Aerial Robotic Platforms Research Articles

A new tilted aerial robotic platform: Modeling and control

Aerial Manipulators are considered in modern applications due to their essential features that have the maneuverability of Unmanned Aerial Vehicles (UAVs) and abilities of robotics arms. One of the primary challenges encountered by aerial manipulators is their inability to maintain a consistent orientation though the duration of flight. This limits the application of aerial manipulators for movement of fragile and liquid objects that should not change their orientation during the flight. To solve this issue, this study introduced a new Aerial Robot Platform (AR-P) which can decouple all motions by configuring tilting rotors into the structure of the AR-P. The AR-P consists of a Tilted-Rotor Quadcopter (T-RQ) and three linked Aerial Robot Arm (AR-A) that have a gripper to grasp objects. The AR-P with this new mechanism of tilting parts overcomes the problem of manipulation limitations by adjusting the direction of the thrusted rotors according to the exerted forces of the grasped object. The new dynamics model of the AR-P is derived using the Lagrange-d'Alembert approach. A special Multi Input Multi Output (MIMO) Proportional Derivative (PD) controller based on Computed Torque Control (CTC) is designed for the developed AR-P. The method of nonlinear least squares is applied to obtain the optimal gains of the over-looped PD controller. The simulation results showed the ability of the developed CTC to track the required joint angle of each the T-RQ and each link of the AR-A with minimizing steady state errors and without overshooting. The implemented controller showed its robustness against external disturbances in form of wind conditions. On one hand, the controller is tested considering traditional aerial manipulator. On the other hand, the controller is tested by our new tilted aerial robot platform. The comparison results of the flight scenario between the new tilted aerial manipulator and the traditional one approved the advantage of the proposed new tilted AR-P to transport the grasped objects in fixed orientation.

Modeling, Control and Implementation of Adaptive Reconfigurable ROtary Wings (ARROWs)

Inspired by the self-rotating motion of the descending maple seeds, we introduce a novel modular aerial robotic platform—ARROWs. With customized wing and control modules, ARROWs can be easily reassembled into different configurations. Unlike conventional multirotor aerial vehicles which rely on the direct thrust from propellers, ARROWs can generate more lift by their revolving wings. However, the complex dynamics causes difficulties in flight controller development. In this work, we first analyze the flight dynamics by considering a combination of effects from the propeller, aerodynamic force on the wing, as well as self-rotating motion. As a result, a cascaded flight controller with a unified framework is designed based on reduced flight dynamics and relaxed hovering conditions to achieve stable flights in all proposed configurations. In addition, a set of inertial measurement units is employed for each flight module to estimate the flight configuration to overcome the dynamic uncertainties caused by manual re-construction. Finally, our proposed platform and flight control strategy are validated using several flight experiments in 12 different configurations (include both centrosymmetric and centrally asymmetric cases). The results show an average position error of 8.9 cm with a deviation of 2.4 cm among all configurations in hovering tests.

Concept of a Modular Multirotor Heavy Lift Unmanned Aerial Vehicle Platform

This paper presents a novel concept of a modular multirotor aerial robotic platform that can be used for specific profiles of heavy payload missions. A comprehensive mathematical model of the multirotor unmanned aerial vehicle (UAV) is presented, which is divided into the dynamic model and the control allocation scheme that describes the configuration of the aircraft. The components of the propulsion and energy module are selected, and the characterization of the propulsion units is carried out, as well as a preliminary analysis of the module parameters with regard to the considered payloads from 5 to 50 kg. The main goal is to design a modular aircraft that consists of easy-to-assemble modules and that enables the assembly of different propulsion and energy module configurations. Based on the analysis of system parameters, a modular aerial system is designed and the process of manufacturing using rapid prototyping technologies is presented. Based on the parameters obtained from the aircraft assembly CAD model and the implemented mathematical model, simulations are conducted for the two considered missions in the field of smart agriculture. For the purpose of conducting preliminary experiments, a quadrotor aircraft is built and tested in the case of attitude and remote control.

