Null-Space Minimization of Center of Gravity Displacementof a Redundant Aerial Manipulator

Displacements of the base during trajectory tracking are a common issue in the control of aerial manipulators. These are caused by reaction torques transferred to the base due to the manipulator motion and, in particular, to the motion of its center of gravity. We present a novel approach to reduce base displacements of a kinematically redundant aerial manipulator by using null-space projection in the inverse kinematic control. A secondary objective function minimizes the horizontal displacement of the manipulator center of gravity. We test this algorithm on different trajectories for both three and four degrees of freedom (DOF) manipulators in a simulation environment. The results comparing our algorithm with inverse kinematic control without the null-space projection show up to an 80% reduction in the end-effector position error and an average of about 56% reduction in maximum base displacement. The simulation implementation also runs faster than in real-time in our code implementation. We provide a workspace analysis based on multiple stopping criteria such as excessive base displacement, joint velocities and end-effector position error for the 3 and 4 DOF manipulators. As expected, the 4 DOF manipulator has a larger workspace

A survey of single and multi-UAV aerial manipulation

Aerial manipulation has direct application prospects in environment, construction, forestry, agriculture, search, and rescue. It can be used to pick and place objects and hence can be used for transportation of goods. Aerial manipulation can be used to perform operations in environments inaccessible or unsafe for human workers. This paper is a survey of recent research in aerial manipulation. The aerial manipulation research has diverse aspects, which include the designing of aerial manipulation platforms, manipulators, grippers, the control of aerial platform and manipulators, the interaction of aerial manipulator with the environment, through forces and torque. In particular, the review paper presents the survey of the airborne platforms that can be used for aerial manipulation including the new aerial platforms with aerial manipulation capability. We also classified the aerial grippers and aerial manipulators based on their designs and characteristics. The recent contributions regarding the control of the aerial manipulator platform is also discussed. The environment interaction of aerial manipulators is also surveyed which includes, different strategies used for end-effectors interaction with the environment, application of force, application of torque and visual servoing. A recent and growing interest of researchers about the multi-UAV collaborative aerial manipulation was also noticed and hence different strategies for collaborative aerial manipulation are also surveyed, discussed and critically analyzed. Some key challenges regarding outdoor aerial manipulation and energy constraints in aerial manipulation are also discussed

A Contribution to the Design of Highly Redundant Compliant Aerial Manipulation Systems

Es ist vorhersehbar, dass die Luftmanipulatoren in den nächsten Jahrzehnten für viele Aufgaben eingesetzt werden, die entweder zu gefährlich oder zu teuer sind, um sie mit herkömmlichen Methoden zu bewältigen. In dieser Arbeit wird eine neuartige Lösung für die Gesamtsteuerung von hochredundanten Luftmanipulationssystemen vorgestellt. Die Ergebnisse werden auf eine Referenzkonfiguration angewendet, die als universelle Plattform für die Durchführung verschiedener Luftmanipulationsaufgaben etabliert wird. Diese Plattform besteht aus einer omnidirektionalen Drohne und einem seriellen Manipulator. Um den modularen Regelungsentwurf zu gewährleisten, werden zwei rechnerisch effiziente Algorithmen untersucht, um den virtuellen Eingang den Aktuatorbefehlen zuzuordnen. Durch die Integration eines auf einem künstlichen neuronalen Netz basierenden Diagnosemoduls und der rekonfigurierbaren Steuerungszuordnung in den Regelkreis, wird die Fehlertoleranz für die Drohne erzielt. Außerdem wird die Motorsättigung durch Rekonfiguration der Geschwindigkeits- und Beschleunigungsprofile behandelt. Für die Beobachtung der externen Kräfte und Drehmomente werden zwei Filter vorgestellt. Dies ist notwendig, um ein nachgiebiges Verhalten des Endeffektors durch die achsenselektive Impedanzregelung zu erreichen. Unter Ausnutzung der Redundanz des vorgestellten Luftmanipulators wird ein Regler entworfen, der nicht nur die Referenz der Endeffektor-Bewegung verfolgt, sondern auch priorisierte sekundäre Aufgaben ausführt. Die Wirksamkeit der vorgestellten Lösungen wird durch umfangreiche Tests überprüft, und das vorgestellte Steuerungssystem wird als sehr vielseitig und effektiv bewertet.:1 Introduction 2 Fundamentals 3 System Design and Modeling 4 Reconfigurable Control Allocation 5 Fault Diagnostics For Free Flight 6 Force and Torque Observer 7 Trajectory Generation 8 Hybrid Task Priority Control 9 System Integration and Performance Evaluation 10 ConclusionIn the following decades, aerial manipulators are expected to be deployed in scenarios that are either too dangerous for human beings or too expensive to be accomplished by traditional methods. This thesis presents a novel solution for the overall control of highly redundant aerial manipulation systems. The results are applied to a reference configuration established as a universal platform for performing various aerial manipulation tasks. The platform consists of an omnidirectional multirotor UAV and a serial manipulator. To ensure modular control design, two computationally efficient algorithms are studied to allocate the virtual input to actuator commands. Fault tolerance of the aerial vehicle is achieved by integrating a diagnostic module based on an artificial neural network and the reconfigurable control allocation into the control loop. Besides, the risk of input saturation of individual rotors is minimized by predicting and reconfiguring the speed and acceleration responses. Two filter-based observers are presented to provide the knowledge of external forces and torques, which is necessary to achieve compliant behavior of the end-effector through an axis-selective impedance control in the outer loop. Exploiting the redundancy of the proposed aerial manipulator, the author has designed a control law to achieve the desired end-effector motion and execute secondary tasks in order of priority. The effectiveness of the proposed designs is verified with extensive tests generated by following Monte Carlo method, and the presented control scheme is proved to be versatile and effective.:1 Introduction 2 Fundamentals 3 System Design and Modeling 4 Reconfigurable Control Allocation 5 Fault Diagnostics For Free Flight 6 Force and Torque Observer 7 Trajectory Generation 8 Hybrid Task Priority Control 9 System Integration and Performance Evaluation 10 Conclusio

