Learning-based path planning is becoming a promising robot navigation methodology due to its adaptability to various environments. However, the expensive computing and storage associated with networks impose significant challenges for their deployment on low-cost robots. Motivated by this practical challenge, we develop a lightweight neural path planning architecture with a dual input network and a hybrid sampler for resource-constrained robotic systems. Our architecture is designed with efficient task feature extraction and fusion modules to translate the given planning instance into a guidance map. The hybrid sampler is then applied to restrict the planning within the prospective regions indicated by the guide map. To enable the network training, we further construct a publicly available dataset with various successful planning instances. Numerical simulations and physical experiments demonstrate that, compared with baseline approaches, our approach has nearly an order of magnitude fewer model size and five times lower computational while achieving promising performance. Besides, our approach can also accelerate the planning convergence process with fewer planning iterations compared to sample-based methods.

翻译：基于学习的路径规划因其对不同环境的适应性正成为一种有前景的机器人导航方法。然而，神经网络相关的昂贵计算和存储成本对其在低成本机器人上的部署构成了重大挑战。受这一实际挑战的启发，我们为资源受限的机器人系统开发了一种轻量化神经路径规划架构，该架构包含一个双输入网络和一个混合采样器。我们的架构设计了高效的任务特征提取与融合模块，用于将给定的规划实例转换为引导图。随后应用混合采样器将规划限制在引导图指示的潜在区域。为了实现网络训练，我们进一步构建了一个包含多种成功规划实例的公开数据集。数值仿真与物理实验表明，与基线方法相比，我们的方法模型规模降低近一个数量级，计算量降低五倍，同时实现了有竞争力的性能。此外，与基于采样的方法相比，我们的方法还能以更少的规划迭代次数加速规划收敛过程。