Bilateral human-robot control for semi-autonomous UAV navigation

Wopereis, H. W.; Fumagalli, Matteo; Carloni, Raffaella

doi:10.1109/iros.2015.7354115

Cited by 5 publications

(5 citation statements)

References 18 publications

(24 reference statements)

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“…Other authors have addressed UAV online control as a semi-autonomous system [26] capable of being controlled externally through a hardware interface. The semi-autonomous UAV control technique allows a human operator to intervene in the drone's actions when required to make paths that are more precise during flights [27]. Moreover, related task-specific techniques were proposed in order to use the UAV as a tool that aids the users' tasks [28,29].…”

Section: Unmanned Aerial Vehicle Controlmentioning

confidence: 99%

Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

2020

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Currently, unmanned aerial vehicles, such as drones, are becoming a part of our lives and extend to many areas of society, including the industrialized world. A common alternative for controlling the movements and actions of the drone is through unwired tactile interfaces, for which different remote control devices are used. However, control through such devices is not a natural, human-like communication interface, which sometimes is difficult to master for some users. In this research, we experimented with a domain-based speech recognition architecture to effectively control an unmanned aerial vehicle such as a drone. The drone control was performed in a more natural, human-like way to communicate the instructions. Moreover, we implemented an algorithm for command interpretation using both Spanish and English languages, as well as to control the movements of the drone in a simulated domestic environment. We conducted experiments involving participants giving voice commands to the drone in both languages in order to compare the effectiveness of each, considering the mother tongue of the participants in the experiment. Additionally, different levels of distortion were applied to the voice commands to test the proposed approach when it encountered noisy input signals. The results obtained showed that the unmanned aerial vehicle was capable of interpreting user voice instructions. Speech-to-action recognition improved for both languages with phoneme matching in comparison to only using the cloud-based algorithm without domain-based instructions. Using raw audio inputs, the cloud-based approach achieves 74.81% and 97.04% accuracy for English and Spanish instructions, respectively. However, with our phoneme matching approach the results are improved, yielding 93.33% accuracy for English and 100.00% accuracy for Spanish.

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Section: Unmanned Aerial Vehicle Controlmentioning

confidence: 99%

Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

2020

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“…Other authors have addressed UAV online control as a semi-autonomous system [26] capable of being controlled externally through a hardware interface. The semi-autonomous UAV control technique allows a human operator to intervene in the drone's actions when required to make paths that are more precise during flights [27]. Moreover, related task-specific works have been proposed aiming to use the UAV as an aiding tool for the users' tasks [28,29].…”

Section: Unmanned Aerial Vehicle Controlmentioning

confidence: 99%

Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition

Contreras,

Ayala,

Cruz

2020

Preprint

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Currently, unmanned aerial vehicles, such as drones, are becoming a part of our lives and reaching out to many areas of society, including the industrialized world. A common alternative to control the movements and actions of the drone is through unwired tactile interfaces, for which different remote control devices can be found. However, control through such devices is not a natural, human-like communication interface, which sometimes is difficult to master for some users. In this work, we present a domain-based speech recognition architecture to effectively control an unmanned aerial vehicle such as a drone. The drone control is performed using a more natural, human-like way to communicate the instructions. Moreover, we implement an algorithm for command interpretation using both Spanish and English languages, as well as to control the movements of the drone in a simulated domestic environment. The conducted experiments involve participants giving voice commands to the drone in both languages in order to compare the effectiveness of each of them, considering the mother tongue of the participants in the experiment. Additionally, different levels of distortion have been applied to the voice commands in order to test the proposed approach when facing noisy input signals.The obtained results show that the unmanned aerial vehicle is capable of interpreting user voice instructions achieving an improvement in speechto-action recognition for both languages when using phoneme matching in comparison to only using the cloud-based algorithm without domainbased instructions. Using raw audio inputs, the cloud-based approach achieves 74.81% and 97.04% accuracy for English and Spanish instructions respectively, whereas using our phoneme matching approach the results are improved achieving 93.33% and 100.00% accuracy for English and Spanish languages.

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“…1) Position control: The position controller is implemented as a PID controller [19], [20]. If a high gain attitude control is considered and small angles are assumed for the roll and pitch, the set-points for the roll and pitch can be linearized and be used as the velocity set-points.…”

Section: Control Of the Quadcopter Uavmentioning

confidence: 99%

Optimal event handling by multiple unmanned aerial vehicles

Roo

Frasca

Carloni

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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This paper proposes a control architecture for a fleet of unmanned aerial vehicles that is responsible for handling the events that take place in a given area. The architecture guarantees that each event is handled by the required number of vehicles in the shortest time, while the rest of the fleet is optimally distributed in order to achieve an optimal coverage of the area. Each vehicle is steered to the specified location by a path planner that follows the shortest path while avoiding collisions. The control architecture has been validated both in simulations and in experiments on a fleet of three vehicles.

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Bilateral human-robot control for semi-autonomous UAV navigation

Cited by 5 publications

References 18 publications

Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition

Optimal event handling by multiple unmanned aerial vehicles

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