P3Net: PointNet-based Path Planning on FPGA

Abstract

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.