In the collision detection task, the main outcomes were correct detections and response to a secondary identification task.
The experimental results acquired during a collision avoidance task are also reported and discussed.
On the first occasion, volunteers also carried out a collision detection task.
In this work, we use a neural network to produce an output that is similar to the range measurements of infrared range sensors, and we use this output to guide the behavior of the robot in a collision-avoidance task.
Thornton, Bülthoff, Horowitz, Rynning, and Lee (2014) found a comparable result in which people could control more items in a collision-avoidance task than they could monitor in a tracking task.
In addition, indices of safety critical behaviour such as minimum time to collision in following tasks and the incidence of overtaking manoeuvres were recorded.
Computing task of collision detection consists of parallelized parts by GPU and sequential parts.
Such actions can be requests for more accurate orbital data, special sensor tasking, or collision avoidance maneuvers.
In robotics, grid maps are often used for solving tasks like collision checking, path planning, and localization.
The solution obtained allows to program the task following collision-free trajectories to perform an optimum sequence of polishing curves.
