Hybridnav: A Hybrid Approach to Zero-Shot Object Detection with Adaptive Exploration for Autonomous Reconnaissance

Abstract

Autonomous reconnaissance in unknown or contested environments demands robust perception systems capable of identifying diverse objects without prior training data. This paper presents HybridNAV, a hybrid framework that combines multiple foundation models with an adaptive navigation system for zero-shot object detection and autonomous exploration. Unlike monolithic detection models, HybridNAV’s multi-model fusion achieves balanced precision (0.60) and recall (0.58) with a macro F-score of 0.59, representing a 24% improvement over single-model baselines. The adaptive navigation system reduces scan time by 25% and path length by 30% compared to static waypoint approaches, while operating in real-time at 3.2 Hz with sub-100 msec latency on resource-constrained hardware. All processing is performed locally on the robotic platform, eliminating reliance on external communication infrastructure—a critical requirement for operations in communication-denied environments. We evaluate HybridNAV in both simulated indoor scenes and physical robot trials, demonstrating its effectiveness for intelligence gathering in unknown environments.