Safe and accurate MAV Control, navigation and manipulation

This work focuses on the problem of precise, aggressive and safe Micro Aerial Vehicle (MAV) navigation as well as deployment in applications which require physical interaction with the environment. To address these issues, we propose three different MAV model based control algorithms that rely on the concept of receding horizon control. As a starting point, we present a computationally cheap algorithm which utilizes an approximate linear model of the system around hover and is thus maximally accurate for slow reference maneuvers. Aiming at overcoming the limitations of the linear model parameterisation, we present an extension to the first controller which relies on the true nonlinear dynamics of the system. This approach, even though computationally more intense, ensures that the control model is always valid and allows tracking of full state aggressive trajectories. The last controller addresses the topic of aerial manipulation in which the versatility of aerial vehicles is combined with the manipulation capabilities of robotic arms. The proposed method relies on the formulation of a hybrid nonlinear MAV-arm model which also takes into account the effects of contact with the environment. Finally, in order to enable safe operation despite the potential loss of an actuator, we propose a supervisory algorithm which estimates the health status of each motor. We further showcase how this can be used in conjunction with the nonlinear controllers described above for fault tolerant MAV flight. While all the developed algorithms are formulated and tested using our specific MAV platforms (consisting of underactuated hexacopters for the free flight experiments, hexacopter-delta arm system for the manipulation experiments), we further discuss how these can be applied to other underactuated/overactuated MAVs and robotic arm platforms. The same applies to the fault tolerant control where we discuss different stabilisation techniques depending on the capabilities of the available hardware. Even though the primary focus of this work is on feedback control, we thoroughly describe the custom hardware platforms used for the experimental evaluation, the state estimation algorithms which provide the basis for control as well as the parameter identification required for the formulation of the various control models. We showcase all the developed algorithms in experimental scenarios designed to highlight the corresponding strengths and weaknesses as well as show that the proposed methods can run in realtime on commercially available hardware.Open Acces

Design, modelling and control of a rotorcraft landing gear for uneven ground conditions

