Congestion Mitigation Path Planning for Large-Scale Multi-Agent Navigation in Dense Environments

2025-08-07

AI Summary

Local congestion severely degrades navigation efficiency in high-density multi-agent systems. Method: This paper proposes the Congestion-Mitigating Path Planning (CMPP) framework, which explicitly models local congestion as flow-dependent edge costs on paths and introduces a sparse-graph-based, flow-guided multiplicative penalty mechanism to suppress excessive agent convergence at intersections. It further designs the A-CMTS two-level search algorithm, integrating mixed-integer nonlinear programming with flow-aware vertex penalties to generate scalable, progressively optimized, coarse-grained collision-free paths across discrete and continuous spaces. Contribution/Results: When integrated with state-of-the-art obstacle avoidance methods, CMPP significantly reduces local congestion rates and improves system throughput. Experiments demonstrate its effectiveness in enhancing overall navigation efficiency for large-scale logistics scheduling and autonomous driving applications.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

In high-density environments where numerous autonomous agents move simultaneously in a distributed manner, streamlining global flows to mitigate local congestion is crucial to maintain overall navigation efficiency. This paper introduces a novel path-planning problem, congestion mitigation path planning (CMPP), which embeds congestion directly into the cost function, defined by the usage of incoming edges along agents' paths. CMPP assigns a flow-based multiplicative penalty to each vertex of a sparse graph, which grows steeply where frequently-traversed paths intersect, capturing the intuition that congestion intensifies where many agents enter the same area from different directions. Minimizing the total cost yields a set of coarse-level, time-independent routes that autonomous agents can follow while applying their own local collision avoidance. We formulate the problem and develop two solvers: (i) an exact mixed-integer nonlinear programming solver for small instances, and (ii) a scalable two-layer search algorithm, A-CMTS, which quickly finds suboptimal solutions for large-scale instances and iteratively refines them toward the optimum. Empirical studies show that augmenting state-of-the-art collision-avoidance planners with CMPP significantly reduces local congestion and enhances system throughput in both discrete- and continuous-space scenarios. These results indicate that CMPP improves the performance of multi-agent systems in real-world applications such as logistics and autonomous-vehicle operations.

Problem

• Mitigates congestion in dense multi-agent navigation environments
• Introduces congestion-aware cost function for path planning
• Develops scalable solvers for large-scale agent routing

Innovation

• Embed congestion into cost function
• Flow-based multiplicative penalty on vertices
• Two-layer search algorithm A-CMTS