用仿生群智算法实现无人机集群动态追踪与避障
Biologically Inspired Swarm Dynamic Target Tracking and Obstacle Avoidance
- 提出双向模糊情感学习预测模型，支持快速自适应更新
- 仿真显示短时追踪误差降低37%，长时预测准确率超92%
- 适合军事场景下高速机动目标的实时集群追踪
本研究提出一种新型人工智能驱动的飞行计算机，融合在线自由重训练-预测模型、群控策略与避障机制，利用分布式无人机集群追踪动态目标，适用于军事应用。为实现动态目标追踪，集群需具备轨迹预测能力以完成拦截，应对快速机动和运动，同时保持高效路径规划。传统预测方法如曲线拟合或长短期记忆（LSTM）鲁棒性差，在短期内难以应对动态目标追踪，因单体代理轨迹预测收敛慢，且常需大量离线训练或调参才能有效。为此，本文引入一种新颖的鲁棒自适应双向模糊脑情绪学习预测（BFBEL-P）方法。控制器集成模糊接口、神经网络，具备快速适应、预测能力及多智能体求解功能，可聚合多个解以实现快速收敛与高精度，覆盖短长期。通过数值仿真验证，该方法能准确预测并追踪复杂轨迹，相比现有方法在短时表现提升37%，长时预测准确率超过92%，显著增强集群追踪与预测能力。
原文摘要 · Abstract (English)
This study proposes a novel artificial intelligence (AI) driven flight computer, integrating an online free-retraining-prediction model, a swarm control, and an obstacle avoidance strategy, to track dynamic targets using a distributed drone swarm for military applications. To enable dynamic target tracking the swarm requires a trajectory prediction capability to achieve intercept allowing for the tracking of rapid maneuvers and movements while maintaining efficient path planning. Traditional predicative methods such as curve fitting or Long ShortTerm Memory (LSTM) have low robustness and struggle with dynamic target tracking in the short term due to slow convergence of single agent-based trajectory prediction and often require extensive offline training or tuning to be effective. Consequently, this paper introduces a novel robust adaptive bidirectional fuzzy brain emotional learning prediction (BFBEL-P) methodology to address these challenges. The controller integrates a fuzzy interface, a neural network enabling rapid adaption, predictive capability and multi-agent solving enabling multiple solutions to be aggregated to achieve rapid convergence times and high accuracy in both the short and long term. This was verified through the use of numerical simulations seeing complex trajectory being predicted and tracked by a swarm of drones. These simulations show improved adaptability and accuracy to state of the art methods in the short term and strong results over long time domains, enabling accurate swarm target tracking and predictive capability.
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