Haptic Tele-operation of Wheeled Mobile Robot and Unmanned Aerial Vehicle over the Internet

Teleoperation of ground/aerial vehicle extends operator\u27s ability (e.g. expertise, strength, mobility) into the remote environment, and haptic feedback enhances the human operator\u27s perception of the slave environment. In my thesis, two cases are studied: wheeled mobile robot (MWR) haptic tele-driving over the Internet and unmanned aerial vehicle (UAV) haptic teleoperation over the Internet. We propose novel control frameworks for both dynamic WMR and kinematic WMR in various tele-driving modes, and for a mixed UAV with translational dynamics and attitude kinematics. The recently proposed passive set-position modulation (PSPM) framework is extended to guarantee the passivity and/or stability of the closed-loop system with time-varying/packet-loss in the communication; and proved performance in steady state is shown by theoretical measurements.For UAV teleoperation, we also derive a backstepping trajectory tracking control with robustness analysis. Experimental results for dynamic/kinematic WMR and an indoor quadrotor-type UAV are presented to show the efficacy of the proposed control framework

Motion feedback in the teleoperation of Unmanned Aerial Vehicles

Teleoperation of unmanned vehicles is a valuable tool in scenarios where the operator can not or should not operate the vehicle from on-board. Applications range from hazardous environments where exposure needs to be avoided, control of Unmanned Aerial Vehicles (UAV) to retrieve overviews of inaccessible disaster areas, to deep sea exploration where on-board operation is simply not possible. However, limitations in sensor performance, noise and laten- cies introduced in the transmission, and ineffective display of the information to the operator can lead to a reduced amount of infor- mation, reduced performance, a loss of situation awareness, and in the worst case a loss of the remote vehicle. The spatial decoupling between the operator and the vehicle is one of the main challenges in teleoperation. Most setups include one or more control sticks to steer the ve- hicle, a monitor displaying the live video feed of the main vehicle camera, and a seat for the operator. This can be extended by display- ing additional state information using monitors or visual overlay, rendered on top of the main video stream [Tvaryanas, 2004; van Erp, 2000]. However, processing of multiple screens can increase mental workload. This can cause the operator to miss important information, leading to a loss of situation awareness and reduced performance or a crash of the vehicle. Instead of presenting information purely visually, other feedback modalities can be used to convey vehicle state or information about the task. The goal of this PhD thesis is to investigate the possibility of providing additional information using motion feedback. Here, motion feedback is defined as physically moving the operator using a motion simulator. In the work presented in this thesis a distinction between two motion feedback types is made. Vehicle-state motion feedback describes vehicle motion, while task-related motion feedback is the result of the combination of desired and actual vehicle motion. To investigate the effects of motion feedback in teleoperation several studies have been conducted. In the experiments presented participants either controlled a virtual quadrotor flying in a simu- lated environment or a real octorotor. Participants controlled the UAV from within the CyberMotion Simulator (CMS), an 8-DOF motion simulator located at the Max Planck Institute for Biological Cybernetics. The results show that providing motion feedback has a positive effect on performance in teleoperation of remote UAVs. If the remote vehicle is subject to external disturbances, e.g., wind gusts, vehicle- state feedback showed to improve disturbance rejection capabilities leading to increased performance. Furthermore, motion feedback can be shaped to include additional information about the task with positive effects on performance. This shows that the additional information included in the motion feedback can be used by the operator to improve performance and control behavior.Die Teleoperation eines unbemannten Gefährts ist ein wertvolles Werkzeug in Situationen, in denen der Pilot das Gefährt nicht von Bord aus steuern kann oder sollte. Beispiele hierfür reichen von, für den Piloten, toxischen Umgebungen, über Luftaufnahmen von Katastrophengebieten mithilfe von unbemannten Flugzeugen (engl. Unmanned Aerial Vehicle(UAV)), bis zur Erforschung der Tiefsee, bei der die Steuerung von Bord schlichtweg unmöglich wird. Allerdings führen Einschränkungen in der Sensorerfassung, Rau- schen und Latenzen in der Übertragung, sowie eine ineffiziente Darstellung der Informationen für den Piloten dann zu einem redu- zierten Informationsfluss, reduzierter Leistung, einem Verlust des Situationsbewusstseins und im schlimmsten Fall zu einem Verlust des Gefährts. Die räumliche Entkopplung zwischen dem Piloten und des Flugobjekts ist eine der wichtigsten Herausforderungen in der Teleoperation von UAVs. Die meisten Kontrollstationen beinhalten ein oder mehrere Steu- erknüppel um das Gefährt zu steuern, einen Monitor der eine di- rekte Videoübertragung der Hauptkamera anzeigt und ein Sitzplatz für den Piloten. Dies kann erweitert werden, in dem zusätzliche Statusinformationen mit weiteren Monitoren oder visuellen Über- lagerungen, die über die Hauptübertragung gezeichnet werden, angezeigt werden [Tvaryanas, 2004; van Erp, 2000]. Jedoch kann die Verarbeitung mehrerer Bildschirme die mentale Belastung erhö- hen. Dies kann dazu führen, dass der Pilot wichtige Informationen nicht aufnimmt, was zu einem Verlust des Situationsbewusstseins und einhergehender reduzierten Leistung oder einem Unfall des Gefährts führt. Anstatt Information rein visuell zu präsentieren, können ande- re Modalitäten genutzt werden Rückmeldungen über den Status des Gefährts oder Informationen über die Aufgabe zu präsentieren. Das Ziel dieser Doktorarbeit ist die Untersuchung der Modalität der Bewegung. Es soll untersucht werden, ob Bewegungen genutzt werden können, um dem Piloten zusätzliche Rückmeldungen über den Zustand des Gefährts bereit zu stellen. Bewegungsfeedback beschreibt hier die physikalische Bewegung des Piloten mit Hilfe eines Bewegungssimulators. In dieser Arbeit wird zwischen zwei Typen von Bewegungsfeedback unterschieden. Fahrzeugzustandsbe- wegungsfeedback beschreibt die Bewegung des Fahrzeugs, während Aufgabenabhängiges Bewegungsfeedback die Kombination aus tatsächli- chem und gewünschtem Fahrzeugzustand ist. Die Effekte von Bewegungsfeedback in der Teleoperation wurden in mehreren Studien untersucht. In den vorgestellten Experimenten kontrollierten Teilnehmer entweder einen virtuellen Quadrotor, der in einer simulierten Umgebung flog, oder einen echten Octorotor. Die Teilnehmer steuerten das UAV von der Kanzel des CyberMotion Simulators (CMS) aus, ein 8-DOF Bewegungssimulator, der sich am Max-Planck-Institut für biologische Kybernetik befindet. Die Ergebnisse zeigen, dass die Bereitstellung von Bewegungs- feedback positive Effekte auf die Leistung und das Verhalten des Piloten in der Steuerung des UAVs hat. Ist das UAV externen Stö- rungen ausgesetzt, wie z.B. Windstößen, zeigte sich, dass Fahr- zeugzustandsbewegungsfeedback die Fähigkeit der Störungsunter- drückung des Piloten verbessert, was zu Leistungsteigerungen führt. Außerdem zeigte sich, dass Bewegungsfeedback dahingehend ge- formt werden kann, zusätzliche Informationen über die Aufgabe bereitzustellen. Dies zeigt, dass die zusätzlichen Informationen vom Piloten genutzt werden können um Leistung und Kontrollverhalten zu verbessern

