Guidance of quadrotor unmanned aerial vehicles via adaptive multiple-surface sliding mode control

In many application domains, navigation of unmanned aerial vehicles (UAVs) requires a planar flight to move along a desired path or to track a moving object under uncertain conditions. In this paper, we propose a robust control approach for quadrotor UAVs performing a nonholonomic-like navigation with a predefined velocity based guidance law. Specifically, the quadrotor model is first recast in the framework of nonholonomic systems, and then an adaptive multiple-surface sliding mode approach, with suboptimal second order sliding mode control, is applied. The robustness features of the proposed approach are discussed and assessed in simulation

Pathfinder autonomous rendezvous and docking project

Capabilities are being developed and demonstrated to support manned and unmanned vehicle operations in lunar and planetary orbits. In this initial phase, primary emphasis is placed on definition of the system requirements for candidate Pathfinder mission applications and correlation of these system-level requirements with specific requirements. The FY-89 activities detailed are best characterized as foundation building. The majority of the efforts were dedicated to assessing the current state of the art, identifying desired elaborations and expansions to this level of development and charting a course that will realize the desired objectives in the future. Efforts are detailed across all work packages in developing those requirements and tools needed to test, refine, and validate basic autonomous rendezvous and docking elements

Transfer Learning-Based Crack Detection by Autonomous UAVs

Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping in building inspection. Yet, the number of studies is limited considering the post processing of the data and its integration with autonomous UAVs. These will enable huge steps onward into full automation of building inspection. In this regard, this work presents a decision making tool for revisiting tasks in visual building inspection by autonomous UAVs. The tool is an implementation of fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack detection. It offers an optional mechanism for task planning of revisiting pinpoint locations during inspection. It is integrated to a quadrotor UAV system that can autonomously navigate in GPS-denied environments. The UAV is equipped with onboard sensors and computers for autonomous localization, mapping and motion planning. The integrated system is tested through simulations and real-world experiments. The results show that the system achieves crack detection and autonomous navigation in GPS-denied environments for building inspection

Vehicle Motion Planning Using Stream Functions

Borrowing a concept from hydrodynamic analysis, this paper presents stream functions which satisfy Laplace's equation as a local-minima free method for producing potential-field based navigation functions in two dimensions. These functions generate smoother paths (i.e. more suited to aircraft-like vehicles) than previous methods. A method is developed for constructing analytic stream functions to produce arbitrary vehicle behaviors while avoiding obstacles, and an exact solution for the case of a single uniformly moving obstacle is presented. The effects of introducing multiple obstacles are discussed and current work in this direction is detailed. Experimental results generated on the Cornell RoboFlag testbed are presented and discussed, as well as related work applying these methods to path planning for unmanned air vehicles

Study of the influence of size of a manned lifting body entry vehicle on research potential and cost. Part I - Summary Final report

Optimization of manned lifting body entry vehicle used for conducting space research task