  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.