On-board Obstacle Avoidance in the Teleoperation of Unmanned Aerial Vehicles

Teleoperation von Drohnen in Umgebungen ohne GPS-Verbindung und wenig Bewegungsspielraum stellt den Operator vor besondere Herausforderungen. Hindernisse in einer unbekannten Umgebung erfordern eine zuverlässige Zustandsschätzung und Algorithmen zur Vermeidung von Kollisionen. In dieser Dissertation präsentieren wir ein System zur kollisionsfreien Navigation einer ferngesteuerten Drohne mit vier Propellern (Quadcopter) in abgeschlossenen Räumen. Die Plattform ist mit einem Miniaturcomputer und dem Minimum an Sensoren ausgestattet. Diese Ausstattung genügt den Anforderungen an die Rechenleistung. Dieses Setup ermöglicht des Weiteren eine hochgenaue Zustandsschätzung mit Hilfe einer Kaskaden-Architektur, sehr gutes Folgeverhalten bezüglich der kommandierten Geschwindigkeit, sowie eine kollisionsfreie Navigation. Ein Komplementärfilter berechnet die Höhe der Drohne, während ein Kalman-Filter Beschleunigung durch eine IMU und Messungen eines Optical-Flow Sensors fusioniert und in die Softwarearchitektur integriert. Eine RGB-D Kamera stellt dem Operator ein visuelles Feedback, sowie Distanzmessungen zur Verfügung, um ein Roboter-zentriertes Modell umliegender Hindernisse mit Hilfe eines Bin-Occupancy-Filters zu erstellen. Der Algorithmus speichert die Position dieser Hindernisse, auch wenn sie das Sehfeld des Sensors verlassen, mit Hilfe des geschätzten Zustandes des Roboters. Das Prinzip des Ausweich-Algorithmus basiert auf dem Ansatz einer modell-prädiktiven Regelung. Durch Vorhersage der wahrscheinlichen Position eines Hindernisses werden die durch den Operator kommandierten Sollwerte gefiltert, um eine mögliche Kollision mit einem Hindernis zu vermeiden. Die Plattform wurde experimentell sowohl in einer räumlich abgeschlossenen Umgebung mit zahlreichen Hindernissen als auch bei Testflügen in offener Umgebung mit natürlichen Hindernissen wie z.B. Bäume getestet. Fliegende Roboter bergen das Risiko, im Fall eines Fehlers, sei es ein Bedienungs- oder Berechnungsfehler, durch einen Aufprall am Boden oder an Hindernissen Schaden zu nehmen. Aus diesem Grund nimmt die Entwicklung von Algorithmen dieser Roboter ein hohes Maß an Zeit und Ressourcen in Anspruch. In dieser Arbeit präsentieren wir zwei Methoden (Software-in-the-loop- und Hardware-in-the-loop-Simulation) um den Entwicklungsprozess zu vereinfachen. Via Software-in-the-loop-Simulation konnte der Zustandsschätzer mit Hilfe simulierter Sensoren und zuvor aufgenommener Datensätze verbessert werden. Eine Hardware-in-the-loop Simulation ermöglichte uns, den Roboter in Gazebo (ein bekannter frei verfügbarer ROS-Simulator) mit zusätzlicher auf dem Roboter installierter Hardware in Simulation zu bewegen. Ebenso können wir damit die Echtzeitfähigkeit der Algorithmen direkt auf der Hardware validieren und verifizieren. Zu guter Letzt analysierten wir den Einfluss der Roboterbewegung auf das visuelle Feedback des Operators. Obwohl einige Drohnen die Möglichkeit einer mechanischen Stabilisierung der Kamera besitzen, können unsere Drohnen aufgrund von Gewichtsbeschränkungen nicht auf diese Unterstützung zurückgreifen. Eine Fixierung der Kamera verursacht, während der Roboter sich bewegt, oft unstetige Bewegungen des Bildes und beeinträchtigt damit negativ die Manövrierbarkeit des Roboters. Viele wissenschaftliche Arbeiten beschäftigen sich mit der Lösung dieses Problems durch Feature-Tracking. Damit kann die Bewegung der Kamera rekonstruiert und das Videosignal stabilisiert werden. Wir zeigen, dass diese Methode stark vereinfacht werden kann, durch die Verwendung der Roboter-internen IMU. Unsere Ergebnisse belegen, dass unser Algorithmus das Kamerabild erfolgreich stabilisieren und der rechnerische Aufwand deutlich reduziert werden kann. Ebenso präsentieren wir ein neues Design eines Quadcopters, um dessen Ausrichtung von der lateralen Bewegung zu entkoppeln. Unser Konzept erlaubt die Neigung der Propellerblätter unabhängig von der Ausrichtung des Roboters mit Hilfe zweier zusätzlicher Aktuatoren. Nachdem wir das dynamische Modell dieses Systems hergeleitet haben, synthetisierten wir einen auf Feedback-Linearisierung