Abstract
Aerial transportation and manipulation have attracted increasing attention in the unmanned aerial vehicle field, and visual servoing methodology is widely used to achieve the autonomous aerial grasping of a target object. However, the existing marker-based solutions pose a challenge to the practical application of target grasping owing to the difficulty in attaching markers on targets. To address this problem, this study proposes a novel image-based visual servoing controller based on natural features instead of artificial markers. The natural features are extracted from the target images and further processed to provide servoing feature points. A six degree-of-freedom (6-DoF) aerial manipulator system is proposed with differential kinematics deduced to achieve aerial grasping. Furthermore, a controller is designed when the target object is outside a manipulator’s workspace by utilizing both the degrees-of-freedom of unmanned aerial vehicle and manipulator joints. Thereafter, a weight matrix is used as basis to develop a multi-tasking visual servoing framework to integrate the controllers inside and outside the manipulator’s workspace. Lastly, experimental results are provided to verify the effectiveness of the proposed approach.
Highlights
Unmanned Aerial Manipulator (UAM), which is a type of Unmanned Aerial Vehicles (UAV) equipped with a multiple Degrees-of-Freedom (DoF) robotic arm, is bio-inspired from flying birds and attracts increasing attention in robotics research
5.1 Evaluation of the ORB-based servoing point detection An experiment was first designed to evaluate the detection of servoing points, which were detected from the target object without attaching artificial markers
This study develops an AM system that achieves object grasping without artificial landmarks on the target
Summary
Unmanned Aerial Manipulator (UAM), which is a type of Unmanned Aerial Vehicles (UAV) equipped with a multiple Degrees-of-Freedom (DoF) robotic arm, is bio-inspired from flying birds and attracts increasing attention in robotics research. Seo et al.[21] formulated the visual servoing problem as a stochastic model-predictive control framework to grasp a cylindrical object using an Aerial Manipulator (AM). Note that the existing vision-based approaches for aerial grasping require artificial markers attached on target objects. [29], a convolutional neural network was used to estimate the position and orientation between the current and desired images and a Position-Based Visual Servoing (PBVS) controller was considered to reach the desired pose. These approaches are developed for large targets and suffer from high computation cost. The two control processes are combined into a hybrid formulation by utilizing a weight matrix
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