We propose a submap-based technique for mapping of underwater structures with complex geometries. Our approach relies on the use of probabilistic volumetric techniques to create submaps from multibeam sonar scans, as these offer increased outlier robustness. Special attention is paid to the problem of denoising/enhancing sonar data. Pairwise submap alignment constraints are used in a factor graph framework to correct for navigation drift and improve map accuracy. We provide experimental results obtained from the inspection of the running gear and bulbous bow of a 600-foot, Wright-class supply ship.

Underwater inspection using sonar-based volumetric submaps

This research is investigating the feasibility of using computer vision to provide a level of situational awareness suitable for the task of UAV "sense and avoid." This term is used to describe the capacity of a UAV to detect airborne traffic and respond with appropriate avoidance maneuvers in order to maintain minimum separation distances. As reflected in regulatory requirements such as FAA Order 7610.4, this capability must demonstrate a level of performance which meets or exceeds that of an equivalent human pilot.

Presented in this paper is a comparison between two initial image processing algorithms that have been designed to detect small, point-like features (potentially corresponding to distant, collision course aircraft), from image streams and a discussion of their performance in processing a real-life collision scenario. This performance is compared against the stated benchmark of equivalent human performance, specifically the measured detection times of an alerted human observer.

The two algorithms were used to process a series of image featuring real collision course aircraft against a variety of daytime backgrounds. Preliminary analysis of this data set has yielded encouraging results, achieving first detection times at distances of approximately 6.5km (3.5nmi), which are 35-40% greater than those of an alerted human observer. Comparisons were also drawn between the two separate detection algorithms, and have demonstrated that a new approach designed to increase resilience to image noise achieves a lower rate of false alarms, particularly in tests featuring more sensitive detection thresholds.

Image Processing Algorithms for UAV "Sense and Avoid"

To make a driverless car with better environment awareness, multi-layer laser radar was applied to detect roads and obstacles. Firstly the road edge data set was extracted from numerous laser radar data based on characteristics of the road edge data, and the cluster analysis of the data sets was done with the improved COBWEB algorithm based on Euclidean distance.

/pdf/road-and-obstacle-detection-based-on-multi-layer-laser-radar-1t6yihdyca.pdf

Road and Obstacle Detection Based on Multi-layer Laser Radar in Driverless Car

The performance of sprayer UAVs largely depends on accurate trajectory control while spraying. A large amount of a liquid payload may create a sloshing effect inside the liquid tank, which may occur largely during hazardous phenomena, such as wind gusts and obstacle avoidance. This all-way sloshing force inside the tank may disturb the UAV’s trajectory by, for example, a displacement from the planned path or collision with an obstacle. A large number of existing sprayer UAVs already carry various-shaped tanks. A UAV’s liquid-sloshing problem must be reduced for existing and future plant protection. Applying suitable methods can achieve these goals and provide better performance. Moreover, various tank models have different structures and capabilities, which must be fixed using a flexible solution. This article proposes a simple baffle solution for all forms of pesticide tanks and compares baffle systems’ impacts using primary shaped tanks. Indoor lab experiments showed the extreme impacts inside the tanks. Outdoor UAV mission experiments provided the practical effectiveness of the tank structures, and primary shaped tank comparison results provided guidance for future UAV pesticide-tank manufacturing. A new baffle ball design is presented for a universal solution. A one-axis linear slider was used for optical observations, an open-source flight controller was used for on-field compliance, and plenty of tests were done to prove the concept and show the efficiency. The flat hexagonal tank and baffle ball system showed better results in both indoor and outdoor experiments.

/pdf/stability-analysis-of-a-sprayer-uav-with-a-liquid-tank-with-2yj8ii5z.pdf

Stability Analysis of a Sprayer UAV with a Liquid Tank with Different Outer Shapes and Inner Structures

Target and off-target spray depositions determine the spray’s effectiveness and impact on the environment. A decisive stage in the measurement of spray deposition and drift is selecting an appropriate sampling approach under field conditions. There are various approaches available for sampling spray deposition and drift, during the evaluation of ground sprayers used for the UAV sprayer assessment, under field conditions. In this study, two sampling approaches (water-sensitive paper, and glass strip collectors) were compared to analyze spray deposition in target and off-target zones. The results showed a variation in the estimation of the spray deposits among the two applied sampling methods. The results showed that the water-sensitive paper recorded the droplet deposition in the target zone with a range from 0.049 to 4.866 µLcm−2, whereas the glass strip recorded from 0.11 to 0.793 µLcm−2. The results also showed the water sensitive paper recorded an 80.3% higher deposition than that of the glass strip at zero position during the driving flight height 2 m and flight speed 2 ms−1 (T1 treatment). It can be concluded that variation in recorded depositing is due to the sampling material. It is recommended that the confident deposition results, measurement methods and sampling approaches must be standardized for UAV sprayers according to the field conditions and controlled within artificial assessments.

/pdf/comparison-of-water-sensitive-paper-and-glass-strip-sampling-3fxqd5z2.pdf

Comparison of Water Sensitive Paper and Glass Strip Sampling Approaches to Access Spray Deposit by UAV Sprayers

Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions.

https://www.mdpi.com/2073-4395/11/6/1069/pdf

A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario

Autonomous sprayer UAVs are one of the most used aerial machines in modern agriculture. During flight missions, some common narrow obstacles appear in the flying zone. These are non-detectable from satellite images and one of the biggest challenges for autonomous sprayer UAVs in farmland. This work introduces an obstacle avoidance architecture specifically for sprayer UAVs. This architecture has generality in the spraying UAV problem, and it reduces the reliance on the global mapping of farmland. This approach computes the avoiding path based on the onboard sensor fusion system in real-time. Moreover, it autonomously determines the transition of several maneuver states using the current spraying liquid data and the UAV dynamics data obtained by offline system identification. This approach accurately tracks the avoidance path for the nonlinear time-variant spraying UAV systems. To verify the performance of the approach, we performed multiple simulations with different spraying missions, and the method demonstrated a high spraying coverage of more than 98% while successfully avoiding all vertical obstacles. We also demonstrated the adaptability of our control architecture; the safe distance between the UAV and obstacles can be changed by specifying the value of a high-level parameter on the controller. The proposed method adds value to precision agriculture, reduces mission time, and maximizes the spraying area coverage.

/pdf/a-data-driven-dynamic-obstacle-avoidance-method-for-liquid-1b952s6i.pdf

A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs

A simulation environment for assessing the forced merging strategies for autonomous vehicles (AV) is described. Such forced merging strategies need to explicitly account for vehicle interactions and require extensive simulation tests before deployment. The proposed environment facilitates such simulation tests to better assess, validate, and improve the forced merging strategies. The simulation environment consists of several control strategies for the merging vehicle, different highway vehicle models that can interact with the merging vehicle, and a set of metrics to evaluate the performance of the merging strategies. As an example of the evaluation, a Leader Follower Game Controller (LFGC) [1] is implemented and tested in this environment. Based on the analysis of the simulation tests, we propose an Enhanced LFGC (e-LFGC). It is shown that our methodology expedites the development of merging controllers so that corner cases are revealed and control requirements can be improved.

Chun-Wei Kong

Papers

A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario

A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs

Simulation Based Methodology for Assessing Forced Merging Strategies for Autonomous Vehicles