Prototype design of an aerial robotic platform for indoor applications

There is an increased interest in using aerial robotic platforms for indoor applications in industrial and manufacturing environments. One such example is stocktaking in warehouses, where the use of other mobile robotic systems is not efficient as they aren’t able to reach higher shelves or operate at the same speed as an aerial robotic platform. In this paper we discuss a prototype design for an aerial robotic platform that will be able to operate in GPS-denied areas, such as indoor warehouses. Suitable choices for the hardware and avionics are proposed based on the system and user requirements. The final design weighs 194 g, costs R 7 354.3, and has an estimated flight time of 5.5 min which is within the system requirements.

Time-delay control of quadrotor unmanned aerial vehicles: a multiplicity-induced-dominancy-based approach

The current work exploits the effects of time-delays on the stability of unmanned aerial vehicles s). In this regard, the main contribution is a symbolic/numeric application of the multiplicity-induced-dominancy property in the control of unmanned aerial vehicles rotorcrafts featuring time-delays. The multiplicity-induced-dominancy property is considered to address two of the most representative aerial robotic platforms: a classical quadrotor vehicle and a quadrotor vehicle endowed with tilting rotors. The aforementioned property leads to an effective delayed feedback control design (multiplicity-induced-dominancy tuning criteria), allowing the system to meet prescribed behavior conditions based on the placement of the rightmost root of the corresponding closed-loop characteristic function/quasipolynomial. Lastly, the results of detailed numerical simulations, including the linear and non-linear dynamics of the vehicle, are presented and discussed to validate the proposal.

Viscoelastic Fluid-Inspired Swarm Behavior to Reduce Susceptibility to Local Minima: The Chain Siphon Algorithm

We present a novel distributed robotic swarm algorithm inspired by the open channel siphon phenomenon displayed in certain viscoelastic fluids. Self-siphoning viscoelastic fluids are often able to mitigate the trapping effects of local minima in the environment. Using a similar strategy, our algorithm enables a robot swarm to mitigate the trapping effects of local minima in potential fields. Once a robot senses the goal, local communication between robots is used to propagate path-to-goal gradient information through the swarm's communication graph. This information is used to augment each agent's local potential field, reducing the local minima traps and often eliminating them. We perform hardware experiments using the Georgia Tech Miniature Autonomous Blimp (GT-MAB) aerial robotic platforms as well as Monte Carlo simulations conducted in the Simulating Collaborative Robots in Massive Multi-Agent Game Execution (SCRIMMAGE) simulator. We compare the new method to other potential field based swarm behaviors that both do and do not incorporate local minima fixes. The distributed algorithm generates self-siphoning behavior within the robotic swarm, and this reduces its susceptibility to local minima.

Autonomous Source Term Estimation in Unknown Environments: From a Dual Control Concept to UAV Deployment

In the gas source search and localisation problem, the use of autonomous robots is of increasing interest due to their deployment speed and lack of human interaction with hazardous materials. This letter presents an aerial robotic platform for performing source term estimation of an unknown chemical release in a challenging a-priori unknown and GPS-denied environment. The proposed system forms the search strategy using the state-of-the-art control concept, dual control for exploitation and exploration, and realises such a function in the aforementioned challenging scenario using an RRT* based path planner. A novel downsampling process on the RRT* is also proposed that addresses the computational infeasibility of calculating the utility of a large number of sample states, whilst still maintaining sample state diversity. The proposed algorithm is tested in a high fidelity simulation environment under a number of configurations, and compared against competing algorithms. The system architecture is also brought forward into a bespoke UAV platform and experimentally tested in real-world conditions. The proposed system is shown to be capable of performing source term estimation robustly and efficiently, which provides a step forward in showing the real world application of previously academic functions.

On Aerial Robots with Grasping and Perching Capabilities: A Comprehensive Review.

