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This paper introduces a novel path planning method under non-holonomic constraint for car-like vehicles, which associates map discovery and heuristic search to attain an optimal resultant path. The map discovery applies fast marching method to investigate the map geometric information. After that, the support vector machine is performed to find obstacle clearance information. This information is then...
We propose a path planning method for autonomous vehicle in cluttered environment with narrow passages. Different from traditional methods, we use a learning approach based on RBF kernel SVM to maximize the safety margin for driving. We use the Lagrange multipliers of SVM dual model to find most critical points in map and generate optimized hyperplane for path. The method is implemented on autonomous...
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