cs.ROOct 25, 2026

Extended LASMP: Language Aided Subset Sampling Based Motion Planner for Drone Navigation

Authors: Saswati Bhattacharjee, Anirban Sinha, Mukulika Ghosh, Chinwe Ekenna

Organizations: University at Albany, Department of Computer Science, NY, USA. · GE Aerospace Research, NY, USA. · Missouri State University, Department of Computer Science, Missouri, USA

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Abstract

This paper proposes a command-aligned sampling distribution that incorporates navigation commands directly into the exploration process to improve the efficiency of sampling-based motion planning for quadcopters in three-dimensional environments. Inspired by the Language-Aided Subset Sampling Motion Planner, this paper extends command-aware subset sampling to aerial systems. Subset sampling is generalized to command-aligned regions, enabling structured 3D exploration while preserving probabilistic completeness. Natural language instructions are grounded into yaw-conditioned and altitude-aware geometric constraints using a prompt-based large language model for entity extraction and instruction validation. Extensive simulations in complex environments demonstrate that the proposed method generates collision-free, consistent trajectories while significantly reducing sampling effort compared to uniform RRT-based planners.

Figures & tables

Fig. 1 : Extend LASMP: A drone navigating a multi-room environment based on natural language input while generating obstacle free paths on the fly. The red line shows the planned path while the yellow dotted lines show smoothened path executed on the robot.
Fig. 2 : (a) Architectural overview of the Extended LASMP pipeline: The Qwen [ 22 ] model process the natural language instructions to extract a sequence of directional primitives (e.g., UP , LEFT ). This turn list serves as a heuristic constraint for the Extend LASMP framework, which optimizes the search space toward a task-relevant subset of the planning domain.
Fig. 3 : An illustration of Chat Markup Language (ChatML)few-shot prompt to extract the motion entities. Messages are structured by roles such as system, user, and assistant. The system message defines the model’s behavior, the user message defines the task, and the assistant message can include an example output.
Fig. 4 : Diverse 3D point cloud environments for experimental validation. The environments vary in obstacle density, and corridor width to evaluate the robustness of command-aware subset sampling under different structural complexities.
Fig. 5 : A comparative evaluation of the planners Extended LASMP, RRT, and RRT* in terms of the number of nodes added to the search tree. The red line denotes the collision-free path extracted from the search tree. Extended LASMP needed significantly less number of nodes as compared to RRT and RRT*.
Fig. 6 : Performance comparison of Extended LASMP, RRT, RRT* and RRTConnect w.r.t number of nodes added to the search tree, the number of random-state queries, the computation time, and PathLength.
ParameterExtended LASMPRRTRRT*RRT-Connect
Step size (m)0.10.50.40.5
Maximum iterations200500040005000
Goal tolerance(m)2N/AN/AN/A
Goal biasN/A0.10.10.1
Rewiring (m)N/AN/A1N/A
TABLE III : Planning parameters used for each algorithm.
Fig. 7 : Instruction sequence Go up, then turn back, take a left followed by another left, then go down and turn back to reach your destination . The snapshots illustrate yaw-conditioned subset sampling and ray-based switching between commands, resulting in a collision-free trajectory. The green blocks indicate the quadcopter poses at various time sequences.
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