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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)
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)
In (Otte and Correll 2013) we present C-FOREST, a parallelization framework for single-query sampling-based shortest-path planning algorithms. C-FOREST has been observed to have super linear speedup on many problems, e.g., paths of quality Ltarget are found 350X faster by 64 CPUs working in parallel than by 1 CPU. In (Otte and Correll 2013) C-FOREST is(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 (i.e. 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)
We are interested in finding solutions to the multi-robot path-planning problem that have guarantees on completeness, are robust to communication failure, and incorporate varying team size. In this paper we present an algorithm that addresses the complete multi-robot path-planning problem from two different angles. First, dynamic teams are used to minimize(More)
— In sampling-based motion planning algorithms the initial step at every iteration is to generate a new sample from the obstacle-free portion of the configuration space. This is usually accomplished via rejection sampling, i.e., repeatedly drawing points from the entire space until an obstacle-free point is found. This strategy is rarely questioned because(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 robots can(More)