Signals in the Soil: Subsurface Sensing

In this chapter, novel subsurface soil sensing approaches are presented for monitoring and real-time decision support system applications. The methods, materials, and operational feasibility aspects of soil sensors are explored. The soil sensing techniques covered in this chapter include aerial sensing, in-situ, proximal sensing, and remote sensing. The underlying mechanism used for sensing is also examined as well. The sensor selection and calibration techniques are described in detail. The chapter concludes with discussion of soil sensing challenges

Testing Enabling Technologies for Safe UAS Urban Operations

A set of more than 100 flight operations were conducted at NASA Langley Research Center using small UAS (sUAS) to demonstrate, test, and evaluate a set of technologies and an overarching air-ground system concept aimed at enabling safety. The research vehicle was tracked continuously during nominal traversal of planned flight paths while autonomously operating over moderately populated land. For selected flights, off-nominal risks were introduced, including vehicle-to-vehicle (V2V) encounters. Three contingency maneuvers were demonstrated that provide safe responses. These maneuvers made use of an integrated air/ground platform and two on-board autonomous capabilities. Flight data was monitored and recorded with multiple ground systems and was forwarded in real time to a UAS traffic management (UTM) server for airspace coordination and supervision

Cooperative UAV–UGV autonomous power pylon inspection: an investigation of cooperative outdoor vehicle positioning architecture

Realizing autonomous inspection, such as that of power distribution lines, through unmanned aerial vehicle (UAV) systems is a key research domain in robotics. In particular, the use of autonomous and semi-autonomous vehicles to execute the tasks of an inspection process can enhance the efficacy and safety of the operation; however, many technical problems, such as those pertaining to the precise positioning and path following of the vehicles, robust obstacle detection, and intelligent control, must be addressed. In this study, an innovative architecture involving an unmanned aircraft vehicle (UAV) and an unmanned ground vehicle (UGV) was examined for detailed inspections of power lines. In the proposed strategy, each vehicle provides its position information to the other, which ensures a safe inspection process. The results of real-world experiments indicate a satisfactory performance, thereby demonstrating the feasibility of the proposed approach.This research was funded by National Counsel of Technological and Scientific Development of Brazil (CNPq). The authors thank the National Counsel of Technological and Scientific Development of Brazil (CNPq); Coordination for the Improvement of Higher Level People (CAPES); and the Brazilian Ministry of Science, Technology, Innovation, and Communication (MCTIC). The authors would also like express their deepest gratitude to Control Robotics for sharing the Pioneer P3 robot for the experiments. Thanks to Leticia Cantieri for editing the experiment video.info:eu-repo/semantics/publishedVersio

Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones

Autonomous drones (also known as unmanned aerial vehicles) are increasingly popular for diverse applications of light-weight delivery and as substitutions of manned operations in remote locations. The computing systems for drones are becoming a new venue for research in cyber-physical systems. Autonomous drones require integrated intelligent decision systems to control and manage their flight missions in the absence of human operators. One of the most crucial aspects of drone mission control and management is related to the optimization of battery lifetime. Typical drones are powered by on-board batteries, with limited capacity. But drones are expected to carry out long missions. Thus, a fully automated management system that can optimize the operations of battery-operated autonomous drones to extend their operation time is highly desirable. This paper presents several contributions to automated management systems for battery-operated drones: (1) We conduct empirical studies to model the battery performance of drones, considering various flight scenarios. (2) We study a joint problem of flight mission planning and recharging optimization for drones with an objective to complete a tour mission for a set of sites of interest in the shortest time. This problem captures diverse applications of delivery and remote operations by drones. (3) We present algorithms for solving the problem of flight mission planning and recharging optimization. We implemented our algorithms in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. We evaluated the results of our algorithms using data from empirical studies. (4) To allow fully autonomous recharging of drones, we also develop a robotic charging system prototype that can recharge drones autonomously by our drone management system

Unmanned Aerial Vehicle (UAV)-Assisted Water Sampling

Water quality assessment programs require the collection of water samples for physical, chemical, and bacteriological analysis. Lack of personnel, accessibility of water bodies, and time constraints for water sampling, especially after natural disasters and emergencies, are some of the challenges of water sampling. To overcome these challenges, a water collection mechanism was developed and mounted on a multirotor unmanned aerial vehicle (UAV) for autonomous water sampling from water bodies. The payload capacity and endurance of the UAV (hexacopter) were verified using an indoor test station. The hexacopter was equipped with floating foam, and the electronic components were coated against water damage in case of landing on water due to emergencies or water sampling. The system was able to collect water samples 48 times out of 73 autonomous flight missions from a pond. The unsuccessful missions were mainly due to the malfunctions of the servo motor used in water sampler’s triggering mechanism. The servo motor for the mechanism was replaced to prevent the future malfunctions. UAV-assisted autonomous water sampling is a promising method for collection of water from water bodies. The system would be useful for collection of water samples from large lakes or difficult to access water sources. The details of the developed water sampling mechanism and the multirotor UAV, and experiment results are reported in this thesis

Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned