Development of an Aerial Manipulation System Using Onboard Cameras and a Multi-Fingered Robotic Hand with Proximity Sensors

An aerial manipulator is a new kind of flying robot system composed of a rotorcraft unmanned aerial vehicle (UAV) and a multi-link robotic arm. It gives the flying robot the capacity to complete manipulation tasks. Steady flight is essential for an aerial manipulator to complete manipulation tasks. This paper focuses on the steady flight control performance of the aerial manipulator. A separate control strategy is used in the aerial manipulator system, in which the UAV and the manipulator are controlled separately. In order to complete tasks in environments with strong wind disturbance, an acceleration feedback enhanced robust H∞ controller was designed for the UAV in the aerial manipulator. The controller is based on the hierarchical inner-outer loop control structure of the UAV and composed of a robust H∞ controller and acceleration feedback enhanced term, which is used to compensate for the wind disturbance. Experimental results of aerial grasping of a target object show that the controller can suppress the wind disturbance effectively, and make the aerial manipulator hover steadily with sufficient accuracy to complete aerial manipulation tasks in strong wind.

<p>An aerial manipulator is a new kind of flying robot system composed of a rotorcraft unmanned aerial vehicle (UAV) and a multi-link robotic arm. It gives the flying robot the capacity to complete manipulation tasks. Steady flight is essential for an aerial manipulator to complete manipulation tasks. This paper focuses on the steady flight control performance of the aerial manipulator. A separate control strategy is used in the aerial manipulator system, in which the UAV and the manipulator are controlled separately. In order to complete tasks in environments with strong wind disturbance, an acceleration feedback enhanced robust <em>H</em>∞ controller was designed for the UAV in the aerial manipulator. The controller is based on the hierarchical inner-outer loop control structure of the UAV and composed of a robust <em>H</em>∞ controller and acceleration feedback enhanced term, which is used to compensate for the wind disturbance. Experimental results of aerial grasping of a target object show that the controller can suppress the wind disturbance effectively, and make the aerial manipulator hover steadily with sufficient accuracy to complete aerial manipulation tasks in strong wind.</p>

<p>An aerial manipulator is a new kind of flying robot system composed of a rotorcraft unmanned aerial vehicle (UAV) and a multi-link robotic arm. It gives the flying robot the capacity to complete manipulation tasks. Steady flight is essential for an aerial manipulator to complete manipulation tasks. This paper focuses on the steady flight control performance of the aerial manipulator. A separate control strategy is used in the aerial manipulator system, in which the UAV and the manipulator are controlled separately. In order to complete tasks in environments with strong wind disturbance, an acceleration feedback enhanced robust <em>H</em>∞ controller was designed for the UAV in the aerial manipulator. The controller is based on the hierarchical inner-outer loop control structure of the UAV and composed of a robust <em>H</em>∞ controller and acceleration feedback enhanced term, which is used to compensate for the wind disturbance. Experimental results of aerial grasping of a target object show that the controller can suppress the wind disturbance effectively, and make the aerial manipulator hover steadily with sufficient accuracy to complete aerial manipulation tasks in strong wind.</p>

Aerial transportation and manipulation have attracted increasing attention in the unmanned aerial vehicle field, and visual servoing methodology is widely used to achieve the autonomous aerial grasping of a target object. However, the existing marker-based solutions pose a challenge to the practical application of target grasping owing to the difficulty in attaching markers on targets. To address this problem, this study proposes a novel image-based visual servoing controller based on natural features instead of artificial markers. The natural features are extracted from the target images and further processed to provide servoing feature points. A six degree-of-freedom (6-DoF) aerial manipulator system is proposed with differential kinematics deduced to achieve aerial grasping. Furthermore, a controller is designed when the target object is outside a manipulator’s workspace by utilizing both the degrees-of-freedom of unmanned aerial vehicle and manipulator joints. Thereafter, a weight matrix is used as basis to develop a multi-tasking visual servoing framework to integrate the controllers inside and outside the manipulator’s workspace. Lastly, experimental results are provided to verify the effectiveness of the proposed approach.

