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AERCam (Autonomous Extra-vehicular Robotic Camera) is designed to provide astronauts and ground control camera views of the space shuttle and station. The _rest generation of AERCam, called AERCam Sprint, ewe on a shuttle mission in December 1997. AERCam Sprint was teleported by an astronaut inside the space shuttle.
AERCam's only autonomy was its ability to automatically stop its rotation when commanded to do so. The next generation of AERCam, called AERCam II, is currently in development at NASA Johnson Space Center. This robot will have additional autonomous functionality and will be controlled by an intelligent layered control architecture called 3T (Bonasso et al. , 1997).
This paper describes the path planning and control algorithms that direct AERCam's motions. First, we will describe the planar prototype for AERCam and the planar path planning algorithm that directs it. This path planning algorithm is based on the generalized Voronoi diagram (GVD), which has been commonly used in the motion planning _eld (Latombe, 1991). One of the contributions of this paper is that we use the GVD to locally optimize fuel usage of AERCam on the air bearing table.
The second part of this paper then upgrades the planar path planning approach to three-dimensions using a new motion planning structure called the generalized Voronoi graph (GVG), already described in prior work (Choset & Burdick, 1995a). Again, we use the GVG initially to _rest _nd a path and then apply a similar optimization technique to minimize fuel usage. Software simulations validate this approach for the space station.
AERCam's only autonomy was its ability to automatically stop its rotation when commanded to do so. The next generation of AERCam, called AERCam II, is currently in development at NASA Johnson Space Center. This robot will have additional autonomous functionality and will be controlled by an intelligent layered control architecture called 3T (Bonasso et al. , 1997).
This paper describes the path planning and control algorithms that direct AERCam's motions. First, we will describe the planar prototype for AERCam and the planar path planning algorithm that directs it. This path planning algorithm is based on the generalized Voronoi diagram (GVD), which has been commonly used in the motion planning _eld (Latombe, 1991). One of the contributions of this paper is that we use the GVD to locally optimize fuel usage of AERCam on the air bearing table.
The second part of this paper then upgrades the planar path planning approach to three-dimensions using a new motion planning structure called the generalized Voronoi graph (GVG), already described in prior work (Choset & Burdick, 1995a). Again, we use the GVG initially to _rest _nd a path and then apply a similar optimization technique to minimize fuel usage. Software simulations validate this approach for the space station.
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