Deep Source-Seekers with Obstacle Avoidance: Adaptive Hybrid Control with Transformers In-the-Loop

Abstract

Autonomous signal source localization is a cornerstone of modern robotics, underpinning critical applications in environmental monitoring, search and rescue, and industrial automation. Traditional source-seeking methods, such as gradient-based algorithms and potential field-based approaches, often struggle with local minimum entrapment in environments cluttered with obstacles. To address these challenges, in this paper we introduce a novel model-free approach that combines a perception-driven hybrid controller—integrating adaptive continuous-time and discrete-time feedback—with an Environmental Complexity Adapter (ECA) for perception model selection. The proposed dynamics implement real-time exploration/exploitation mechanisms and complementary deep learning-based perception architectures: YOLOv10 for rapid and accurate object detection in clear conditions, and Real-Time DEtection TRansformer (RT-DETR) for enhanced robustness in noisy environments. By continuously assessing the quality of sensor data, the ECA dynamically switches between these models, optimizing the trade-off between processing speed and detection reliability. This approach harnesses the robustness of hybrid controllers while enabling efficient, perception-guided source-seeking and obstacle avoidance in complex environments. Extensive numerical simulations validate the effectiveness of the proposed approach.