Virtual Reality-Based Interface for Advanced Assisted Mobile Robot Teleoperation

[EN] This work proposes a new interface for the teleoperation of mobile robots based on virtual reality that allows a natural and intuitive interaction and cooperation between the human and the robot, which is useful for many situations, such as inspection tasks, the mapping of complex environments, etc. Contrary to previous works, the proposed interface does not seek the realism of the virtual environment but provides all the minimum necessary elements that allow the user to carry out the teleoperation task in a more natural and intuitive way. The teleoperation is carried out in such a way that the human user and the mobile robot cooperate in a synergistic way to properly accomplish the task: the user guides the robot through the environment in order to benefit from the intelligence and adaptability of the human, whereas the robot is able to automatically avoid collisions with the objects in the environment in order to benefit from its fast response. The latter is carried out using the well-known potential field-based navigation method. The efficacy of the proposed method is demonstrated through experimentation with the Turtlebot3 Burger mobile robot in both simulation and real-world scenarios. In addition, usability and presence questionnaires were also conducted with users of different ages and backgrounds to demonstrate the benefits of the proposed approach. In particular, the results of these questionnaires show that the proposed virtual reality based interface is intuitive, ergonomic and easy to use.This research was funded by the Spanish Government (Grant PID2020-117421RB-C21 funded byMCIN/AEI/10.13039/501100011033) and by the Generalitat Valenciana (Grant GV/2021/181).Solanes, JE.; Muñoz García, A.; Gracia Calandin, LI.; Tornero Montserrat, J. (2022). Virtual Reality-Based Interface for Advanced Assisted Mobile Robot Teleoperation. Applied Sciences. 12(12):1-22. https://doi.org/10.3390/app12126071122121

Adaptive Shared Autonomy between Human and Robot to Assist Mobile Robot Teleoperation

Die Teleoperation vom mobilen Roboter wird in großem Umfang eingesetzt, wenn es für Mensch unpraktisch oder undurchführbar ist, anwesend zu sein, aber die Entscheidung von Mensch wird dennoch verlangt. Es ist für Mensch stressig und fehleranfällig wegen Zeitverzögerung und Abwesenheit des Situationsbewusstseins, ohne Unterstützung den Roboter zu steuern einerseits, andererseits kann der völlig autonome Roboter, trotz jüngsten Errungenschaften, noch keine Aufgabe basiert auf die aktuellen Modelle der Wahrnehmung und Steuerung unabhängig ausführen. Deswegen müssen beide der Mensch und der Roboter in der Regelschleife bleiben, um gleichzeitig Intelligenz zur Durchführung von Aufgaben beizutragen. Das bedeut, dass der Mensch die Autonomie mit dem Roboter während des Betriebes zusammenhaben sollte. Allerdings besteht die Herausforderung darin, die beiden Quellen der Intelligenz vom Mensch und dem Roboter am besten zu koordinieren, um eine sichere und effiziente Aufgabenausführung in der Fernbedienung zu gewährleisten. Daher wird in dieser Arbeit eine neuartige Strategie vorgeschlagen. Sie modelliert die Benutzerabsicht als eine kontextuelle Aufgabe, um eine Aktionsprimitive zu vervollständigen, und stellt dem Bediener eine angemessene Bewegungshilfe bei der Erkennung der Aufgabe zur Verfügung. Auf diese Weise bewältigt der Roboter intelligent mit den laufenden Aufgaben auf der Grundlage der kontextuellen Informationen, entlastet die Arbeitsbelastung des Bedieners und verbessert die Aufgabenleistung. Um diese Strategie umzusetzen und die Unsicherheiten bei der Erfassung und Verarbeitung von Umgebungsinformationen und Benutzereingaben (i.e. der Kontextinformationen) zu berücksichtigen, wird ein probabilistischer Rahmen von Shared Autonomy eingeführt, um die kontextuelle Aufgabe mit Unsicherheitsmessungen zu erkennen, die der Bediener mit dem Roboter durchführt, und dem Bediener die angemesse Unterstützung der Aufgabenausführung nach diesen Messungen anzubieten. Da die Weise, wie der Bediener eine Aufgabe ausführt, implizit ist, ist es nicht trivial, das Bewegungsmuster der Aufgabenausführung manuell zu modellieren, so dass eine Reihe von der datengesteuerten Ansätzen verwendet wird, um das Muster der verschiedenen Aufgabenausführungen von menschlichen Demonstrationen abzuleiten, sich an die Bedürfnisse des Bedieners in einer intuitiven Weise über lange Zeit anzupassen. Die Praxistauglichkeit und Skalierbarkeit der vorgeschlagenen Ansätze wird durch umfangreiche Experimente sowohl in der Simulation als auch auf dem realen Roboter demonstriert. Mit den vorgeschlagenen Ansätzen kann der Bediener aktiv und angemessen unterstützt werden, indem die Kognitionsfähigkeit und Autonomieflexibilität des Roboters zu erhöhen

