TY - GEN
T1 - LASMP
T2 - 21st IEEE International Conference on Automation Science and Engineering, CASE 2025
AU - Bhattacharjee, Saswati
AU - Sinha, Anirban
AU - Ghosh, Mukulika
AU - Ekenna, Chinwe
N1 - Publisher Copyright: © 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105018297329
U2 - 10.1109/CASE58245.2025.11164114
DO - 10.1109/CASE58245.2025.11164114
M3 - Conference contribution
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1632
EP - 1639
BT - 2025 IEEE 21st International Conference on Automation Science and Engineering, CASE 2025
PB - IEEE Computer Society
Y2 - 17 August 2025 through 21 August 2025
ER -