLERO, G.HEREDIA, A.FRANCHI, G.ANTONELLI, K.KONDAC, A.S.CORTES, A.VIGURIA, J.R.MARTINEZ-DE-DIOS, F.PIERRI, J.CORTES, A.SANTAMARIA-NAVARRO, M.A.TRUJILLO SOTO, R.BALACHANDRAN, J.ANDRADE-CETTO, A.RODRIGUEZ CASTANO
Seville, RIS, UNICAS, DLR, UPC, CATEC, University of Basilicata, IRI, UPC/CSIC
Revue Scientifique : IEEE Robotics and Automation Magazine, 9p., Octobre 2018, DOI: 10.1109/MRA.2018.2852789 , N° 18297
This paper summarizes new aerial robotic manipulation technologies and methods, required for outdoor industrial inspection and maintenance, developed in the AEROARMS project. It presents aerial robotic manipulators with dual arms and multi-directional thrusters. It deals with the control systems, including the control of the interaction forces and the compliance, the teleoperation, which uses passivity to tackle the trade-off between stability and performance, perception methods for localization, mapping and inspection, and planning methods, including a new control-aware approach for aerial manipulation. Finally, it describes a novel industrial platform with multi-directional thrusters and a new arm design to increase the robustness in industrial contact inspections. The lessons learned in the application to outdoor aerial manipulation for inspection and maintenance are pointed out.
R.BAILON-RUIZ, A.BIT-MONNOT, S.LACROIX
Manifestation avec acte : IEEE/RSJ International Conference on Intelligent Robots and Systems ( IROS ) 2018 du 01 octobre au 05 octobre 2018, Madrid (Espagne), Octobre 2018, 6p. , N° 18261
We present an approach to plan trajectories for a fleet of fixed-wing UAVs to observe a wildfire evolving over time. Realistic models of the terrain, of the fire propagation process, and of the UAVs are exploited, together with a model of the wind. The approach tailors a generic Variable Neighborhood Search method to these models and associated constraints. Simulation results show ability to plan observation trajectories for a small fleet of UAVs, and to update the plans when new information on the fire are incorporated in the fire model.
C.GABELLIERI, M.TOGNON, L.PALLOTTINO, A.FRANCHI
Rapport LAAS N°18263, Septembre 2018, 13p.
This work investigates collaborative aerial transportation by swarms of agents based only on implicit information, enabled by the physical interaction among the agents and the environment. Such a coordinating mechanism in collaborative transportation is a basic skill in groups of social animals. We consider cable-suspended objects transported by a swarm of flying robots and we formulate several hypothesis on the behavior of the overall system which are validated thorough numerical study. In particular, we show that a nonzero internal force reduces to one the number of asymptotically stable equilibria and that the internal force intensity is directly connected to the convergence rate. As such, the internal force represents the cornerstone of a communication-less cooperative manipulation paradigm in swarms of flying robots. We also show how a swarm can achieve a stable transportation despite the imprecise knowledge of the system parameters.
I.ARDI, H.CARFANTAN, S.LACROIX, A.MONMAYRANT
PHOTO, IRAP, RIS
Manifestation avec acte : European Signal Processing Conference ( EUSIPCO ) 2018 du 03 septembre au 07 septembre 2018, Rome (Italie), Septembre 2018, 5p. , N° 18238
We consider the problem of hyperspectral cube reconstruction with a new controllable imaging system. The reconstruction with a small number of images acquired with different configurations of the imager avoids a complete scanning of the hyperspectral cube. We focus here on a quadratic penalty reconstruction approach, which provides a fast resolution thanks to the high sparsity of the involved matrices. While such a regu-larization is known to smooth the restored images, we propose to exploit the system capability to acquire the panchromatic image of the scene, to introduce prior information on the sharp edges of the image, leading to a fast and edge-preserved reconstruction of the image.
A.ESTANA, K.MOLLOY, M.VAISSET, N.SIBILLE, T.SIMEON, P.BERNADO, J.CORTES
CBS, RIS, IDEA
Revue Scientifique : Parallel Computing, Vol.77, pp.84-100, Septembre 2018, DOI 10.1016/j.parco.2018.06.005 , N° 18176
The study of the conformational energy landscape of a molecule is essential for the understanding of its physicochemical properties. This requires the exploration of a continuous, high-dimensional space to identify the most probable conformations and the transition paths between them. The problem is computationally difficult, in particular for highly-flexible biomolecules such as Intrinsically Disordered Proteins (IDPs). In recent years, a robotics-inspired algorithm called Transition-based Rapidly-exploring Random Tree (TRRT) has been proposed to solve this problem, and has been shown to provide good results with small and middle-sized biomolecules. Aiming to treat larger systems, we propose a hybrid strategy for the efficient parallelization of a multi-tree variant of TRRT, called Multi-TRRT, enabling an efficient execution in (possibly large) computer clusters. The parallel algorithm uses OpenMP multi-threading for computation inside each multi-core processor and MPI to perform the communication between processors. Results show a near-linear speedup for a wide range of cluster configurations. Although the paper mainly deals with the application of the proposed parallel algorithm to the investigation of biomolecules, the explanations concerning the methods are general, aiming to inspire future work on the parallelization of related algorithms.
