<p>Task allocation is a critical determinant of mission success in satellite Orbital Pursuit-Evasion Games (OPEG). However, strict orbital constraints and communication latencies pose significant challenges to real-time decision-making. Existing methods often lack robustness or suffer from slow convergence. To address these issues, a Distributed Dynamic Task Allocation Algorithm (DDTAA) is proposed. First, a comprehensive game-theoretic optimization model is constructed to capture tactical advantages and long-term sustainability. Subsequently, a novel asynchronous communication architecture utilizing a local serial auction mechanism is developed. This approach enables satellites to reorganize tasks via localized information exchange, significantly reducing communication overhead. Simulations demonstrate that DDTAA reduces task reassignment cost by approximately 8.0% and achieves faster convergence in communication-constrained environments, offering a scalable solution for on-orbit autonomous coordination.</p>

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A distributed dynamic task allocation for satellite orbital pursuit-evasion game via local asynchronous communication

  • Hanyu Qian,
  • Jingwen Xu,
  • Bing Xiao,
  • Huijun Li

摘要

Task allocation is a critical determinant of mission success in satellite Orbital Pursuit-Evasion Games (OPEG). However, strict orbital constraints and communication latencies pose significant challenges to real-time decision-making. Existing methods often lack robustness or suffer from slow convergence. To address these issues, a Distributed Dynamic Task Allocation Algorithm (DDTAA) is proposed. First, a comprehensive game-theoretic optimization model is constructed to capture tactical advantages and long-term sustainability. Subsequently, a novel asynchronous communication architecture utilizing a local serial auction mechanism is developed. This approach enables satellites to reorganize tasks via localized information exchange, significantly reducing communication overhead. Simulations demonstrate that DDTAA reduces task reassignment cost by approximately 8.0% and achieves faster convergence in communication-constrained environments, offering a scalable solution for on-orbit autonomous coordination.