California Institute of Technology

Anthony T S Fragoso

Richard Martin Murray

Roland Brockers

Larry H Matthies

A method and system provide the ability to automatically control a vehicle to avoid obstacle collision. Range data of a real-world scene (including depth data to static objects) is acquired. Positions and velocities of moving objects are acquired. The range data is combined into an egospace representation for pixels in egospace that is specified with respect to a radially aligned coordinate system. An apparent size of the static objects is expanding, in the egospace representation, based on a dimension of the vehicle. A speed of the vehicle is specified. A velocity obstacle corresponding to the moving objects is constructed. A mask is created in the coordinate system and identifies candidate radial paths that will result in a collision between the vehicle and the moving objects. The mask is combined with the egospace representation that is then used to determine a path for the vehicle.

Simultaneous representation of moving and static obstacles for automatically controlled vehicles

A method, device, framework, and system provide the ability to control an unmanned aerial vehicle (UAV) to avoid obstacle collision. Range data of a real-world scene is acquired using range sensors (that provide depth data to visible objects). The range data is combined into an egospace representation (consisting of pixels in egospace). An apparent size of each of the visible objects is expanded based on a dimension of the UAV. An assigned destination in the real world scene based on world space is received and transformed into egospace coordinates in egospace. A trackable path from the UAV to the assigned destination through egospace that avoids collision with the visible objects (based on the expanded apparent sizes of each of the visible objects) is generated. Inputs that control the UAV to follow the trackable path are identified.

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Los Angeles, CA, United States of America

Anthony T S Fragoso