生物启发的群体动态目标跟踪与避障
Biologically Inspired Swarm Dynamic Target Tracking and Obstacle Avoidance
AI总结：
本研究提出了一种AI驱动的飞行计算机，集成了在线免重训练预测模型、群体控制和避障策略，并引入了新型鲁棒自适应双向模糊大脑情感学习预测（BFBEL-P）方法，以解决传统预测方法在无人机群动态目标跟踪中收敛慢、鲁棒性低的问题，实现了高精度和快速收敛。
摘要：
本研究提出了一种新型人工智能（AI）驱动的飞行计算机，集成了在线免重训练预测模型、群体控制和避障策略，利用分布式无人机群进行军事应用中的动态目标跟踪。为实现动态目标跟踪，群体需要具备轨迹预测能力以实现拦截，从而允许对快速机动和运动进行跟踪，同时保持高效的路径规划。传统的预测方法如曲线拟合或长短期记忆网络（LSTM）鲁棒性较低，由于单智能体轨迹预测收敛缓慢，在短期内难以进行动态目标跟踪，且通常需要大量的离线训练或调优才能有效。因此，本文引入了一种新型鲁棒自适应双向模糊大脑情感学习预测（BFBEL-P）方法来解决这些挑战。该控制器集成了一个模糊接口、一个实现快速适应的神经网络、预测能力以及多智能体求解能力，使得能够聚合多种解以在短期和长期内实现快速收敛时间和高精度。这通过数值模拟得到了验证，模拟中观察到复杂轨迹被无人机群预测和跟踪。这些模拟显示，与最先进的方法相比，该方法在短期内具有更好的适应性和准确性，并在长时间域内表现出强劲的结果，从而实现了精确的群体目标跟踪和预测能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
