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Computer Science > Robotics
Title: PPNet: A Novel Neural Network Structure for End-to-End Near-Optimal Path Planning
(Submitted on 18 Jan 2024 (this version), latest version 23 Apr 2024 (v2))
Abstract: The classical path planners, such as sampling-based path planners, have the limitations of sensitivity to the initial solution and slow convergence to the optimal solution. However, finding a near-optimal solution in a short period is challenging in many applications such as the autonomous vehicle with limited power/fuel. To achieve an end-to-end near-optimal path planner, we first divide the path planning problem into two subproblems, which are path's space segmentation and waypoints generation in the given path's space. We further propose a two-level cascade neural network named Path Planning Network (PPNet) to solve the path planning problem by solving the abovementioned subproblems. Moreover, we propose a novel efficient data generation method for path planning named EDaGe-PP. The results show the total computation time is less than 1/33 and the success rate of PPNet trained by the dataset that is generated by EDaGe-PP is about $2 \times$ compared to other methods. We validate PPNet against state-of-the-art path planning methods. The results show PPNet can find a near-optimal solution in 15.3ms, which is much shorter than the state-of-the-art path planners.
Submission history
From: Adam Q. L. Meng [view email][v1] Thu, 18 Jan 2024 09:20:27 GMT (10704kb,D)
[v2] Tue, 23 Apr 2024 09:21:50 GMT (11533kb,D)
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