用自然语言指导机器人规划路径，效率提升显著
LASMP: Language Aided Subset Sampling Based Motion Planner
- 基于改进的RRT算法，通过语言模型理解用户指令
- 节点数减少55%，随机采样查询量降低80%
- 适合需要自然语言交互的智能导航场景
本文提出语言辅助子集采样运动规划器（LASMP），一种通过自然语言指令帮助移动机器人规划路径的系统。LASMP采用改进的快速探索随机树（RRT）方法，利用语言模型（RoBERTa）处理用户指令，引导规划聚焦于工作空间特定区域，从而提升效率。与传统RRT相比，LASMP在保持安全、无碰撞路径生成的前提下，节点数量减少55%，随机采样查询量下降80%。在仿真和真实环境测试中，系统均展现出对复杂室内场景的更好适应能力。结果表明，结合语言理解与运动规划可显著提升机器人导航效率。
原文摘要 · Abstract (English)
This paper presents the Language Aided Subset Sampling Based Motion Planner (LASMP), a system that helps mobile robots plan their movements by using natural language instructions. LASMP uses a modified version of the Rapidly Exploring Random Tree (RRT) method, which is guided by user-provided commands processed through a language model (RoBERTa). The system improves efficiency by focusing on specific areas of the robot's workspace based on these instructions, making it faster and less resource-intensive. Compared to traditional RRT methods, LASMP reduces the number of nodes needed by 55% and cuts random sample queries by 80%, while still generating safe, collision-free paths. Tested in both simulated and real-world environments, LASMP has shown better performance in handling complex indoor scenarios. The results highlight the potential of combining language processing with motion planning to make robot navigation more efficient.
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