Human-Autonomy Teaming in Robot Navigation Using Bioinspired Approach for Search and Rescue

Abstract

Search and rescue operations save thousands of lives yearly, and robotics can further improve survival rates in hazardous, partially observable environments. This article introduces a trustworthy human-autonomy teaming (HAT) navigation framework that fuses human situational awareness with algorithmic planning across both global and local layers. At the global level, a hybrid graph integrates generalized Voronoi diagrams (GVDs), for high-clearance, topology-preserving roadmaps interpretable to operators, with a spatiotemporal graph that dynamically updates edges and node salience as obstacles and mission priorities evolve. Human input is incorporated through a node optimization protocol that prioritizes access nodes, adjusts waypoint sequences, and invokes RRT*-informative path planning when jump-search shortcuts become unreliable, thereby aligning exploration with operator-defined areas of interest and mission constraints. At the local level, a bioinspired neural network (BNN) with an adaptive window strategy provides reactive obstacle avoidance and target tracking, ensuring smooth and safe motion in cluttered, dynamic scenes. Simulation results across indoor and urban-like environments demonstrate that the proposed framework enhances path efficiency, robustness to environmental change, and responsiveness while reducing unnecessary re-planning. By unifying GVD-based global structure, human-guided decision-making, and BNN-based local reactivity, the HAT framework advances autonomous navigation toward greater safety, adaptability, and effectiveness in real-world search and rescue operations.