basierten Regler. Simulationen bestätigen unsere Überlegungen und heben die Verbesserung der Manövrierfähigkeit dieses neuartigen Designs hervor.The teleoperation of unmanned aerial vehicles (UAVs), especially in cramped, GPS-restricted, environments, poses many challenges. The presence of obstacles in an unfamiliar environment requires reliable state estimation and active algorithms to prevent collisions. In this dissertation, we present a collision-free indoor navigation system for a teleoperated quadrotor UAV. The platform is equipped with an on-board miniature computer and a minimal set of sensors for this task and is self-sufficient with respect to external tracking systems and computation. The platform is capable of highly accurate state-estimation, tracking of the velocity commanded by the user and collision-free navigation. The robot estimates its state in a cascade architecture. The attitude of the platform is calculated with a complementary filter and its linear velocity through a Kalman filter integration of inertial and optical flow measurements. An RGB-D camera serves the purpose of providing visual feedback to the operator and depth measurements to build a probabilistic, robot-centric obstacle state with a bin-occupancy filter. The algorithm tracks the obstacles when they leave the field of view of the sensor by updating their positions with the estimate of the robot's motion. The avoidance part of our navigation system is based on the Model Predictive Control approach. By predicting the possible future obstacles states, the UAV filters the operator commands by altering them to prevent collisions. Experiments in obstacle-rich indoor and outdoor environments validate the efficiency of the proposed setup. Flying robots are highly prone to damage in cases of control errors, as these most likely will cause them to fall to the ground. Therefore, the development of algorithm for UAVs entails considerable amount of time and resources. In this dissertation we present two simulation methods, i.e. software- and hardware-in-the-loop simulations, to facilitate this process. The software-in-the-loop testing was used for the development and tuning of the state estimator for our robot using both the simulated sensors and pre-recorded datasets of sensor measurements, e.g., from real robotic experiments. With hardware-in-the-loop simulations, we are able to command the robot simulated in Gazebo, a popular open source ROS-enabled physical simulator, using computational units that are embedded on our quadrotor UAVs. Hence, we can test in simulation not only the correct execution of algorithms, but also the computational feasibility directly on the robot's hardware. Lastly, we analyze the influence of the robot's motion on the visual feedback provided to the operator. While some UAVs have the capacity to carry mechanically stabilized camera equipment, weight limits or other problems may make mechanical stabilization impractical. With a fixed camera, the video stream is often unsteady due to the multirotor's movement and can impair the operator's situation awareness. There has been significant research on how to stabilize videos using feature tracking to determine camera movement, which in turn is used to manipulate frames and stabilize the camera stream. However, we believe that this process could be greatly simplified by using data from a UAV’s on-board inertial measurement unit to stabilize the camera feed. Our results show that our algorithm successfully stabilizes the camera stream with the added benefit of requiring less computational power. We also propose a novel quadrotor design concept to decouple its orientation from the lateral motion of the quadrotor. In our design the tilt angles of the propellers with respect to the quadrotor body are being simultaneously controlled with two additional actuators by employing the parallelogram principle. After deriving the dynamic model of this design, we propose a controller for this platform based on feedback linearization. Simulation results confirm our theoretical findings, highlighting the improved motion capabilities of this novel design with respect to standard quadrotors

