We present in this paper a decentralized multirobot (aerial and ground) cooperation scheme for objects transportation. A team of ground mobile robots guided by a drone and a human operator moves in a coordinated way keeping a predefined formation in order to carry objects (tools, gas masks,...) in unsafe industrial areas. One ground mobile robot (leader) navigates among obstacles thanks to the waypoints provided by the drone and the human operator. The other ground mobile robots (followers) use a predictive vision based target tracking controller to keep a certain distance and bearing to the leader.

A stochastic ellipsoid modelling of repeatability is proposed for industrial manipulator robots. The covariance matrix of angular position is determined introducing the jump process, which reveals to be a first and second order stationary Gaussian process.From this accurate covariance matrix, the stochastic ellipsoid theory gives the density of position in the workspace around the mean position. Hence the pose repeatability index can be computed in different locations. Computed and experimental repeatability are compared. Experimental repeatability variability is studied. A new “intrinsic repeatability index” is proposed. In conclusion, the modelling reflects well the location and load influence on the repeatability.

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Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle.We present in this paper a Fuzzy Logic Controller (FLC) combined with a predictive algorithm to track an Unmanned Ground Vehicle (UGV), using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with a down facing camera. The video flow is sent continuously to a ground station to be processed in order to extract the location of the UGV and send the commands back to the UAV to follow autonomously the UGV. To emulate an experienced UAVs pilot, we propose a fuzzy-logic sets of rules. Double Exponential Smoothing Algorithm (DES) is used to filter the measurements and give the predictive value of the UGV pose. The FLC inputs are the filtered UGV position in the image plan and the derivative of its predicted value. The outputs are pitch and roll commands to be sent to the UAV. We show the efficiency of the proposed controller experimentally, and discuss the improvement of the tracking results compared to our previous work.

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