R.CHATILA, E.RENAUDO, M.ANDRIES, R.O.CHAVEZ-GARCIA, P.LUCE-VEYRAC, R.GOTTSTEIN, R.ALAMI, A.CLODIC, S.DEVIN, B.GIRARD, M.KHAMASSI
ISIR, Jussieu, RIS, RAP
Revue Scientifique : Frontiers in Robotics and AI, art.88, Vol.5, 20p., Août 2018 , N° 18248
Despite major progress in Robotics and AI, robots are still basically " zombies " repeatedly achieving actions and tasks without understanding what they are doing. Deep-Learning AI programs classify tremendous amounts of data without grasping the meaning of their inputs or outputs. We still lack a genuine theory of the underlying principles and methods that would enable robots to understand their environment, to be cognizant of what they do, to take appropriate and timely initiatives, to learn from their own experience and to show that they know that they have learned and how. The rationale of this paper is that the understanding of its environment by an agent (the agent itself and its effects on the environment included) requires its self-awareness, which actually is itself emerging as a result of this understanding and the distinction that the agent is capable to make between its own mind-body and its environment. The paper develops along five issues: agent perception and interaction with the environment; learning actions; agent interaction with other agents—specifically humans; decision-making; and the cognitive architecture integrating these capacities.
M.FURCI, D.BICEGO, A.FRANCHI
Manifestation avec acte : FAC Symposium on Robot Control ( SYROCO ) 2018 du 27 août au 30 août 2018, Budapest (Hongrie), Août 2018, 6p. , N° 18249
In this paper we consider fully-actuated and redundantly-actuated robots, whose saturated inputs can have high bandwidth or can be slowly varying (with dynamics). The slowly varying inputs can be considered as configurations for the system. The proposed strategy allows to find the optimal actuators' configuration to optimize a cost function as the manipulability or the energy consumption. The approach allows for both a static design, which can include actuators' parameters such as position, orientation, saturations, numbers of actuators, and for a dynamic design, where the configurations can be controlled by an input of the system. A generalized solution to the optimal problem is proposed with the use of genetic algorithms. The results are validated in two simulation scenarios: a reconfiguration of the actuators orientation of an redundantly-actuated planar robot for trajectory tracking and the design optimization of the orientation of the motors in a generalized hexa-rotor with arbitrary propeller orientation.
M.RYLL, D.BICEGO, A.FRANCHI
Manifestation avec acte : FAC Symposium on Robot Control ( SYROCO ) 2018 du 27 août au 30 août 2018, Budapest (Hongrie), Août 2018, 6p. , N° 18262
V.WALTER, N.STAUB, M.SASKA, A.FRANCHI
Manifestation avec acte : IEEE International Conference on Automation Science and Engineering ( CASE ) 2018 du 20 août au 24 août 2018, Munich (Allemagne), Août 2018, 6p. , N° 18165
A novel vision-based approach for indoor/outdoor mutual localization on Unmanned Aerial Vehicles (UAVs) with low computational requirements and without external infrastructure is proposed in this paper. The proposed solution exploits the low natural emissions in the near-UltraViolet (UV) spectrum to avoid major drawbacks of the visible spectrum .Such approach provides much better reliability while being less computationally intensive. Working in near-UV requires active markers, which can be leveraged by enriching the information content through blinking patterns encoded marker-ID. In order to track the markers motion and identify their blinking frequency, we propose an innovative use of three dimensional Hough Transform, applied to stored position-time points. The proposed method was intensively tested onboard multi-UAV systems in real-world scenarios that are very challenging for visible-spectrum methods.The results of our methods in terms of robustness, reliability and precision, as well as the low requirement on the system deployment, predestine this method to be an enabling technology for using swarms of UAVs.
K.MOLLOY, L.DENARIE, M.VAISSET, T.SIMEON, J.CORTES
Revue Scientifique : International Journal of Robotics Research, 14p., Août 2018 , N° 18225
This paper addresses the simultaneous design and path planning problem, in which features associated to the bodies of a mobile system have to be selected to find the best design that optimizes its motion between two given configurations. Solving individual path planning problems for all possible designs and selecting the best result would be a straightforward approach for very simple cases. We propose a more efficient approach that combines discrete (design) and continuous (path) optimization in a single stage. It builds on an extension of a sampling-based algorithm, which simultaneously explores the configuration-space costmap of all possible designs aiming to find the best path-design pair. The algorithm filters out unsuitable designs during the path search, which breaks down the combinatorial explosion. Illustrative results are presented for relatively simple (academic) robotic examples, showing that even in these simple cases, the computational cost can be reduced by two orders of magnitude with respect to the na¨ıvena¨ıve approach. A preliminary application to challenging problems in computational biology related to protein design is also discussed at the end of the paper.