Visual servoing requires an object in the field of view of the camera, in order to control the robot evolution. Otherwise, the virtual link is broken and the control loop cannot continue to be closed. In this paper, a novel approach is presented in order to guarantee that the object remains in the field of view of the camera during the whole robot motion. It consists in tracking an iteratively computed trajectory. A position based modeling adapted to a moving target object is established, and is used to control the trajectory. A nonlinear decoupling approach is then used to control the robot. Experiments, demonstrating the capabilities of this approach, have been conducted on a Cartesian robot connected to a real time vision system, with a CCD camera mounted on the end effector of the robot.

The three point hitch controller of an agricultural farm tractor is part of an experimental setup that performs step response. A graphical representation of the data is introduced: it allows an operator to detect long term trends. A more involved approach, relying upon the linear Hough transform, automatically estimates the latency time and the rise time of the system, even in the presence of short pulse noise. Such a method has been used on an agricultural farm tractor. Thousands of experiments have been carried out and analyzed in a few hours, allowing a swift qualification of the closed loop system.

This paper presents a path planning algorithm for a tractor to execute specific farmwork tasks requiring the complete coverage of a field while minimizing the overlapping between successive passages. The proposed method is based on the determination of a set of characteristic points from which a graph is defined. The covering trajectory is determined by means of a greedy algorithm associated to a heuristic and a cost function minimization which is used to determine the best Hamiltonian solution in the graph.

Precision agriculture involves very accurate farm vehicle control along recorded paths, which are not necessarily straight lines. We investigate the possibility of achieving this task with CP-DGPS (carrier phase differential GPS) as the unique sensor. The vehicle heading is derived according to a Kalman state reconstructor, and a nonlinear velocity independent control law is designed, relying on chained systems properties. Field experiments, demonstrating the capabilities of our guidance system, are reported and discussed.