Autonomous grasping with an aerial manipulator in the applications of aerial transportation and manipulation is still a challenging problem because of the complex kinematics/dynamics and motion constraints of the coupled rotors-manipulator system. The paper develops a novel aerial manipulation system with a lightweight manipulator, an X8 coaxial octocopter and onboard visual tracking system. To implement autonomous grasping control, we develop a novel and efficient approach that includes trajectory planning, visual trajectory tracking and kinematic compensation. Trajectory planning for aerial grasping control is formulated as a multi-objective optimization problem, while motion constraints and collision avoidance are considered in the optimization. A genetic method is applied to obtain the optimal solution. A kinematic compensation-based visual trajectory tracking is introduced to address the coupled affection between the manipulator and octocopter, with the advantage of discarding the complex dynamic parameter calibration. Finally, several experiments are performed to verify the effectiveness of the proposed approach.

No abstract

No abstract

No 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.