Year: 2025
Type: conference-paper
Abstract: This paper presents the Language Aided Subset Sampling Based Motion Planner (LASMP), a framework that helps mobile robots plan their movements from natural language instructions. LASMP uses a modified version of the Rapidly Exploring Random Tree (RRT) method, which is guided by user-provided instructions processed through a language model. The planner 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 and 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.
Authors: Saswati Bhattacharjee, Anirban Sinha, Mukulika Ghosh, Chinwe Ekenna
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414432279