Design and Structural Validation of a Micro-UAV with On-Board Dynamic Route Planning

Micro aerial vehicles are becoming increasingly important in search and rescue operations due to their agility, speed, and ability to access confined spaces or hazardous areas. However, designing lightweight aerial systems presents significant structural, aerodynamic, and computational challenges. This work addresses two key limitations in many low-cost aerial systems under two kilograms: their lack of structural durability during flight through rough terrains and inability to replan paths dynamically when new victims or obstacles are detected. We present a fully customised drone built from scratch using only commonly available components and materials, emphasising modularity, low cost, and ease of assembly. The structural frame is reinforced with lightweight yet durable materials to withstand impact, while the onboard control system is powered entirely by free, open-source software solutions. The proposed system demonstrates real-time perception and adaptive navigation capabilities without relying on expensive hardware accelerators, offering an affordable and practical solution for real-world search and rescue missions.

AURASeg: Attention Guided Upsampling with Residual Boundary-Assistive Refinement for Drivable-Area Segmentation

Free space ground segmentation is essential to navigate robots and autonomous vehicles, recognize drivable zones, and traverse efficiently. Fine-grained features remain challenging for existing segmentation models, particularly for robots in indoor and structured environments. These difficulties arise from ineffective multi-scale processing, suboptimal boundary refinement, and limited feature representation. In order to overcome these limitations, we propose Attention-Guided Upsampling with Residual Boundary-Assistive Refinement (AURASeg), a ground-plane semantic segmentation model that maintains high segmentation accuracy while improving border precision. Our method uses CSP-Darknet backbone by adding a Residual Border Refinement Module (RBRM) for accurate edge delineation and an Attention Progressive Upsampling Decoder (APUD) for strong feature integration. We also incorporate a lightweight Atrous Spatial Pyramid Pooling (ASPP-Lite) module to ensure multi-scale context extraction without compromising real-time performance. The proposed model beats benchmark segmentation architectures in mIoU and F1 metrics when tested on the Ground Mobile Robot Perception (GMRP) Dataset and a custom Gazebo indoor dataset. Our approach achieves an improvement in mean Intersection-over-Union (mIoU) of +1.26% and segmentation precision of +1.65% compared to state-of-the-art models. These results show that our technique is feasible for autonomous perception in both indoor and outdoor environments, enabling precise border refinement with minimal effect on inference speed.