Forests are critical ecosystems that play a fundamental role in supporting biodiversity and maintaining climate stability. However, forest monitoring and exploration present huge challenges due to the vast scale and complex terrain. This paper proposes a novel bionic robot, PteroBot, designed to support a new paradigm for forest exploration inspired by the locomotion of Pteromyini. PteroBot is capable of regulating its gliding posture via a flexible membrane, enabling low-energy and low-disturbance mobility within forest environments. An adaptive gliding control system tailored to the robot’s structure is developed and its effectiveness is validated through aerodynamic analysis, simulation, and experimental testing. Results show that under a cascaded closed-loop attitude controller, PteroBot achieves an average glide ratio of 2.02 and demonstrates controllable turning via attitude modulation. Additionally, comparative tests with UAVs demonstrate that PteroBot offers significant advantages in energy efficiency and acoustic disturbance. Experimental outcomes confirm that PteroBot offers a biologically inspired and ecologically compatible solution for forest exploration, with strong potential in applications such as environmental monitoring, habitat assessment, and covert reconnaissance.

Growing demands for maritime security and navigational safety have made video surveillance indispensable for monitoring distant ship targets. Visible-thermal fusion provides a robust solution across varying weather and illumination conditions; however, tiny targets remain highly sensitive to positional errors, and cross-modal misalignment caused by occlusion, low illumination, and cluttered scenes further degrades detection accuracy. To address these issues, we propose CPSF-Net, a confidence-chained, position-sensitive and scale-aware fusion network for visible-thermal tiny ship detection. A Position-Sensitive Multimodal Fusion (PMF) module enhances and fuses pre-aligned visible-thermal features with possible residual local misalignment through cross-attention and derives a position-sensitive token-level matching confidence directly from the attention weights without introducing additional parameters. A Confidence-Guided Sparse Attention (CSA) module uses this confidence to adaptively suppress redundant cross-modal correlations and improve positional awareness. Finally, an adaptive Scale-Weighted Loss (SWL) balances Intersection over Union (IoU) and Normalized Wasserstein Distance (NWD) according to object scale and matching confidence, providing more stable localization supervision across object scales. On the AISail and RGBT-Tiny datasets, CPSF-Net achieves 92.1% and 84.2% ${\mathrm{mAP}}_{50}$ from unmanned surface vehicle (USV) and unmanned aerial vehicle (UAV) perspectives, respectively. These results show that the proposed method performs consistently in challenging maritime environments.