Over the last decade, there has been an increased interest in developing aerial robotic platforms that exhibit grasping and perching capabilities not only within the research community but also in companies across different industry sectors. Aerial robots range from standard multicopter vehicles/drones, to autonomous helicopters, and fixed-wing or hybrid devices. Such devices rely on a range of different solutions for achieving grasping and perching. These solutions can be classified as: 1) simple gripper systems, 2) arm-gripper systems, 3) tethered gripping mechanisms, 4) reconfigurable robot frames, 5) adhesion solutions, and 6) embedment solutions. Grasping and perching are two crucial capabilities that allow aerial robots to interact with the environment and execute a plethora of complex tasks, facilitating new applications that range from autonomous package delivery and search and rescue to autonomous inspection of dangerous or remote environments. In this review paper, we present the state-of-the-art in aerial grasping and perching mechanisms and we provide a comprehensive comparison of their characteristics. Furthermore, we analyze these mechanisms by comparing the advantages and disadvantages of the proposed technologies and we summarize the significant achievements in these two research topics. Finally, we conclude the review by suggesting a series of potential future research directions that we believe that are promising.

Aerial Robotic Solution for Detailed Inspection of Viaducts

The inspection of public infrastructure, such as viaducts and bridges, is crucial for their proper maintenance given the heavy use of many of them. Current inspection techniques are very costly and manual, requiring highly qualified personnel and involving many risks. This article presents a novel solution for the detailed inspection of viaducts using aerial robotic platforms. The system provides a highly automated visual inspection platform that does not rely on GPS and could even fly underneath the infrastructure. Unlike commercially available solutions, our system automatically references the inspection to a global coordinate system usable throughout the lifespan of the infrastructure. In addition, the system includes another aerial platform with a robotic arm to make contact inspections of detected defects, thus providing information that cannot be obtained only with images. Both aerial robotic platforms feature flexibility in the choice of camera or contact measurement sensors as the situation requires. The system was validated by performing inspection flights on real viaducts.

Networking for Cloud Robotics: The DewROS Platform and Its Application

With the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System (ROS) which embodies the three-layer, Dew-Robotics architecture, where computation and storage can be distributed among the robot, the network devices close to it, and the Cloud. After presenting the design and implementation of DewROS, we show its application in a real use-case called SHERPA, which foresees a mixed ground and aerial robotic platform for search and rescue in an alpine environment. We used DewROS to analyze the video acquired by the drones in the Cloud and quickly spot signs of human beings in danger. We perform a wide experimental evaluation using different network technologies and Cloud services from Google and Amazon. We evaluated the impact of several variables on the performance of the system. Our results show that, for example, the video length has a minimal impact on the response time with respect to the video size. In addition, we show that the response time depends on the Round Trip Time (RTT) of the network connection when the video is already loaded into the Cloud provider side. Finally, we present a model of the annotation time that considers the RTT of the connection used to reach the Cloud, discussing results and insights into how to improve current Cloud Robotics applications.

Character Synthesis of Ballet Archetypes on Robots Using Laban Movement Analysis: Comparison Between a Humanoid and an Aerial Robot Platform with Lay and Expert Observation

Imitation of human movement by robots is an important area of research to create expressive systems that are able to interact with humans in social settings. One lens through which imitation may be investigated is that of character archetypes, commonly found in human story-telling. Classical ballet, a form of story-telling through movement, makes extensive use of such archetypes. This paper reviews how Laban Movement Analysis creates a descriptive taxonomy in which features of interest may be identified and reported from human performers to robotic platforms, specifically for a BeBop aerial drone and a Nao humanoid platform. To determine the success of this approach, the paper also presents evaluation by lay human viewers that validates the character transfer. Moreover, this paper offers more nuance into this evaluation by querying two specialized groups of experts, which frequently deviated from lay ratings and, thus, offer deeper insight into both the success of the character transfer and the importance of prior training in movement perception. This work may be useful in human-facing scenarios where character archetypes facilitate engagement, identification, and understanding between robots and human counterparts. Further, it highlights nuances in working with expert artists to create broadly applicable designs.

VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems

Indoor environments have abundant presence of high-level semantic information which can provide a better understanding of the environment for robots to improve the uncertainty in their pose estimate. Although semantic information has proved to be useful, there are several challenges faced by the research community to accurately perceive, extract and utilize such semantic information from the environment. In order to address these challenges, in this paper we present a lightweight and real-time visual semantic SLAM framework running on board aerial robotic platforms. This novel method combines low-level visual/visual-inertial odometry (VO/VIO) along with geometrical information corresponding to planar surfaces extracted from detected semantic objects. Extracting the planar surfaces from selected semantic objects provides enhanced robustness and makes it possible to precisely improve the metric estimates rapidly, simultaneously generalizing to several object instances irrespective of their shape and size. Our graph-based approach can integrate several state of the art VO/VIO algorithms along with the state of the art object detectors in order to estimate the complete 6DoF pose of the robot while simultaneously creating a sparse semantic map of the environment. No prior knowledge of the objects is required, which is a significant advantage over other works. We test our approach on a standard RGB-D dataset comparing its performance with the state of the art SLAM algorithms. We also perform several challenging indoor experiments validating our approach in presence of distinct environmental conditions and furthermore test it on board an aerial robot. Video:https://vimeo.com/368217703Released Code:https://bitbucket.org/hridaybavle/semantic_slam.git.

Universal Flying Objects: Modular Multirotor System for Flight of Rigid Objects

In this article, we introduce Universal Flying Objects—a modular aerial robotic platform for transforming a rigid object into a multirotor robot. To achieve this, we develop flight modules, in the form of a control module and propelling modules that can be affixed to an object. The object, or payload, serves as the airframe of the vehicle. The modular design produces a highly versatile platform as it is reconfigurable by the addition or removal of flight modules, adjustment of the modules’ arrangement, or change of payloads. To facilitate the flight control, we propose an inertial measurement unit (IMU)-based estimation strategy for rapid computation of the robot's configuration. When combined with the adaptive geometric controller for further refinement of uncertain parameters, stable flights are accomplished with minimal manual intervention or tuning required by a user. To this end, we demonstrate hovering and trajectory tracking flights through various robot configurations with different dummy payloads, weighing $\approx$ 200–800 g, using four to eight propelling modules. The results reveal that stable flights are attainable thanks to the proposed IMU-based estimation method. The flight performance is markedly improved over time through the adaptive scheme, with position errors of a few centimeters after the parameter convergence.

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions.

Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle—usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps.

A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques

Search and Rescue (SAR) missions represent an important challenge in the robotics research field as they usually involve exceedingly variable-nature scenarios which require a high-level of autonomy and versatile decision-making capabilities. This challenge becomes even more relevant in the case of aerial robotic platforms owing to their limited payload and computational capabilities. In this paper, we present a fully-autonomous aerial robotic solution, for executing complex SAR missions in unstructured indoor environments. The proposed system is based on the combination of a complete hardware configuration and a flexible system architecture which allows the execution of high-level missions in a fully unsupervised manner (i.e. without human intervention). In order to obtain flexible and versatile behaviors from the proposed aerial robot, several learning-based capabilities have been integrated for target recognition and interaction. The target recognition capability includes a supervised learning classifier based on a computationally-efficient Convolutional Neural Network (CNN) model trained for target/background classification, while the capability to interact with the target for rescue operations introduces a novel Image-Based Visual Servoing (IBVS) algorithm which integrates a recent deep reinforcement learning method named Deep Deterministic Policy Gradients (DDPG). In order to train the aerial robot for performing IBVS tasks, a reinforcement learning framework has been developed, which integrates a deep reinforcement learning agent (e.g. DDPG) with a Gazebo-based simulator for aerial robotics. The proposed system has been validated in a wide range of simulation flights, using Gazebo and PX4 Software-In-The-Loop, and real flights in cluttered indoor environments, demonstrating the versatility of the proposed system in complex SAR missions.

