UAV-VLPA*: A Vision-Language-Path-Action System for Optimal Route Generation on a Large Scales

The UAV-VLPA* (Visual-Language-Planning-and-Action) system represents a cutting-edge advancement in aerial robotics, designed to enhance communication and operational efficiency for unmanned aerial vehicles (UAVs). By integrating advanced planning capabilities, the system addresses the Traveling Salesman Problem (TSP) to optimize flight paths, reducing the total trajectory length by 18.5\% compared to traditional methods. Additionally, the incorporation of the A* algorithm enables robust obstacle avoidance, ensuring safe and efficient navigation in complex environments. The system leverages satellite imagery processing combined with the Visual Language Model (VLM) and GPT's natural language processing capabilities, allowing users to generate detailed flight plans through simple text commands. This seamless fusion of visual and linguistic analysis empowers precise decision-making and mission planning, making UAV-VLPA* a transformative tool for modern aerial operations. With its unmatched operational efficiency, navigational safety, and user-friendly functionality, UAV-VLPA* sets a new standard in autonomous aerial robotics, paving the way for future innovations in the field.

UAV-VLRR: Vision-Language Informed NMPC for Rapid Response in UAV Search and Rescue

Emergency search and rescue (SAR) operations often require rapid and precise target identification in complex environments where traditional manual drone control is inefficient. In order to address these scenarios, a rapid SAR system, UAV-VLRR (Vision-Language-Rapid-Response), is developed in this research. This system consists of two aspects: 1) A multimodal system which harnesses the power of Visual Language Model (VLM) and the natural language processing capabilities of ChatGPT-4o (LLM) for scene interpretation. 2) A non-linearmodel predictive control (NMPC) with built-in obstacle avoidance for rapid response by a drone to fly according to the output of the multimodal system. This work aims at improving response times in emergency SAR operations by providing a more intuitive and natural approach to the operator to plan the SAR mission while allowing the drone to carry out that mission in a rapid and safe manner. When tested, our approach was faster on an average by 33.75% when compared with an off-the-shelf autopilot and 54.6% when compared with a human pilot. Video of UAV-VLRR: https://youtu.be/KJqQGKKt1xY

UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation

The UAV-VLA (Visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the Visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple text requests. This system leverages the rich contextual information provided by satellite images, allowing for enhanced decision-making and mission planning. The combination of visual analysis by VLM and natural language processing by GPT can provide the user with the path-and-action set, making aerial operations more efficient and accessible. The newly developed method showed the difference in the length of the created trajectory in 22% and the mean error in finding the objects of interest on a map in 34.22 m by Euclidean distance in the K-Nearest Neighbors (KNN) approach.

FlightAR: AR Flight Assistance Interface with Multiple Video Streams and Object Detection Aimed at Immersive Drone Control

The swift advancement of unmanned aerial vehicle (UAV) technologies necessitates new standards for developing human-drone interaction (HDI) interfaces. Most interfaces for HDI, especially first-person view (FPV) goggles, limit the operator's ability to obtain information from the environment. This paper presents a novel interface, FlightAR, that integrates augmented reality (AR) overlays of UAV first-person view (FPV) and bottom camera feeds with head-mounted display (HMD) to enhance the pilot's situational awareness. Using FlightAR, the system provides pilots not only with a video stream from several UAV cameras simultaneously, but also the ability to observe their surroundings in real time. User evaluation with NASA-TLX and UEQ surveys showed low physical demand ($\mu=1.8$, $SD = 0.8$) and good performance ($\mu=3.4$, $SD = 0.8$), proving better user assessments in comparison with baseline FPV goggles. Participants also rated the system highly for stimulation ($\mu=2.35$, $SD = 0.9$), novelty ($\mu=2.1$, $SD = 0.9$) and attractiveness ($\mu=1.97$, $SD = 1$), indicating positive user experiences. These results demonstrate the potential of the system to improve UAV piloting experience through enhanced situational awareness and intuitive control. The code is available here: https://github.com/Sautenich/FlightAR

MorphoMove: Bi-Modal Path Planner with MPC-based Path Follower for Multi-Limb Morphogenetic UAV

This paper discusses developments for a multi-limb morphogenetic UAV, MorphoGear, that is capable of both aerial flight and ground locomotion. A hybrid path planning algorithm based on A* strategy has been developed enabling seamless transition between air-to-ground navigation modes, thereby enhancing robot's mobility in complex environments. Moreover, precise path following is achieved during ground locomotion with a Model Predictive Control (MPC) architecture for its novel walking behaviour. Experimental validation was conducted in the Unity simulation environment utilizing Python scripts to compute control values. The algorithms' performance is validated by the Root Mean Squared Error (RMSE) of 0.91 cm and a maximum error of 1.85 cm, as demonstrated by the results. These developments highlight the adaptability of MorphoGear in navigation through cluttered environments, establishing it as a usable tool in autonomous exploration, both aerial and ground-based.