Cooperative Guidance for Aerial Defense in Multiagent Systems

2025-10-02 · Shivam Bajpai, Abhinav Sinha, Shashi Ranjan Kumar arxiv

This paper addresses a critical aerial defense challenge in contested airspace, involving three autonomous aerial vehicles -- a hostile drone (the pursuer), a high-value drone (the evader), and a protective drone (the defender). We present a cooperative guidance framework for the evader-defender team that guarantees interception of the pursuer before it can capture the evader, even under highly dynamic and uncertain engagement conditions. Unlike traditional heuristic, optimal control, or differential game-based methods, we approach the problem within a time-constrained guidance framework, leveraging true proportional navigation based approach that ensures robust and guaranteed solutions to the aerial defense problem. The proposed strategy is computationally lightweight, scalable to a large number of agent configurations, and does not require knowledge of the pursuer's strategy or control laws. From arbitrary initial geometries, our method guarantees that key engagement errors are driven to zero within a fixed time, leading to a successful mission. Extensive simulations across diverse and adversarial scenarios confirm the effectiveness of the proposed strategy and its relevance for real-time autonomous defense in contested airspace environments.

📄 PDF Abstract BibTeX arXiv:2510.02087

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Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction

2018-09-25 · Hongyao Tang, Jianye Hao, Tangjie Lv, Yingfeng Chen 외

Multiagent reinforcement learning (MARL) is commonly considered to suffer from non-stationary environments and exponentially increasing policy space. It would be even more challenging when rewards are sparse and delayed …

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version

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We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the…

Distributed Optimization Model Predictive Control

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Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and the static social learning framework. However, two aspects of dynamics in real…

Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments

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Recently, multiagent deep reinforcement learning (DRL) has received increasingly wide attention. Existing multiagent DRL algorithms are inefficient when facing with the non-stationarity due to agents update their policie…

Deep Reinforcement Learning Q-Learning reinforcement-learning Reinforcement Learning+1

Coordinating Multiagent Industrial Symbiosis

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We present a formal multiagent framework for coordinating a class of collaborative industrial practices called Industrial Symbiotic Networks (ISNs) as cooperative games. The game-theoretic formulation of ISNs enables sys…