The use of modern tools for modelling and simulation of UAV with Haptic

Unmanned Aerial Vehicle (UAV) is a research field in robotics which is in high demand in recent years, although there still exist many unanswered questions. In contrast, to the human operated aerial vehicles, it is still far less used to the fact that people are dubious about flying in or flying an unmanned vehicle. It is all about giving the control right to the computer (which is the Artificial Intelligence) for making decisions based on the situation like human do but this has not been easy to make people understand that it’s safe and to continue the enhancement on it. These days there are many types of UAVs available in the market for consumer use, for applications like photography to play games, to map routes, to monitor buildings, for security purposes and much more. Plus, these UAVs are also being widely used by the military for surveillance and for security reasons. One of the most commonly used consumer product is a quadcopter or quadrotor. The research carried out used modern tools (i.e., SolidWorks, Java Net Beans and MATLAB/Simulink) to model controls system for Quadcopter UAV with haptic control system to control the quadcopter in a virtual simulation environment and in real time environment. A mathematical model for the controlling the quadcopter in simulations and real time environments were introduced. Where, the design methodology for the quadcopter was defined. This methodology was then enhanced to develop a virtual simulation and real time environments for simulations and experiments. Furthermore, the haptic control was then implemented with designed control system to control the quadcopter in virtual simulation and real time experiments. By using the mathematical model of quadcopter, PID & PD control techniques were used to model the control setup for the quadcopter altitude and motion controls as work progressed. Firstly, the dynamic model is developed using a simple set of equations which evolves further by using complex control & mathematical model with precise function of actuators and aerodynamic coefficients Figure5-7. The presented results are satisfying and shows that flight experiments and simulations of the quadcopter control using haptics is a novel area of research which helps perform operations more successfully and give more control to the operator when operating in difficult environments. By using haptic accidents can be minimised and the functional performance of the operator and the UAV will be significantly enhanced. This concept and area of research of haptic control can be further developed accordingly to the needs of specific applications

