deletions | additions       diff --git a/Additionally_Kinect_Sensors_can_also__.tex b/Additionally_Kinect_Sensors_can_also__.tex  index d85ae06..ffb6cac 100644  --- a/Additionally_Kinect_Sensors_can_also__.tex  +++ b/Additionally_Kinect_Sensors_can_also__.tex 
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  Avoiding an undesired collision is indeed the safest approach for human-robot coexistence. The main information needed by any on-line collision avoidance algorithm is the relative distance between the robot and some obstacle in its workspace, as acquired by exteroceptive sensors either fixed in the environment or mounted on the robot. The performance of the algorithm depends also on the fast processing capability of the sensor data. In \citet{Flacco_2012}'s study, he have proposed a new efficient method for estimating obstacle-to-robot distances that works directly in the depth space associated to a depth sensor (e.g., a Kinect monitoring the HRI scene).    A collision detection system based on a velocity-distance bound algorithm was implemented. The system consist of three components: a solid modeler, a kinematic simulator, and a collision detection control module. The solid modeler defines the solids, it derives approximation to the solids, in to a given positions, the solids' geometry transforms, and for any two solids, it provides the previously discussed extended distance function. The simulator, using kinematic models for robot links, it defines the where all of the solids are placed. and provides an interactive example and user interface. THe collision detection module will use the simulator to define where the solids' are placed at a time t once invoked by the user using the simulator, use the modeler, as discussed previously, to find the extended distance function values for the solids at time t, choose a $dt$ to repeat the simulation/detection cycle at $t + dt$ \cite{culley1986collision}.