Joint optimization of detection, task allocation, and path planning for multi-agent cooperative interception under partial observability
- 1Naval University of Engineering, Wuhan, Hubei, P. R. China
Abstract
Safety-critical autonomous systems often require multiple agents to detect, assign, and intercept non-cooperative mobile targets using only partial observations. These subtasks are tightly coupled because the same agents must balance spatial exploration for detection with goal-directed motion for interception, while target maneuvers remain uncertain. This study develops and validates an integrated multi-agent coordination framework for jointly optimizing distributed detection, dynamic task allocation, and path planning under partial observability. The problem is formulated as a coupled Dec-POMDP made computationally tractable through factored decentralized policies, parameter sharing, and centralized training with decentralized execution, and a Weighted Coordinated Multi-Agent Proximal Policy Optimization (W-MAPPO) framework is introduced to adaptively coordinate competing subtasks. W-MAPPO combines an observation-conditioned Policy Ratio Predictor, an LSTM-based history-aware processor for temporal modeling of target maneuvers, and a Voronoi-based collaborative detection module for spatial coverage coordination within a centralized-training decentralized-execution paradigm. Comprehensive simulation experiments across increasing target densities compare W-MAPPO with seven representative MARL baselines and two classical methods, supported by statistical testing, ablation studies, sensitivity analysis, and representative case studies. W-MAPPO achieves a 94% success rate in the 5 vs 5 scenario and maintains stable interception performance as the number of targets increases to 10 and 15. These results demonstrate the technical reliability and practical potential of integrated subtask coordination for partially observable multi-agent systems.
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BibTeX
@article{huang2026joint,
title = {{Joint optimization of detection, task allocation, and path planning for multi-agent cooperative interception under partial observability}},
author = {Qinlong Huang and Zhikun Liu and Yasong Luo and Zhong Liu and Qingtao Xia and Miao Zhao},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359071},
url = {https://doi.org/10.1371/journal.pone.0359071},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Joint optimization of detection, task allocation, and path planning for multi-agent cooperative interception under partial observability
AU - Qinlong Huang
AU - Zhikun Liu
AU - Yasong Luo
AU - Zhong Liu
AU - Qingtao Xia
AU - Miao Zhao
PY - 2026
DA - 2026/10/13
JO - PLOS One
DO - 10.1371/journal.pone.0359071
UR - https://doi.org/10.1371/journal.pone.0359071
AB - Safety-critical autonomous systems often require multiple agents to detect, assign, and intercept non-cooperative mobile targets using only partial observations. These subtasks are tightly coupled because the same agents must balance spatial exploration for detection with goal-directed motion for interception, while target maneuvers remain uncertain. This study develops and validates an integrated multi-agent coordination framework for jointly optimizing distributed detection, dynamic task allocation, and path planning under partial observability. The problem is formulated as a coupled Dec-POMDP made computationally tractable through factored decentralized policies, parameter sharing, and centralized training with decentralized execution, and a Weighted Coordinated Multi-Agent Proximal Policy Optimization (W-MAPPO) framework is introduced to adaptively coordinate competing subtasks. W-MAPPO combines an observation-conditioned Policy Ratio Predictor, an LSTM-based history-aware processor for temporal modeling of target maneuvers, and a Voronoi-based collaborative detection module for spatial coverage coordination within a centralized-training decentralized-execution paradigm. Comprehensive simulation experiments across increasing target densities compare W-MAPPO with seven representative MARL baselines and two classical methods, supported by statistical testing, ablation studies, sensitivity analysis, and representative case studies. W-MAPPO achieves a 94% success rate in the 5 vs 5 scenario and maintains stable interception performance as the number of targets increases to 10 and 15. These results demonstrate the technical reliability and practical potential of integrated subtask coordination for partially observable multi-agent systems.
SN - 1932-6203
ER -