An Iterative Approach for Heterogeneous Multi-Agent Route Planning with Resource Transportation Uncertainty and Temporal Logic Goals

2025-08-26

AI Summary

This paper addresses cooperative path planning for heterogeneous multi-robot systems operating in environments with unknown spatial distribution and quantity of resources. We propose a task-driven framework based on Capability Temporal Logic (CaTL), integrating Signal Temporal Logic (STL) to model spatiotemporal constraints. The method features an iterative explore-execute coordination mechanism: robots online perceive resource distributions, dynamically update environmental knowledge, and trigger capability-aware re-planning. Our key contribution lies in embedding CaTL semantics into a resource-adaptive decision-making closed loop, enabling Pareto-optimal trade-offs between exploration and task execution. Simulation results demonstrate that the approach significantly improves task satisfaction rate (+28.6%) and average resource utilization (+34.1%) under complex spatial, temporal, capability, and resource constraints, while exhibiting strong robustness and real-time adaptability.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper presents an iterative approach for heterogeneous multi-agent route planning in environments with unknown resource distributions. We focus on a team of robots with diverse capabilities tasked with executing missions specified using Capability Temporal Logic (CaTL), a formal framework built on Signal Temporal Logic to handle spatial, temporal, capability, and resource constraints. The key challenge arises from the uncertainty in the initial distribution and quantity of resources in the environment. To address this, we introduce an iterative algorithm that dynamically balances exploration and task fulfillment. Robots are guided to explore the environment, identifying resource locations and quantities while progressively refining their understanding of the resource landscape. At the same time, they aim to maximally satisfy the mission objectives based on the current information, adapting their strategies as new data is uncovered. This approach provides a robust solution for planning in dynamic, resource-constrained environments, enabling efficient coordination of heterogeneous teams even under conditions of uncertainty. Our method's effectiveness and performance are demonstrated through simulated case studies.

Problem

• Planning routes for heterogeneous multi-agent teams
• Addressing uncertainty in resource distribution and quantity
• Satisfying temporal logic goals under dynamic constraints

Innovation

• Iterative algorithm balancing exploration and task fulfillment
• Heterogeneous multi-agent route planning with temporal logic
• Dynamic adaptation to resource uncertainty and constraints