The ability to perform vertical take-off and landing, hovering and lateral flight provides rotorcrafts crucial advantages over other aircrafts and land vehicles for operations in remote areas. However, a major limitation of rotorcrafts is the requirement of a flat surface to land, increasing the difficulty and risk of landing operations on rough terrain or unstable surfaces. This limitation is mainly due to the use of conventional landing gear like skids or wheels. The growing use of Unmanned Aerial Vehicles (UAVs) also increases the necessity for more landing autonomy of these systems. This thesis presents the investigation into the development of an adaptive robotic landing gear for a small UAV that enhances the landing capabilities of current rotorcrafts. This landing gear consists in a legged system that is able to sense and adapt the position of its legs to the terrain conditions. This research covers the development of effective tools for the design and testing of the control system using software and hardware platforms. Mathematical models using multibody system dynamics are developed and implemented in software simulations. A hardware robot is designed and built to validate the simulation results. The system proposed in this thesis consists in a landing gear with four robotic legs that uses an Inertial Measurement Unit (IMU) to sense the body attitude, Force Sensing Resistors (FSR) to measure feet pressure and a distance sensor to detect ground approach. The actuators used are position-controlled servo motors that also provide angular position feedback. The control strategy provides position commands to coordinate the motion of all joints based on attitude and foot pressure information. It offers the advantage of being position-controlled, so it is easier to be implemented in hardware systems using low-cost components, and at the same time, the feet forcecontrol and leg design add compliance to the system. Through software simulations and laboratory experiments the system successfully landed on a 20° slope surface, substantially increasing the current slope landing limit

Continuous Autonomous UAV Inspection for FPSO vessels

This Master's thesis represents the preliminary design study and proposes the unmanned aerial vehicle (UAV) -based inspection framework, comprising several multirotors with automatic charging and deployment for 24/7 integrity inspection tasks. This project has three main topics. First one describes the operational environment and existing regulations that cover use of UAVs. It forms the basis for proposal of the relevant use-case scenarios. Third part comprises two chapters, where design of concept and framework is being based on the previous factors. It shows that before implementation of fully autonomous inspection system, there is a need to cover both regulatory and technical gaps. It can be explained by the fact that there does not exist any autonomous inspection system today. Thus, this project can be seen as a base for future development of the UAV-based inspection system, as it focuses on creation of a general framework

Learning Autonomous Flight Controllers with Spiking Neural Networks

The ability of a robot to adapt in-mission to achieve an assigned goal is highly desirable. This thesis project places an emphasis on employing learning-based intelligent control methodologies to the development and implementation of an autonomous unmanned aerial vehicle (UAV). Flight control is carried out by evolving spiking neural networks (SNNs) with Hebbian plasticity. The proposed implementation is capable of learning and self-adaptation to model variations and uncertainties when the controller learned in simulation is deployed on a physical platform. Controller development for small multicopters often relies on simulations as an intermediate step, providing cheap, parallelisable, observable and reproducible optimisation with no risk of damage to hardware. Although model-based approaches have been widely utilised in the process of development, loss of performance can be observed on the target platform due to simplification of system dynamics in simulation (e.g., aerodynamics, servo dynamics, sensor uncertainties). Ignorance of these effects in simulation can significantly deteriorate performance when the controller is deployed. Previous approaches often require mathematical or simulation models with a high level of accuracy which can be difficult to obtain. This thesis, on the other hand, attempts to cross the reality gap between a low-fidelity simulation and the real platform. This is done using synaptic plasticity to adapt the SNN controller evolved in simulation to the actual UAV dynamics. The primary contribution of this work is the implementation of a procedural methodology for SNN control that integrates bioinspired learning mechanisms with artificial evolution, with an SNN library package (i.e. eSpinn) developed by the author. Distinct from existing SNN simulators that mainly focus on large-scale neuron interactions and learning mechanisms from a neuroscience perspective, the eSpinn library draws particular attention to embedded implementations on hardware that is applicable for problems in the robotic domain. This C++ software package is not only able to support simulations in the MATLAB and Python environment, allowing rapid prototyping and validation in simulation; but also capable of seamless transition between simulation and deployment on the embedded platforms. This work implements a modified version of the NEAT neuroevolution algorithm and leverages the power of evolutionary computation to discover functional controller compositions and optimise plasticity mechanisms for online adaptation. With the eSpinn software package the development of spiking neurocontrollers for all degrees of freedom of the UAV is demonstrated in simulation. Plastic height control is carried out on a physical hexacopter platform. Through a set of experiments it is shown that the evolved plastic controller can maintain its functionality by self-adapting to model changes and uncertainties that take place after evolutionary training, and consequently exhibit better performance than its non-plastic counterpart