P3Net: PointNet-based Path Planning on FPGA
P3Net: PointNet-based Path Planning on FPGA
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DOI：
10.1109/icfpt56656.2022.9974251
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
K. Sugiura;Hiroki Matsutani
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文献类型：
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作者：
K. Sugiura;Hiroki Matsutani
路径规划对于自主式移动的机器人至关重要，并且具有广泛的现实应用，包括运输、监视和救援。目前，它的高计算复杂度是在这种资源有限的机器人上应用的主要瓶颈。作为一个有前途的和有效的解决方案来解决这个问题，在本文中，我们提出了一种新的基于学习的方法，2D/3D路径规划，P3网（PointNet-based Path Planning Network），沿着，其资源有效的实现针对Xilinx ZCU 104板。我们的建议是建立在最近提出的MPNet的两个改进之上：我们使用参数高效的基于PointNet的编码器网络从点云中提取高保真障碍物特征，结合轻量级规划网络迭代规划路径。使用2D/3D数据集的实验结果表明，我们基于FPGA的P3 Net的性能明显优于MPNet，甚至可以与最先进的采样技术相媲美-例如BIT*。P3 Net能够比MPNet快6.24倍-9.34倍地规划接近最优的路径，并最终将成功率提高了24.45%，同时将参数大小减少了5.43倍-32.32倍。这使得亚秒级的实时性能在许多情况下，开辟了一个新的研究方向，基于边缘的高效路径规划。
Path planning is of crucial importance for au-tonomous mobile robots, and comes with a wide range of real-world applications including transportation, surveillance, and rescue. Currently, its high computational complexity is a major bottleneck for the application on such resource-limited robots. As a promising and effective solution to tackle this issue, in this paper, we propose a novel learning-based method for 2D/3D path planning, P3Net (PointNet-based Path Planning Network), along with its resource-efficient implementation targeting Xilinx ZCU104 boards. Our proposal is built upon two improvements to the recently proposed MPNet: we use a parameter-efficient PointNet-based encoder network to extract high-fidelity obstacle features from a point cloud, in conjunction with a lightweight planning network to iteratively plan a path. Experimental results using 2D/3D datasets demonstrate that our FPGA-based P3Net performs significantly better than MPNet and even comparable to the state-of-the-art sampling-based methods such as BIT*. P3Net is able to plan near-optimal paths 6.24x-9.34x faster than MPNet, and eventually improves the success rate by up to 24.45%, while reducing the parameter size by 5.43x-32.32x. This enables the subsecond real-time performance in many cases and opens up a new research direction for the edge-based efficient path planning.