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Collision checking is generally considered to be the primary computational bottleneck in sampling-based motion planning algorithms. We show that this does not have to be the case. More specifically, we introduce a novel way of implementing collision checking in the context of sampling-based motion planning, such that the amortized complexity of collision(More)
C-FOREST is a parallelization framework for single-query sampling-based shortest path-planning algorithms. Multiple search trees are grown in parallel (e.g., 1 per CPU). Each time a better path is found, it is exchanged between trees so that all trees can benefit from its data. Specifically, the path's nodes increase the other trees' configuration space(More)
Algorithms such as Field-D* [1] use linear interpolation to infer continuous fields of costdistance-to-goal, where costdistance is cost integrated over distance. Traditionally, field values have been used as direct input to trajectory planners. In contrast, we focus on extracting a minimum costdistance path between two points, given the continuous field. We(More)
Autonomous robots in unknown and unstructured environments must be able to distinguish safe and unsafe terrain in order to navigate effectively. Stereo depth data is effective in the near field, but agents should also be able to observe and learn perceptual models for identifying traversable surfaces and obstacles in the far field. As the robot passes(More)
An approach to stereo based local path planning in unstructured environments is presented. The approach differs from previous stereo based and image based planning systems (e.g. top-down occupancy grid planners, autonomous highway driving algorithms, and view-sequenced route representation), in that it uses specialized cost functions to find paths through(More)
Path planning systems using graph-search algorithms such as A* usually operate in uniform plan-view occupancy grids. However, the sensors used to construct these grids observe the environment in their own sample space based on sensor type and viewpoint. In this paper we present an image space technique for path planning in unknown unstructured outdoor(More)
We present RRT X , the first asymptotically optimal sampling-based motion planning algorithm for real-time navigation in dynamic environments (containing obstacles that unpredictably appear, disappear, and move). Whenever obstacle changes are observed, e.g., by onboard sensors, a graph rewiring cascade quickly updates the search-graph and repairs its(More)
We present an any-com algorithm that enables a decentralized team of robots to share the work of collision checking while each robot independently calculates its own motion plan. In our method “safety-certificates” (i.e., bounds on the collision-free subspace around each collision-checked point [1]), are shared among the team so that all(More)