An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model for Enhancing UAV Landing on Moving Targets

The autonomous landing of an unmanned aerial vehicle (UAV) on a moving platform is an essential functionality in various UAV-based applications. It can be added to a teleoperation UAV system or part of an autonomous UAV control system. Various robust and predictive control systems based on the traditional control theory are used for operating a UAV. Recently, some attempts were made to land a UAV on a moving target using reinforcement learning (RL). Vision is used as a typical way of sensing and detecting the moving target. Mainly, the related works have deployed a deep-neural network (DNN) for RL, which takes the image as input and provides the optimal navigation action as output. However, the delay of the multi-layer topology of the deep neural network affects the real-time aspect of such control. This paper proposes an adaptive multi-level quantization-based reinforcement learning (AMLQ) model. The AMLQ model quantizes the continuous actions and states to directly incorporate simple Q-learning to resolve the delay issue. This solution makes the training faster and enables simple knowledge representation without needing the DNN. For evaluation, the AMLQ model was compared with state-of-art approaches and was found to be superior in terms of root mean square error (RMSE), which was 8.7052 compared with the proportional-integral-derivative (PID) controller, which achieved an RMSE of 10.0592

The use of modern tools for modelling and simulation of UAV with Haptic

Unmanned Aerial Vehicle (UAV) is a research field in robotics which is in high demand in recent years, although there still exist many unanswered questions. In contrast, to the human operated aerial vehicles, it is still far less used to the fact that people are dubious about flying in or flying an unmanned vehicle. It is all about giving the control right to the computer (which is the Artificial Intelligence) for making decisions based on the situation like human do but this has not been easy to make people understand that it’s safe and to continue the enhancement on it. These days there are many types of UAVs available in the market for consumer use, for applications like photography to play games, to map routes, to monitor buildings, for security purposes and much more. Plus, these UAVs are also being widely used by the military for surveillance and for security reasons. One of the most commonly used consumer product is a quadcopter or quadrotor. The research carried out used modern tools (i.e., SolidWorks, Java Net Beans and MATLAB/Simulink) to model controls system for Quadcopter UAV with haptic control system to control the quadcopter in a virtual simulation environment and in real time environment. A mathematical model for the controlling the quadcopter in simulations and real time environments were introduced. Where, the design methodology for the quadcopter was defined. This methodology was then enhanced to develop a virtual simulation and real time environments for simulations and experiments. Furthermore, the haptic control was then implemented with designed control system to control the quadcopter in virtual simulation and real time experiments. By using the mathematical model of quadcopter, PID & PD control techniques were used to model the control setup for the quadcopter altitude and motion controls as work progressed. Firstly, the dynamic model is developed using a simple set of equations which evolves further by using complex control & mathematical model with precise function of actuators and aerodynamic coefficients Figure5-7. The presented results are satisfying and shows that flight experiments and simulations of the quadcopter control using haptics is a novel area of research which helps perform operations more successfully and give more control to the operator when operating in difficult environments. By using haptic accidents can be minimised and the functional performance of the operator and the UAV will be significantly enhanced. This concept and area of research of haptic control can be further developed accordingly to the needs of specific applications

Twin Delayed Deep Deterministic Policy Gradient-Based Target Tracking for Unmanned Aerial Vehicle with Achievement Rewarding and Multistage Training

