Constraint-aware Path Planning from Natural Language Instructions Using Large Language Models
arXiv:2603.19257v1 Announce Type: new Abstract: Real-world path planning tasks typically involve multiple constraints beyond simple route optimization, such as the number of routes, maximum route length, depot locations, and task-specific requirements. Traditional approaches rely on dedicated formulations and algorithms for each problem variant, making them difficult to scale across diverse scenarios. In this work, we propose a flexible framework that leverages large language models (LLMs) to solve constrained path planning problems directly from natural language input. The core idea is to allow users to describe routing tasks conversationally, while enabling the LLM to interpret and solve the problem through solution verification and iterative refinement. The proposed method consists of two integrated components. For problem types that have been previously formulated and studied, the LLM first matches the input request to a known problem formulation in a library of pre-defined templa
arXiv:2603.19257v1 Announce Type: new Abstract: Real-world path planning tasks typically involve multiple constraints beyond simple route optimization, such as the number of routes, maximum route length, depot locations, and task-specific requirements. Traditional approaches rely on dedicated formulations and algorithms for each problem variant, making them difficult to scale across diverse scenarios. In this work, we propose a flexible framework that leverages large language models (LLMs) to solve constrained path planning problems directly from natural language input. The core idea is to allow users to describe routing tasks conversationally, while enabling the LLM to interpret and solve the problem through solution verification and iterative refinement. The proposed method consists of two integrated components. For problem types that have been previously formulated and studied, the LLM first matches the input request to a known problem formulation in a library of pre-defined templates. For novel or unseen problem instances, the LLM autonomously infers a problem representation from the natural language description and constructs a suitable formulation in an in-context learning manner. In both cases, an iterative solution generation and verification process guides the LLM toward producing feasible and increasingly optimal solutions. Candidate solutions are compared and refined through multiple rounds of self-correction, inspired by genetic-algorithm-style refinement. We present the design, implementation, and evaluation of this LLM-based framework, demonstrating its capability to handle a variety of constrained path planning problems. This method provides a scalable and generalizable approach for solving real-world routing tasks with minimal human intervention, while enabling flexible problem specification through natural language.
Executive Summary
This article proposes a novel framework for constraint-aware path planning using large language models (LLMs). The framework enables users to describe routing tasks conversationally, while the LLM interprets and solves the problem through solution verification and iterative refinement. The method consists of two integrated components, matching the input request to a known problem formulation or inferring a problem representation from the natural language description. The LLM-based framework demonstrates its capability to handle a variety of constrained path planning problems, providing a scalable and generalizable approach with minimal human intervention. The framework's flexibility and adaptability make it a promising solution for real-world routing tasks.
Key Points
- ▸ The proposed framework leverages LLMs to solve constrained path planning problems directly from natural language input.
- ▸ The framework consists of two integrated components: matching known problem formulations and inferring novel representations.
- ▸ The LLM-based framework demonstrates its capability to handle a variety of constrained path planning problems.
Merits
Strength
The framework's flexibility and adaptability make it a promising solution for real-world routing tasks.
Scalability
The framework's ability to handle diverse scenarios and problem instances makes it a scalable solution.
Generalizability
The framework's use of LLMs enables generalizability across different problem types and domains.
Demerits
Limitation
The framework's reliance on LLMs may lead to errors in problem interpretation or solution generation.
Complexity
The framework's iterative solution generation and verification process may be computationally intensive.
Data Quality
The quality of the input data and the accuracy of the LLM-based problem representation may impact the framework's performance.
Expert Commentary
The proposed framework is a significant contribution to the field of path planning and routing optimization. The use of LLMs and AI techniques enables the framework to handle complex and diverse scenarios, making it a promising solution for real-world routing tasks. However, the framework's reliance on LLMs and the quality of the input data may impact its performance. Furthermore, the framework's computational intensity and potential errors in problem interpretation or solution generation should be carefully considered. Overall, the framework's flexibility, scalability, and generalizability make it a valuable addition to the field of path planning and routing optimization.
Recommendations
- ✓ Future research should focus on improving the framework's performance and robustness, particularly in terms of problem interpretation and solution generation.
- ✓ The framework's applications and potential implications for policy-making and decision-making in transportation and logistics industries should be explored further.
Sources
Original: arXiv - cs.CL