In our modern world, the application of small unmanned aerial vehicles (UAV) for monitoring work or slope mapping expanded and is widely used by people in the construction field and researchers. Slope mapping can be considered challenging when using traditional surveying methods since most slopes especially in forest regions are high and considered risky if monitored by human themselves. Other than that, mapping by using UAV need a lower number of manpower to operate the device itself which is more than enough to be conducted by a single person only. This paper discusses the applications of unmanned aerial vehicles for mapping and also its important parameters including perimeter, area and also volume of certain selected area. With the development of modern technology, the utilization of UAV to gather data for geological mapping is becoming easier as it is quick, reliable, precise, cost-effective and also easy to operate. High imagery quality and high-resolution images are essential for the effectiveness and nature of normal mapping output such as digital elevation model (DEM) and also orthoimages. With the help of established software, the parameters of three selected study areas (stockpile, slope A and slope B) can be determined easily which can be considered as one of the main interest in this study. In addition to that, the horizontal and vertical cross section of every selected area can be obtained which help to determine the highest and lowest point of each area. From this cross section, the slope path profile can be determined. Other than that, from this path profile, the potential slope hazard will be determined based on the slope angle (slope classes) as suggested by the United States Department of Agriculture (USDA). Overall, the application of unmanned aerial vehicles for photogrammetry together with slope mapping and slope hazard monitoring can be considered as a reliable modern technology which ease the work with proper assurance of analysis due to its advancement and powerful technology. This modern surveying device helps workers and researchers to simplify and fasten their work.KeywordsUnmanned aerial vehicle (UAV)Slope mappingSlope parametersSlope cross sectionDigital elevation model (DEM)Slope hazard

Wheat is a major food source throughout the world.  However, biological factors like pests and weeds can lead to lower crop yield. Most crop protection nowadays involves pesticide and herbicides application. This is commonly conducted with knapsack in China, which is inefficient and high labor intensive. Unmanned aerial vehicle (UAV) are an aerial spraying technology recently-developed. Using UAV application is more flexible and standardized, the spraying efficiency is 60 times more than knapsack sprayer. However, weed management using UAV is still a challenge. Low spray volume and droplet density with less penetration may affect weed control efficacy. High droplet concentration may induce crop injury. This study focused on discovering crop safety and weed control efficacy of UAV in wheat fields. Different herbicides, rates and spray volume were tested for pre-emergence (PRE) and post-emergence (POST). The results show that no crop injury was induced for PRE. While 10%-20% injury on wheat was found for POST. All herbicides treatments showed significant effects on weed management compared to untreated control. However, the efficacy was not stable between years and fields. Weed management for PRE can reach 98%-100% when the soil is humid, smooth and with no straw coverage when using diflufenican + isoproturon (120 + 1200 g ai ha -1 ). For POST application via UAV, weed injury ranged from 10% injury to 70%, in which isoproturon + clodinafop-propargyl + mesosulfuron (120 + 7.5 + 0.9 g ai ha -1 ) injured weed the most in 2018 (reached 70%). Knapsack sprayer showed relatively better weed control efficacy in many cases for POST applications. Weeds showed certain degree of resistance. In general, PRE application with UAV showed better potential, but herbicide spraying needs to be combined with field management to achieve better weed management efficacy. Keywords: unmanned aerial vehicle (UAV), herbicide application, weed management, wheat DOI:  10.33440/j.ijpaa.20190202.45.  Citation: Chen Y, Qi H L, Li G Z, Lan Y B.  Weed control effect of unmanned aerial vehicle (UAV) application in wheat field.  Int J Precis Agric Aviat, 2019; 2(2): 25–31.

