2D Contour Following with an Unmanned Aerial Manipulator: Towards Tactile-Based Aerial Navigation

Hamaza, Salua; Georgilas, Ioannis; Richardson, Thomas

doi:10.1109/iros40897.2019.8968591

Cited by 11 publications

(3 citation statements)

References 26 publications

(18 reference statements)

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“…Experiments include “aerial writing tasks, where a marker pen is mounted on the aircraft frame for interaction with textured surfaces and obstacle avoidance maneuvers. On the same line, the work by Hamaza, Georgilas, and Richardson (2019) shows an aerial manipulator contour following a two‐dimensional (2D) surface while exerting a continuous force. The difference in the latter approach lays in the use of a passivity‐based force controller on the active manipulator which compensates for any drifting and shaking in the near proximity with the surface, while the on‐board attitude controller remains a standard one.…”

Section: Related Workmentioning

confidence: 84%

Design, modeling, and control of an aerial manipulator for placement and retrieval of sensors in the environment

Hamaza

Georgilas

Heredia

et al. 2020

Journal of Field Robotics

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On‐site inspection of large‐scale infrastructure often involves high risks for the operators and high insurance costs. Despite several safety measures already in place to avoid accidents, an increasing concern has brought the need to remotely monitor hard‐to‐reach locations, for which the use of aerial robots able to interact with the environment has arisen. In this paper a novel approach to aerial manipulation is presented, where a compact manipulator with a single degree‐of‐freedom is tailored for the placement and retrieval of sensors in the environment. The proposed design integrates on‐board sensing, a high‐performance force controller on the manipulator, and a thrust‐to‐force mapping on the flight controller. Experimental results demonstrate the high reliability achieved during both placement and retrieval tasks on flat surfaces (e.g., a bridge wall) and cylindrical surfaces (e.g., tree trunks). A total number of 89 flight experiments were carried out to demonstrate the robustness and potential of the compact, bespoke aerial design.

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Section: Related Workmentioning

confidence: 84%

Design, modeling, and control of an aerial manipulator for placement and retrieval of sensors in the environment

Hamaza

Georgilas

Heredia

et al. 2020

Journal of Field Robotics

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“…Due to their vertical take-off and landing (VTOL) abilities, versatility and accessibility, UAVs have become ubiquitous in the natural and industrial world. In more recent years, UAVs have been deployed to physically interact with the environment for tasks such as object transportation [1]- [4], assembly tasks [5], [6], contact-based interaction [7], [8] and inspection [9], [10], valve turning [11] and marking of surfaces [12], [13]. These interactive concepts and capabilities are poised to rapidly expand the utility of UAVs for operation and maintenance, allowing for the efficiency and safety benefits of automation to be implemented within a vast new realm of applications, some of which are depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework for Autonomous Sensor Placement With Aerial Robots

Stephens

Nguyen

Hamaza

et al. 2023

IEEE/ASME Trans. Mechatron.

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Aerial manipulators have the unique ability to cover wide-spread areas within a single mission, making them ideal for the transport and placement of sensors required to develop an instrumented environment. Recent work in the field has focused on controllers for aerial interaction that account for compliance during contact-based tasks, omitting integration concerns that are critical to an automated sensor placement solution. Furthermore, state-of-the-art flying base manipulators are often mechanically and computationally complex, reducing their efficiency and practicality. Within this work, we present an interactive framework for autonomous sensor placement that incorporates both mechanical and software based compliance, optimised for use on a simple coplanar quadrotor. Under appropriate actuation and perception constraints, we detail the development of a control, perception, and motion planning strategy to enable automated sensor placement that relies solely on onboard computation and sensing, thus presenting a fully contained and accessible sensor placement approach capable of robust interaction with the environment. An extended finite-state machine is developed to facilitate automated mission planning.Extensive flight experiments are performed to validate the effectiveness of each sub-system, as well as the integrated solution. Experiments result in trajectory tracking errors under 10 mm as well as onboard mass estimation errors under 0.7 % for sensors of various weights. A statistical analysis of 162 flight experiments shows the proposed framework's ability to autonomously place sensors within 10 cm of the target with a success rate of 93.8 % and 95 % confidence interval of (89 %, 97 %), thus confirming the robustness and repeatability of our approach. A video showcasing our implemented solution can be found here: https://youtu.be/4R8DhVpEbSQ.

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“…Moreover, while physical interaction with static and movable obstacles has been extensively studied [11], obstacles with an elastic response, such as vegetation, have received limited attention. Another limitation of current drones is that interaction tasks are constrained to sensorized end-effectors, as demonstrated by tasks such as sensor installation and retrieval [12,13], contact-based inspection [14,15], or object manipulation [16,17]. However, traversing vegetation demands an "unconstrained" interaction, wherein contacts can be established and moved along the entire body of the drone as it traverses the vegetation.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Morphology and Feedback Control for Traversal of Unknown Compliant Obstacles with Aerial Robots

Aucone,

Geckeler,

Morra

et al. 2023

Preprint

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Animals can traverse vegetation by direct physical interaction using their entire body to push aside and slide along compliant obstacles. Current drones lack this interaction versatility that stems from synergies between body morphology and feedback control modulated by sensing. Taking inspiration from nature, we show that a task-oriented design allows a drone with a minimalistic controller to traverse obstacles with unknown elastic responses. A discoid sensorized shell allows contact to be established and sensed anywhere along the shell and facilitates the sliding along the obstacle. This simplifies the formalization of the control strategy, which does not require a model of the interaction with the environments, nor high-level switching conditions for alternating between pushing and sliding, thereby reducing the computational burden. Indeed, we exploit an optimization-based controller that only ensures safety constraints on the state of the robot and dampens the oscillations of the environment during the interaction, even if the elastic response is unknown and variable. Experimental evaluation, using a hinged surface with three different stiffness values ranging from 18 to 155.5 Nmm/rad, validates the proposed strategy and the necessity for the synergic task-oriented design and feedback control. By also showcasing the traversal of real branches, this work makes an initial contribution toward drone flight across cluttered vegetation, with relevant applications in environmental monitoring, precision agriculture, and search and rescue.

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2D Contour Following with an Unmanned Aerial Manipulator: Towards Tactile-Based Aerial Navigation

Cited by 11 publications

References 26 publications

Design, modeling, and control of an aerial manipulator for placement and retrieval of sensors in the environment

Design, modeling, and control of an aerial manipulator for placement and retrieval of sensors in the environment

An Integrated Framework for Autonomous Sensor Placement With Aerial Robots

Synergistic Morphology and Feedback Control for Traversal of Unknown Compliant Obstacles with Aerial Robots

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