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Aubière, France

Lenain R.,IRSTEA | Berducat M.,IRSTEA | Cartade P.,IRSTEA | Thuilot B.,LASMEA
Precision Agriculture 2011 - Papers Presented at the 8th European Conference on Precision Agriculture 2011, ECPA 2011

The necessity of decreasing the environmental impact of agricultural activities, while preserving the level of production to satisfy growing population demands requires investigation of new production tools. Mobile robots may constitute a promising solution, since autonomous devices may allow increasing production levels, while preserving the environment due to their high accuracy. In this paper, the use of several mobile robots of medium size working in close co-operation is investigated. It is assumed that they can exchange data through wireless communication, and a co-ordination control law which is accurate despite typical off-road conditions (low grip, terrain irregularities, etc). It was designed using nonlinear observer-based adaptive control. The algorithm proposed in this paper was tested by advanced simulation, as well as experimental validation. Source

Bouton N.,IRSTEA | Lenain R.,IRSTEA | Thuilot B.,LASMEA | Martinet P.,LASMEA
Proceedings - IEEE International Conference on Robotics and Automation

Automation in outdoor applications (farming, surveillance, etc.) requires highly accurate control of mobile robots, at high speed, accounting for natural ground specificities (mainly sliding effects). In previous work, predictive control algorithms dedicated to All-Terrain Vehicle lateral stability was investigated. Satisfactory advanced simulation results have been reported but no experimental ones were presented. In this paper, the prevention of a real off-road mobile robot rollover is addressed. First, both rollover dynamic modeling and previous work on a Mixed observer designed to estimate on-line sliding phenomena for path tracking control are recalled. Then, this observer is here used to compute a rollover indicator accounting for sliding phenomena, from a low-cost perception system. Next, the maximum vehicle velocity, compatible with a safe motion over some horizon of prediction, is computed via Predictive Functional Control (PFC), and can then be applied, if needed, to the vehicle actuator to prevent from rollover. The capabilities of the proposed device are demonstrated and discussed thanks to real experimentation. ©2010 IEEE. Source

Cariou C.,IRSTEA | Lenain R.,IRSTEA | Thuilot B.,LASMEA | Martinet P.,LASMEA
Proceedings - IEEE International Conference on Robotics and Automation

This paper addresses the problem of path generation and motion control for the autonomous maneuver of a farm vehicle with a trailed implement in headland. A reverse turn planner is firstly investigated, based on primitives connected together to easily generate the reference motion. Then, both steering and speed control algorithms are presented to accurately guide the vehicle-trailer system. They are based on a kinematic model extended with additional sliding parameters and on model predictive control approaches. Real world experiments have been carried out on a low friction terrain with an experimental mobile robot pulling a trailer. At the end of each row, the reverse turn is automatically generated to connect the next reference track, and the maneuver is autonomously performed by the vehicle-trailer system. Reported experiments demonstrate the capabilities of the proposed algorithms. ©2010 IEEE. Source

Courbon J.,CEA Fontenay-aux-roses | Mezouar Y.,LASMEA | Guenard N.,CEA Fontenay-aux-roses | Martinet P.,LASMEA
Control Engineering Practice

This paper presents a vision-based navigation strategy for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) using a single embedded camera observing natural landmarks. In the proposed approach, images of the environment are first sampled, stored and organized as a set of ordered key images (visual path) which provides a visual memory of the environment. The robot navigation task is then defined as a concatenation of visual path subsets (called visual route) linking the current observed image and a target image belonging to the visual memory. The UAV is controlled to reach each image of the visual route using a vision-based control law adapted to its dynamic model and without explicitly planning any trajectory. This framework is largely substantiated by experiments with an X4-flyer equipped with a fisheye camera. © 2010 Elsevier Ltd. Source

Goyat Y.,LCPC | Chateau T.,LASMEA | Trassoudaine L.,LASMEA
Machine Vision and Applications

This article presents a probabilistic method for vehicle tracking using a sensor composed of both a camera and a laser rangefinder. Two main contributions will be set forth in this paper. The first involves the definition of an original likelihood function based on the projection of simplified 3D vehicle models. We will also propose an efficient approach to compute this function using a line-based integral image. The second contribution focuses on a sampling algorithm designed to handle several sources. The resulting modified particle filter is capable of naturally merging several observation functions in a straightforwardmanner.Many trajectories of a vehicle equipped with a kinematic GPS 1 have been measured on actual field sites, with a video system specially developed for the project. This field input has made it possible to experimentally validate the result obtained from the algorithm. The ultimate goal of this research is to derive a better understanding of driver behavior in order to assist road managers in their effort to ensure network safety. © Springer-Verlag 2008. Source

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