Natural disasters and urban accidents drive the demand for rescue robots to provide safer, faster, and more efficient rescue trajectories. In this paper, a feature learning-based bio-inspired neural network (FLBBINN) is proposed to quickly generate a heuristic rescue path in complex and dynamic environments, as traditional approaches usually cannot provide a satisfactory solution to real-time responses to sudden environmental changes. The neurodynamic model is incorporated into the feature learning method that can use environmental information to improve path planning strategies. Task assignment and collision-free rescue trajectory are generated through robot poses and the dynamic landscape of neural activity. A dual-channel scale filter, a neural activity channel, and a secondary distance fusion are employed to extract and filter feature neurons. After completion of the feature learning process, a neurodynamics-based feature matrix is established to quickly generate the new heuristic rescue paths with parameter-driven topological adaptability. The proposed FLBBINN aims to reduce the computational complexity of the neural network-based approach and enable the feature learning method to achieve real-time responses to environmental changes. Several simulations and experiments have been conducted to evaluate the performance of the proposed FLBBINN. The results show that the proposed FLBBINN would significantly improve the speed, efficiency, and optimality for rescue operations.

翻译：自然灾害与城市事故推动了救援机器人的需求，以提供更安全、快速、高效的救援轨迹。本文提出一种基于特征学习的仿生神经网络（FLBBINN），用于在复杂动态环境中快速生成启发式救援路径——传统方法通常无法对突发环境变化提供令人满意的实时响应方案。将神经动力学模型融入特征学习方法中，可利用环境信息改进路径规划策略。通过机器人姿态与神经活动的动态景观生成任务分配与无碰撞救援轨迹。采用双通道尺度滤波器、神经活动通道及二次距离融合技术提取并筛选特征神经元。特征学习过程完成后，建立基于神经动力学的特征矩阵，通过参数驱动的拓扑适应性快速生成新的启发式救援路径。所提出的FLBBINN旨在降低基于神经网络方法的计算复杂度，使特征学习方法能够实现环境变化的实时响应。通过多项仿真与实验评估了FLBBINN的性能，结果表明该网络能显著提升救援操作的速度、效率与最优性。