## 1 Introduction  
Autonomous systems such as aerial manipulators have seen an uptake in recent years for conducting safety-critical and resource-intensive applications in industry, such as the inspection, assembly, and maintenance of mechanical structures, and in general for inspection and maintenance tasks. Aerial manipulators offer improved feasibility of executing difficult tasks due to the small size and improved maneuverability in comparison to humans. The types of aerial robotic manipulation platforms are defined by their base and manipulation design. The most common base is a multicopter unmanned aerial vehicle (UAV) with four or more rotors [1], which can remain hovering while the manipulator completes the required task. The manipulator can have several different designs, and for a large workspace and dexterity multiple joint arms are extensively used. The types of aerial robotic manipulation platforms are defined by their actuator and manipulation design. Based on the mechanical design of these two aspects, platformsare specialized for various tasks such as grasping, pulling/pushing, sliding, bending, or assembly. The actuator design ranges from single to multiple propeller configurations, with various additional actuators to allow for omni-directional motion. The most common type of platform is a parallel propeller configuration with tilted rotors [1], such as TiltHex [2], AEROX [3], and ODAR [4], equipped with rigid links, grippers, or articulated arms. This propeller configuration has also been extended to include servomotors that can tilt the propeller arms, enabling full omni-directional motion [4,5]. The design and control of aerial manipulators face many challenges compared to other types of mobile robots. Unlike wheeled robots, aerial manipulators do not have a stable base, and therefore the forces and torques generated by the manipulator and interactions with the environment affect the platform as a whole [6]. This impacts the position, attitude, stability, and flight autonomy of the aerial manipulator. Various approaches in controller design ranging from decoupled to coupled have been explored to address this problem. A decoupled approach controls the robot and manipulator separately, and compensates for the interaction forces and related motions through disturbance observers and robust controllers. A coupled approach models the full-body dynamics of both the base and the manipulator. A partially coupled approach is also possible, where the control of both components is independent, but improved through sensing or estimation of interaction wrenches on each other [7]. The coupling of both the kinematics and dynamics of the aerial robot and manipulator results in difficulty in precise positioning and control of the end-effector, which is required for complex tasks such as grasping. Some existing approaches to address this issue include balancing of the center of gravity (CoG) through attitude control [8,9], or static balancing through mechanical design of the manipulator mount mechanism [10,11]. Nevertheless, the experimental results for these approaches still show significant CoG variations and attitude destabilization during manipulator motion. In general, static balancing through controlling the arm to be aligned with the aerial robot does not guarantee that zero reaction forces and torques are transferred from the manipulator to the robot. The dynamic interaction between the manipulator and UAV in aerial manipulation has also been discussed in [12]. To solve this problem, we require minimization of the manipulator reaction torque disturbances on the base, while it moves to complete its task. This problem has been investigated in the domain of space robotic manipulators through various approaches [13, 14, 15, 16]. Of relevance for our application is the generalized Jacobian, which allows for manipulator joint-velocity level control with accurate end-effector trajectory tracking even with large base motions [17].