Target tracking using an unmanned aerial vehicle (UAV) is a challenging robotic problem. It requires handling a high level of nonlinearity and dynamics. Model-free control effectively handles the uncertain nature of the problem, and reinforcement learning (RL)-based approaches are a good candidate for solving this problem. In this article, the Twin Delayed Deep Deterministic Policy Gradient Algorithm (TD3), as recent and composite architecture of RL, was explored as a tracking agent for the UAV-based target tracking problem. Several improvements on the original TD3 were also performed. First, the proportional-differential controller was used to boost the exploration of the TD3 in training. Second, a novel reward formulation for the UAV-based target tracking enabled a careful combination of the various dynamic variables in the reward functions. This was accomplished by incorporating two exponential functions to limit the effect of velocity and acceleration to prevent the deformation in the policy function approximation. In addition, the concept of multistage training based on the dynamic variables was proposed as an opposing concept to one-stage combinatory training. Third, an enhancement of the rewarding function by including piecewise decomposition was used to enable more stable learning behaviour of the policy and move out from the linear reward to the achievement formula. The training was conducted based on fixed target tracking followed by moving target tracking. The flight testing was conducted based on three types of target trajectories: fixed, square, and blinking. The multistage training achieved the best performance with both exponential and achievement rewarding for the fixed trained agent with the fixed and square moving target and for the combined agent with both exponential and achievement rewarding for a fixed trained agent in the case of a blinking target. With respect to the traditional proportional differential controller, the maximum error reduction rate is 86%. The developed achievement rewarding and the multistage training opens the door to various applications of RL in target tracking

Novel Haptic Cueing for UAV Tele-Operation.

The use of Unmanned Aerial Vehicles (UAVs) is continuously increasing both for military and civilian operations. The degree of automation inside an UAV has reached the capability of high levels of autonomy, increasing but human participation/action is still a requirement to ensure an ultimate level of safety for the mission. Direct remote piloting is often required for a board range of situations; this is true especially for larger UAVs, where a fault might be dangerous for the platform but even for the other entities of its environment (people, building etc.). Unfortunately the physical separation between pilot/operator and the UAV reduces greatly the situational awareness; this has a negative impact on system performance in the presence of remote and unforeseen environmental constraints and disturbances. This is why this thesis is dedicated to the study of means to increase the level of situational awareness of the UAV operator. The sense of telepresence is very important in teleoperation, and it appears reasonable, and it has already been shown in the literature, that extending the visual feedback with force feedback is able to complement the visual information (when missing or limited). An artificially recreated sense of touch (haptic) may allow the operator to better perceive information from the remote aircraft state, the environment and its constraints, hopefully preventing dangerous situations. This thesis introdues first a novel classification for haptic aid systems in two large classes: Direct Haptic Aid (DHA) and Indirect Haptic Aid (IHA), then, after showing that almost all existing aid concepts belong to the first class, focuses on IHA and tries to show that classical applications (that used a DHA approach) can be revised in a IHA fashion. The novel IHA systems produce different sensations, which in most cases may appear as exactly "opposite in sign" from the corresponding DHA; these sensations can provide valuable cues for the pilot, both in terms of improvement of performance and "level of appreciation". Furthermore, it will be shown that the novel cueing algorithms, which were designed just to appear "natural" to the operator, and not to directly help the pilot in his task (as in the DHA cases), can outperform the corresponding DHA systems. Three case studies were selected: obstacle avoidance, wind gust rejection, and a combination of the two. For all the cases, DHA and IHA systems were designed and compared against baseline performance with no haptic aid. Test results show that a net improvement in terms of performance is provided by employing the IHA cuse instead of both the DHA cues or the visual cues only. Both professional pilots and naïve subjects were used in some of the experiments. The perceived feelings transmitted by the haptic cues, strongly depend by the type of the experiment and the quality of the participants: the professional pilots, for instance, retained the DHA the most helpful force while they preferred IHA because they found it more natural and because they felt a better control authority on the aircraft; different results were obtained with naive participants. In the end, this thesis aim is to show that the IHA philosophy is a valid and promising alternative to the other commonly used, and published in the scientific literature, approaches which fall in the DHA category. Finally the haptic cueing for the obstacle avoidance task was tested in the presence of time delay in the communication link, as in a classical bilateral teleoperation scheme. The Master was provide with an admittance controller and an observer for force exerted by the human on the stick was developed. Experiments have shown that the proposed system is capable of standing substantial communication delays