Academic

Multi-Agent Pathfinding with Non-Unit Integer Edge Costs via Enhanced Conflict-Based Search and Graph Discretization

arXiv:2604.05416v1 Announce Type: new Abstract: Multi-Agent Pathfinding (MAPF) plays a critical role in various domains. Traditional MAPF methods typically assume unit edge costs and single-timestep actions, which limit their applicability to real-world scenarios. MAPFR extends MAPF to handle non-unit costs with real-valued edge costs and continuous-time actions, but its geometric collision model leads to an unbounded state space that compromises solver efficiency. In this paper, we propose MAPFZ, a novel MAPF variant on graphs with non-unit integer costs that preserves a finite state space while offering improved realism over classical MAPF. To solve MAPFZ efficiently, we develop CBS-NIC, an enhanced Conflict-Based Search framework incorporating time-interval-based conflict detection and an improved Safe Interval Path Planning (SIPP) algorithm. Additionally, we propose Bayesian Optimization for Graph Design (BOGD), a discretization method for non-unit edge costs that balances efficie

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Hongkai Fan, Qinjing Xie, Bo Ouyang, Yaonan Wang, Zhi Yan, Jiawen He, Zheng Fang · · 1 min read · 2 views
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arXiv:2604.05416v1 Announce Type: new Abstract: Multi-Agent Pathfinding (MAPF) plays a critical role in various domains. Traditional MAPF methods typically assume unit edge costs and single-timestep actions, which limit their applicability to real-world scenarios. MAPFR extends MAPF to handle non-unit costs with real-valued edge costs and continuous-time actions, but its geometric collision model leads to an unbounded state space that compromises solver efficiency. In this paper, we propose MAPFZ, a novel MAPF variant on graphs with non-unit integer costs that preserves a finite state space while offering improved realism over classical MAPF. To solve MAPFZ efficiently, we develop CBS-NIC, an enhanced Conflict-Based Search framework incorporating time-interval-based conflict detection and an improved Safe Interval Path Planning (SIPP) algorithm. Additionally, we propose Bayesian Optimization for Graph Design (BOGD), a discretization method for non-unit edge costs that balances efficiency and accuracy with a sub-linear regret bound. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in runtime and success rate across diverse benchmark scenarios.

Executive Summary

This paper introduces MAPFZ, a novel variant of Multi-Agent Pathfinding (MAPF) addressing a critical gap in traditional methods by accommodating non-unit integer edge costs and continuous-time actions. The authors propose CBS-NIC, an enhanced Conflict-Based Search framework incorporating time-interval-based conflict detection and an improved Safe Interval Path Planning (SIPP) algorithm, alongside BOGD, a Bayesian Optimization-based discretization method for non-unit edge costs with sub-linear regret bounds. Through extensive experiments, the approach demonstrates superior performance in runtime and success rates compared to state-of-the-art methods, offering a more realistic and scalable solution for real-world applications such as robotics, logistics, and autonomous systems. This work bridges the gap between theoretical MAPF models and practical deployment constraints.

Key Points

  • ▸ Introduces MAPFZ, a MAPF variant that supports non-unit integer edge costs and continuous-time actions, addressing limitations of traditional unit-cost models.
  • ▸ Proposes CBS-NIC, an enhanced Conflict-Based Search framework with time-interval-based conflict detection and an improved SIPP algorithm for efficient pathfinding.
  • ▸ Develops BOGD, a Bayesian Optimization-based discretization method for non-unit edge costs, providing a sub-linear regret bound and balancing efficiency with accuracy.
  • ▸ Demonstrates through extensive experiments that MAPFZ outperforms state-of-the-art methods in runtime and success rate across diverse benchmark scenarios.

Merits

Innovative Problem Formulation

The paper addresses a long-standing limitation in MAPF research by extending the model to non-unit integer edge costs and continuous-time actions, which closely aligns with real-world deployment scenarios in robotics and logistics.

Computational Efficiency

The introduction of CBS-NIC and BOGD significantly improves solver efficiency and scalability, as evidenced by the experimental results demonstrating superior runtime and success rates over existing methods.

Theoretical Rigor

The development of BOGD with a sub-linear regret bound provides a strong theoretical foundation, ensuring both efficiency and accuracy in graph discretization, which is crucial for practical applications.

Real-World Applicability

The proposed MAPFZ framework offers a more realistic model for real-world scenarios, making it highly relevant for industries such as autonomous vehicles, warehouse automation, and air traffic control.

Demerits

Assumption of Integer Edge Costs

While the paper addresses non-unit edge costs, it restricts these costs to integers, which may limit applicability in scenarios requiring real-valued costs (e.g., dynamic environments with partial observability).

Dependency on Graph Discretization

The reliance on BOGD for discretization introduces a potential source of error, as the performance of the algorithm may degrade if the discretization process does not accurately capture the underlying continuous-time dynamics.

Limited Generalization of Experiments

The experimental validation, while extensive, may not fully capture the diversity of real-world scenarios, particularly those involving highly dynamic or adversarial environments, which could affect the robustness of the proposed methods.

Complexity of Implementation

The integration of CBS-NIC and BOGD may require significant computational resources and expertise to implement, potentially limiting adoption in resource-constrained or less technically advanced settings.

Expert Commentary

This paper represents a significant advancement in the field of Multi-Agent Pathfinding by addressing a critical gap in the literature: the inability of classical MAPF models to handle non-unit integer edge costs and continuous-time actions. The introduction of MAPFZ, coupled with CBS-NIC and BOGD, demonstrates a sophisticated blend of algorithmic innovation and theoretical rigor. The enhancement of Conflict-Based Search with time-interval-based conflict detection and improved SIPP is particularly noteworthy, as it directly addresses the scalability challenges inherent in multi-agent systems operating in complex environments. The theoretical contributions, such as the sub-linear regret bound for BOGD, further solidify the work's academic merit. However, the restriction to integer edge costs, while a reasonable simplification, may limit applicability in highly dynamic or partially observable environments. Future research could explore extensions to real-valued costs and adaptive discretization techniques to further enhance realism and robustness. The practical implications of this work are profound, particularly for industries reliant on multi-agent coordination, such as logistics and autonomous transportation. This paper sets a new benchmark for MAPF research and is poised to influence both academic inquiry and industrial applications.

Recommendations

  • ✓ Extend the MAPFZ framework to accommodate real-valued edge costs and continuous-time dynamics, thereby enhancing its applicability to a broader range of real-world scenarios.
  • ✓ Investigate adaptive discretization techniques that dynamically adjust based on environmental feedback, improving the accuracy and efficiency of graph discretization in dynamic environments.
  • ✓ Develop standardized benchmark suites specifically tailored to MAPF variants with non-unit costs, enabling more comprehensive and comparable evaluations of future algorithms.
  • ✓ Explore hybrid approaches that combine CBS-NIC with machine learning techniques to further optimize conflict resolution and path planning in highly dynamic environments.
  • ✓ Collaborate with industry partners to deploy MAPFZ in real-world applications, such as warehouse automation or autonomous vehicle coordination, to validate its scalability and robustness under operational constraints.

Sources

Original: arXiv - cs.AI

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