This paper proposes a novel cell decomposition approach for finding shortest paths through a known, three-dimensional (3-D) environment or conditional shortest paths through an unknown, 3-D environment. A conditional shortest path is a collision-free path of shortest distance which is computed from environmental information which is known at a given time. The proposed 3-D path planning methods are based on a new data structure called the framed-octree and on several techniques of computational geometry. These methods compute a distance transform using a spherical path planning wave in either the L 1 or L1 metric. Our methods combine together the accuracy of three-dimensional, grid-based path planning techniques with the efficiency of octree-based path planning techniques, thereby having the advantages of both kinds of these techniques and avoiding their disadvantages. These methods readily support multiple goals, allow an efficient environmental update strategy, and do not place any unrealistic constraints on either the obstacles or on the environment.
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