Autonomous Aerial Manipulation Using a Hexacopter Equipped with a Robotic Arm

This paper presents the implementation of an autonomous aerial manipulation using a hexacopter equipped with a two DOF robotic arm. The kinematic and dynamic models are developed by considering the dynamic characteristics of the combined manipulation platform. A novel adaptive sliding mode controller is proposed for both position and velocity control. By building SSD (Single Shot Detection) deep neural network based on deep learning, an object detection solution is developed. The three dimensional coordinates of the target object relative to the multirotor are obtained via combining the vertical plane position obtained from object detection law with the depth value from stereo camera. Finally, with the proposed controller and object detection law, an autonomous flight experiment is accomplished including approaching, grabbing and delivering the target object. The proposed approaches are demonstrated with effectiveness and could be utilized in various manipulation applications.