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

In recent years, Unmanned Aerial Vehicles (UAVs) have grown and increased in applications because of computational simplicity and adaptive control capacity with strong support from both civilian and military sectors. The applications of UAVs in various military, commercial and civilian areas have led to sustainable results. The application areas include but are not limited to oil & gas, cargo transport, geographic mapping, aerial photography, health care, and disaster management. The success of the UAV application missions is completely dependent on the accuracy in control provided by the flight controllers. Thus, there is a need for accurate, robust, and adaptive flight controllers. UAV dynamics modeling and identification and control of these vehicles are still major active areas of research and development. They pose severe challenges due to the vehicle's complex design, inherently nonlinear, and time-varying dynamics. The main goal of this paper is to identify the past research trends and recent improvements in UAVs. Furthermore, this paper discusses a comprehensive literature review according to the optimized objectives, solution techniques, and applications of UAVs such as Cargo Transport, Disaster Management, etc. According to the literature review, aerial photography is one of the applications of smart UAVs. The reliability of image matching across multiple camera perspectives, angles, and positions encourages computer vision approaches for UAV navigation, opening the way for future researchers to develop vision applications. This article presents a comprehensive literature review discussing the importance of UAV applications related to cost-effectiveness and versatility. Furthermore, a detailed survey of system modeling identification and control techniques is presented.

This study empirically evaluated the effects of unmanned aerial vehicles (UAVs) applications and acceptable cost on intention to use UAVs in maritime shipping contexts. Factor analysis was employed to identify key UAVs applications (i.e. pollution forensics, supervision and service, search and rescue, humanitarian and emergency delivery, and safety and security), acceptable UAV cost, and intention to use UAVs. Survey data were collected from 201 respondents working for a maritime port corporation, shipping company, agency, and stevedoring company, and then hierarchical regression analysis was conducted to test research hypotheses and examine the effects of UAVs applications and acceptable cost dimensions on intention to use UAVs. The results suggested that the pollution forensics, supervision and service, search and rescue, humanitarian and emergency delivery, and safety and security dimensions positively affected intention to use UAVs. The study findings also revealed negative associations among seniority, turnover, and intention to use UAVs. Theoretical contributions and managerial implications are proposed to assist maritime port bureaus, corporations, and operating practice design in remaining competitive and efficient.

The integration of unmanned aerial vehicles (UAVs) into agriculture has emerged as a transformative approach to enhance resource efficiency and enable precision farming. UAVs are used for various agricultural tasks, including monitoring, mapping and spraying of pesticides, providing detailed data that support targeted and sustainable practices. However, effective deployment of UAVs in these applications faces complex control challenges. This paper presents a comprehensive review of UAVs in agricultural applications, highlighting the sophisticated control strategies required to address these challenges. Key obstacles, such as modelling inaccuracies, unstable centre of gravity (COG) due to shifting payloads, fluid sloshing within pesticide tanks and external disturbances like wind, are identified and analysed. The review delves into advanced control methodologies, with particular focus on adaptive algorithms, backstepping control and machine learning-enhanced systems, which collectively enhance UAV stability and responsiveness in dynamic agricultural environments. Through an in-depth examination of flight dynamics, stability control and payload adaptability, this paper highlights how UAVs can achieve precise and reliable operation despite environmental and operational complexities. The insights drawn from this review underscore the importance of integrating adaptive control frameworks and real-time sensor data processing, enabling UAVs to autonomously adjust to changing conditions and ensuring optimal performance in agriculture. Future research directions are proposed, advocating for the development of control systems that enhance UAV resilience, accuracy and sustainability. By addressing these control challenges, UAVs have the potential to significantly advance precision agriculture, offering practical and environmental benefits crucial to sustaining global food production demands.

The aerial manipulator is a flight robot system composed of a unmanned aerial vehicle (UAV) and a manipulator. The system combines the characteristics of maneuvering flight, hovering of the UAV and flexible operation of the manipulator, which can complete the operation task in flight state. At present, the researches on the aerial manipulator system mainly focus on the design and control of the system. There are a few researches on the grasp pose generation in the object manipulation task of the aerial manipulator. In this paper, a grasp pose estimation method for aerial manipulator is proposed. Firstly, a 3D point cloud extraction method which combines object detection and background subtraction is designed. Secondly, the 6-DOF GraspNet is utilized to generate grasp for the object. In order to generate executable grasp for the aerial manipulator, the grasp evaluation strategy of 6-DOF GraspNet is improved. The experimental results show that the proposed method can estimate an effective grasp pose for the aerial manipulator. The success rate of grasping for the bowl shaped object is 90%, and the success rate of grasp for the bottle shaped object is 83.3%.