An innovative tri-rotor drone and associated distributed aerial drone swarm control

This paper presents a novel unmanned aerial vehicle platform based on a three rotor configuration, which can achieve the highest level of maneuverability in all 6 dimensions (i.e. 3D position and 3D attitude). The three propellers can be tilted independently to obtain full force and torque vectoring authority, such that this new aerial robotic platform can overcome the limitations of a classic quadrotor UAV that cannot change its attitude while hovering at a stationary position. A robust feedback linearization controller is developed to deal with this highly coupled and nonlinear dynamics of the proposed tri-rotor UAV, which linearizes the dynamics globally using geometric transformations to produce a linear model that matches the Jacobi linearization of the nonlinear dynamics at the operating point of interest. A distributed formation control tracking protocol is then proposed to control a swarm of tri-rotor UAVs. The 3D position and 3D attitude of each vehicle can be controlled independently to follow a desired time-varying formation. The effectiveness of the designed control strategy is illustrated in a realistic virtual reality simulation environment based on real hardware parameters from a physical construction.

Development of a Wall-Sticking Drone for Non-Destructive Ultrasonic and Corrosion Testing

Refineries’ structures require constant inspection, maintenance of their structural health condition, and safety of the users; however, accessing these structures is getting more and more difficult due to their enormous height and size. In order to deal with this problem, many researchers have developed several robots for wall crawling, yet there is no guaranteed solution. One of the critical reasons why existing wall-crawling robots have not been available in the field is the risk of accidental fall due to operational failure from the harsh environment, like strong wind and the surface’s unpredictable condition. Therefore, we attempted to develop a wall-sticking aerial robot platform that can approach any place of the structure by flying and sticking to the target place. The robot is equipped with electro-magnetic hold mount elements to stick the sensor probe on the surface of the structure. This paper deals with installing the wall-sticking mechanism on the aerial robot.

A small-scale hyperacute compound eye featuring active eye tremor: application to visual stabilization, target tracking, and short-range odometry

In this study, a miniature artificial compound eye (15 mm in diameter) called the curved artificial compound eye (CurvACE) was endowed for the first time with hyperacuity, using similar micro-movements to those occurring in the fly’s compound eye. A periodic micro-scanning movement of only a few degrees enables the vibrating compound eye to locate contrasting objects with a 40-fold greater resolution than that imposed by the interommatidial angle. In this study, we developed a new algorithm merging the output of 35 local processing units consisting of adjacent pairs of artificial ommatidia. The local measurements performed by each pair are processed in parallel with very few computational resources, which makes it possible to reach a high refresh rate of 500 Hz. An aerial robotic platform with two degrees of freedom equipped with the active CurvACE placed over naturally textured panels was able to assess its linear position accurately with respect to the environment thanks to its efficient gaze stabilization system. The algorithm was found to perform robustly at different light conditions as well as distance variations relative to the ground and featured small closed-loop positioning errors of the robot in the range of 45 mm. In addition, three tasks of interest were performed without having to change the algorithm: short-range odometry, visual stabilization, and tracking contrasting objects (hands) moving over a textured background.

Control theoretic interpretation of directional motion preferences in optic flow processing interneurons

In this article, we formalize the processing of optic flow in identified fly lobula plate tangential cells and develop a control theoretic framework that suggests how the signals of these cells may be combined and used to achieve reflex-like navigation behavior. We show that this feedback gain synthesis task can be cast as a combined static state estimation and linear feedback control problem. Our framework allows us to analyze and determine the relationship between optic flow measurements and actuator commands, which greatly simplifies the implementation of biologically inspired control architectures on terrestrial and aerial robotic platforms.