This paper presents a new design of an arm exoskeleton using shape memory alloy wires and springs embedded in fabric and taking the form of a sleeve. The device is intended to facilitate everyday life tasks by providing the user with an easily operated lift aiding device. The “SMA Aiding Sleeve” will bring extra lifting power to various classes of users (medical and non-medical). This paper presents the details of the design including the hardware and control scheme. The prototype preliminary tests provide encouraging results in terms of controllability and actuation. The results demonstrate the feasibility of position control.

The recent global pandemic has posed unprecedented challenges to public health systems and has highlighted the critical need for effective, contactless disinfection strategies in shared environments. This study investigates the use of autonomous robotics to enhance disinfection efficiency and safety in public spaces through the development of a custom-built mobile spraying platform. The proposed robotic system is equipped with an integrated 3D object pose estimation framework that fuses RGB-based object detection with point cloud segmentation to accurately identify and localize high-contact surfaces. To facilitate autonomous operation, both local and global motion planning algorithms are implemented, enabling the robot to navigate complex environments and execute disinfection tasks with minimal human intervention. Experimental results demonstrate the feasibility of the proposed disinfection robotic system.

A novel dynamic resource allocation model is introduced for efficient sharing of services provided by ad hoc assemblies of heterogeneous autonomous vehicles. A key contribution is the provision of capability to dynamically select sensors and platforms within constraints imposed by time dependencies, refueling, and transportation services. The problem is modeled as a connected network of nodes and formulated as an integer linear program. Solution fitness is prioritized over computation time. Simulation results of an illustrative scenario are used to demonstrate the ability of the model to plan for sensor selection, refueling, collaboration, and cooperation between heterogeneous resources. Prioritization of operational cost leads to missions that use cheaper resources but take longer to complete. Prioritization of completion time leads to shorter missions at the expense of increased overall resource cost. Missions can be successfully replanned through dynamic reallocation of new requests during a mission. Monte Carlo studies on systems of increasing complexity show that good solutions can be obtained using low time resolutions, with small time windows at a relatively low computational cost. In comparison with other approaches, the developed integer linear program model provides best solutions at the expense of longer computation time.

Global Navigation Satellite Systems (GNSS)-denied environments pose significant challenges for autonomous drone navigation, requiring robust visual localization systems capable of real-time performance. Existing approaches either sacrifice accuracy for speed or fail to adapt to varying flight altitudes and orientations, limiting their practical deployment. We present Real-Time Elevation and Orientation-Aware Localization Architecture (REOLA), a visual localization system that combines similarity-driven autonomous window sizing, element-wise correlation-based orientation detection, and reinforcement learning with human feedback (RLHF) enhancement for publicly available satellite imagery. On desktop hardware (i7-10700K + RTX 3070), the REOLA achieved approximately 59 FPS performance with sub-5-m accuracy across diverse flight conditions through intelligent similarity-based matching, combined with efficient MobileNet-V3 embeddings and FAISS similarity search. For embedded deployment on NVIDIA Jetson Orin Nano, the system achieved 22.5 FPS, meeting real-time requirements for autonomous drone localization. The system autonomously selects optimal window sizes corresponding to the current elevation and determines drone orientation through element-wise correlation scoring across discrete rotation angles. Enhanced through RLHF, the REOLA achieved a $97.1\%$ success rate (sub-5-m localization) while processing frames in 17 milliseconds on desktop hardware (44.4 ms on embedded hardware), providing a substantial margin over real-time requirements. The approach demonstrates particular superiority over traditional keypoint-based methods in challenging environments with repetitive patterns such as agricultural fields, rocky mountains, dense forests, and grasslands, where conventional keypoint detection struggles. We explicitly identify featureless sand dune deserts and open-sea or coastal water flights as out of scope, since the reference satellite imagery in those regimes does not contain stable landmarks.