Artificial Intelligence based Robotic Platforms for Autonomous Precision Agriculture

Robotic applications are continuously expanding into every aspect of human livelihood, it becomes paramount to leverage this trend for precision agriculture. The agricultural sector despite being an important sector for human is slowly evolving in terms of technology. Crude and manual processes which are conventionally used for agriculture have severe economic and social impacts. The inefficiencies and less productiveness of these methods results to food wastage amidst food shortage, inconsistencies, time consumption, higher labour expenses, and low yield. The world will benefit from automating the processes in agriculture. In bid of addressing such, it becomes necessary to build on existing platforms and develop intelligent autonomous vehicles for precision agriculture. This should include development of intelligent drones for precision agriculture, development of intelligent ground robots for precision agriculture, and other systems working cooperatively. To achieve this, we leverage on Artificial Intelligence (AI) and mathematical methods to impact sufficient intelligence on robotic platforms to make them suitable for precision agriculture. This thesis explores the capabilities of AI for weed classification and detection, weed relative position estimation, fruit 6D pose estimation and virtual reality for teleoperated systems in fruit picking. Infestation of weeds diminishes the yield of crops in agriculture. Deep learning is becoming a more popular approach for identifying weeds on farmlands. However, precision agriculture requires that the object of interest (weed) is precisely classified and detected to facilitate removal or spraying. An approach for this is presented and involves cascading a classification network (ResNet-50) with a detection network (YOLO) for weed classification and detection which we termed Fused-YOLO. Thus, weeds can precisely be located and classified (type) within an image frame. Inspired by the precision of this detection model, the work extends to presenting a novel monocular vision-based approach for drones to detect multiple types of weeds and estimate their positions autonomously for precision agriculture applications. A drone is subjected to an elliptical trajectory while acquiring images from an onboard monecular camera. The images are fed to the fused-YOLO model in real-time. The centre of the detection bounding boxes is leveraged to be the centre of the detected object of interest (weeds). The centre pixels are extracted and converted into world coordinates forming azimuth and elevation angles from the target to the UAV and are effectively used in an estimation scheme that adopts the Unscented Kalman Filteration to estimate the exact relative positions of the weeds. The robustness of this algorithm allows for both indoor and outdoor implementation while achieving a competitive result with affordable off-the-shelf sensors. Artificial intelligence for autonomous 6D pose estimation has valuable contributions to agricultural practices rallying around fruit picking, harvesting, remote operations and other contact-related applications. Conventionally, Convolutional Neural Networks (CNNs) based approaches are adopted for pose estimation. However, precision agriculture applications are demanding on higher accuracy at lower computational costs for real-time applications. Motivated by this, a novel architecture called Transpose is proposed based on transformers. TransPose is an improved Transformer-based 6D pose estimation with a depth refinement. More modalities often result in higher accuracy at the expense of computational cost. TransPose takes in a single RGB image as input without extra modality. However, an innovative light-weight depth estimation network architecture is incorporated into the model to estimate depth from an RGB image using a feature pyramid with an up-sampling method. A transformer model having proven to be efficient, regress the 6D pose directly and also outputs object patches. The depth and the patches are utilised to further refine the regressed 6D pose. The performance of the model is extensively assessed and compared with state-of-the-art methods. As part of this research, a first-ever fruit-oriented 6D pose dataset was acquired. Lastly, a seamless teleoperation pipeline that interfaces virtual reality with robots for precision agriculture tasks is proposed to pave the way for virtual agriculture. This utilises the Transpose model to estimate the 6D pose of a fruit and render it in a virtual reality environment. A robotic manipulator is which is then controlled from within the virtual reality environment to pick/harvest the fruit while being guided by the Transpose AI model. The robustness of the pipeline is tested over simulation and real-time implementation with a physical robotic manipulator is also investigated

Virtual Reality-Based Interface for Advanced Assisted Mobile Robot Teleoperation

This work proposes a new interface for the teleoperation of mobile robots based on virtual reality that allows a natural and intuitive interaction and cooperation between the human and the robot, which is useful for many situations, such as inspection tasks, the mapping of complex environments, etc. Contrary to previous works, the proposed interface does not seek the realism of the virtual environment but provides all the minimum necessary elements that allow the user to carry out the teleoperation task in a more natural and intuitive way. The teleoperation is carried out in such a way that the human user and the mobile robot cooperate in a synergistic way to properly accomplish the task: the user guides the robot through the environment in order to benefit from the intelligence and adaptability of the human, whereas the robot is able to automatically avoid collisions with the objects in the environment in order to benefit from its fast response. The latter is carried out using the well-known potential field-based navigation method. The efficacy of the proposed method is demonstrated through experimentation with the Turtlebot3 Burger mobile robot in both simulation and real-world scenarios. In addition, usability and presence questionnaires were also conducted with users of different ages and backgrounds to demonstrate the benefits of the proposed approach. In particular, the results of these questionnaires show that the proposed virtual reality based interface is intuitive, ergonomic and easy to use