The goal of the article is to develop proposals for the improvement of the existing normative legal documents regulating UAVs application in the surveillance of the state border of Ukraine. The research methods have been selected based on the goal and tasks of the research. A complex of general scientific and special-scientific methods has been used in the process of the research. In particular, the use of comparative and formal-logical methods made it possible to investigate the evolution of legal regulation of UAVs application by the law enforcement agencies and military formations in Ukraine. The logical and legal method has been used for the development, argumentation and determination of the directions of improving the legal regulation of UAVs application in the process of the state border surveillance. The article reveals the proposals for the improvement of the existing normative legal documents regulating unmanned aerial vehicles (UAVs) application in the surveillance of the state border of Ukraine. It is concluded that UAVs application is effective in the surveillance of the state border of Ukraine, since information obtained with the help of these aerial vehicles allows to effectively and rapidly establish facts of the state border violation and detain its violators.

A key challenge in the use of drones for an aerial manipulation task such as cutting tree branches is the control problem, especially in the presence of an unpredictable and nonlinear environment. While prior work focused on simplifying the problem by modeling a simple interaction with branches and controlling the system with nonlinear and non-robust control schemes, the current work deals with the problem by designing novel robust nonlinear controllers for aerial manipulation systems that are appropriate for vegetation sampling. In this regard, two different potential control schemes are proposed: nonlinear disturbance observer-based control (NDOBC) and adaptive sliding mode control (ASMC). Each considers the external disturbances and unknown parameters in controller design. The proposed control scheme in both methods employs a decoupled architecture that treats the unmanned aerial vehicle and the manipulator arm of the sampler payload as separate units. In the proposed control structures, controllers are designed after comprehensively investigating the dynamics of both the aerial vehicle and the robotic arm. Each system is then controlled independently in the presence of external disturbances, unknown parameter changes, and the nonlinear coupling between the aerial vehicle and robotic arm. In addition, fully actuated and underactuated aerial platforms are examined, and their stability and controllability are compared so as to choose the most practical framework. Finally, the simulation findings verify and compare the performance and effectiveness of the proposed control strategies for a custom aerial manipulation system that has been designed and developed for field trials.

Unmanned aerial vehicles (UAVs), when paired with an onboard camera, have proven to be useful tools in many applications, including aerial photography, precision agriculture, and search and rescue operations. Likewise, UAVs capable of physically interacting with the environment have shown great potential to help people perform dangerous, or time-consuming tasks more safely and efficiently than they could on their own. However, due to onboard computation and battery life limitations and complex flight dynamics, using UAVs to physically interact with the environment is still a developing area of research. Considering these limitations, the primary goals of this work are to (1) develop a new UAV platform for aerial manipulation, (2) develop modular hardware and software for the platform to enable specific tasks to be performed autonomously, and (3) develop a visual target tracking method to enable robust performance of autonomous aerial manipulation tasks in unstructured, real-world environments. To that end, the design of the Interacting-BoomCopter UAV (I-BC) is presented here as a new platform for aerial manipulation. With a simple tricopter frame, a single additional actuator for generating horizontal forces, and lightweight, modular end-effectors, the I-BC aims to balance efficiency and functionality in performing aerial manipulation tasks, and is able to perform various tasks such as mounting sensors in hard-to-reach places, and opening small doors or panels. An onboard camera, force and distance sensors, and a powerful single board computer (SBC) enable the I-BC to operate autonomously in unstructured environments, with potential applications in areas such as large-scale infrastructure inspection, industrial inspection and maintenance, and nuclear decontamination efforts.

A robotic manipulator mounted on unmanned aerial vehicle (UAV) is called an aerial manipulation system usually. Any movements of manipulator affect the stability of the UAV. In particular, the horizontal shift of the center of mass (COM) requires of the UAV the powerful controller that change forces of the propellers of quadrotor to bring the UAV to a stable state. To make manipulations by UAV it is important to save the center of mass of the aerial manipulation system in stable state. In this study, we developed a model of a manipulator to be mounted on a quadrotor. Fuzzy PID controller has been built to control of the manipulator. Control method takes into account joint space of robotic arm and unmanned aerial vehicle. The COM stabilization issue has been solved for manipulator motion. In experiments, the horizontal shift of COM of manipulator was less than 1 mm while picked up objects up to 0.15 kg of payload.