**5. Aerial manipulation**

A seven Degree-of -Freedom (DoF) robotic arm has been attached for exerting forces on surfaces in aerial manipulation tasks, such as grinding, cleaning or physical contact based inspection [46]. The Kinova Gen 2 Assistive 7DoF robot [47] was attached through a custom mount. This manipulator is characterized by a 2:1 weight to payload ratio, with the available payload at the end-effector being 1.2 kg grasped by the 3-finger gripper. Torque sensing is provided at each joint and these measurements along with the joint angles are communicated to the main computer at 100 Hz under ROS middleware.

For mounting the robot to the drone's payload attachment rods, a generic payload mount base was designed and manufactured. The base is firmly mounted to the drone's payload carrying rods utilizing quick attachment clamps. The construction material was selected to be T-6065 aluminum and features four 10 mm openings for attaching the payload. A second rigid base was similarly designed for attaching the robot's base to the generic payload mount base using 10 mm hex bolts. An exploded view of the entire mounting configuration can be visualized in **Figure 17**. The aerial manipulator is shown in **Figure 18**.

The indoor position hold scheme of Section 3.2 was expanded [48, 49] so as to utilize the manipulator in a surface ultra-sound scanning scenario. The surface is placed at 45° angle in an a-priori known position. After taking off the FCU retracts the landing gear (if commanded) and moves the manipulator to its joint angles [180, 90, 180, −30, 90]° respectively. On arrival to the desired setpoint pose, the manipulator's end tip comes into contact with the surface and the system hovers at the specified pose for some time for performing the area scan. The process is completed with the onboard computer initiating a landing after returning to the initial take-off position.

The described scheme is aimed for future use in the Abu Dhabi airport's Miedfiled Terminal [50], for scanning the integrity of critical structures such as facades and rooftop. **Figure 19** presents the hovering pose of the physical prototype

#### **Figure 17.**

*Universal mount of robotic manipulators on aerial platform.*

**53**

equal to 1 kHz.

*Development of a Versatile Modular Platform for Aerial Manipulators*

while scanning the surface, whilst the full video concept including moments of the

In this chapter the mechatronic aspects (hardware and software) of a heavy lift drone are presented. This drone can operate either indoors or outdoors in an autonomous manner. Equipped with spherical and PTZ cameras, the drone has environment perception capabilities and can collaborate with other drones. A robot manipulator is attached at the drone for physical interaction purposes. The ability to carry out the aforementioned tasks in an accurate and modular manner depicts the efficiency of the system for future robotic aerial applications of increased complexity. However, many challenges are yet to be examined. The authors' aim is to focus future research on autonomous navigation in confined environments as

In aerial manipulation, the challenge lies with the forces at the tip of a stiff 7-DoF manipulator being directly transferred to the main UAS frame. Additionally, their orientation can be varying, depending on the pose of the manipulator. Thus, the ability of the aerial manipulator to robustly maintain its position and attitude while performing the task is mandatory. Compared to the depicted experimentation of this book chapter the induced forces from such operation are calculated to be in the area of 10 to 100 N. Subsequently, although the existing position controller of the ArduCopter flight stack is able to withhold a proper pose while ultrasound scanning of inclined areas, advanced control techniques [49] will be utilized in the sequel. The authors intend to test the efficiency of the built-in attitude controller of the ArduCopter flight stack, as well as exploit the adaptive backstepping control strategies in [48, 49] and other (model predictive) control techniques. The implementation of such controllers relies on the ability to directly control the angular velocity of the drone's motors independently, at rates greater or

experiment is available through the link given in [51].

*Surface ultra-sound scanning utilizing aerial manipulation.*

well as high interaction forces aerial manipulation [52].

**6. Conclusions/discussion**

**Figure 19.**

*DOI: http://dx.doi.org/10.5772/intechopen.94027*

**Figure 18.** *Aerial manipulation system with PTZ-camera.*

*Development of a Versatile Modular Platform for Aerial Manipulators DOI: http://dx.doi.org/10.5772/intechopen.94027*

*Service Robotics*

initial take-off position.

For mounting the robot to the drone's payload attachment rods, a generic payload mount base was designed and manufactured. The base is firmly mounted to the drone's payload carrying rods utilizing quick attachment clamps. The construction material was selected to be T-6065 aluminum and features four 10 mm openings for attaching the payload. A second rigid base was similarly designed for attaching the robot's base to the generic payload mount base using 10 mm hex bolts. An exploded view of the entire mounting configuration can be visualized in

The indoor position hold scheme of Section 3.2 was expanded [48, 49] so as to utilize the manipulator in a surface ultra-sound scanning scenario. The surface is placed at 45° angle in an a-priori known position. After taking off the FCU retracts the landing gear (if commanded) and moves the manipulator to its joint angles [180, 90, 180, −30, 90]° respectively. On arrival to the desired setpoint pose, the manipulator's end tip comes into contact with the surface and the system hovers at the specified pose for some time for performing the area scan. The process is completed with the onboard computer initiating a landing after returning to the

The described scheme is aimed for future use in the Abu Dhabi airport's Miedfiled Terminal [50], for scanning the integrity of critical structures such as facades and rooftop. **Figure 19** presents the hovering pose of the physical prototype

**Figure 17**. The aerial manipulator is shown in **Figure 18**.

**52**

**Figure 18.**

**Figure 17.**

*Aerial manipulation system with PTZ-camera.*

*Universal mount of robotic manipulators on aerial platform.*

**Figure 19.** *Surface ultra-sound scanning utilizing aerial manipulation.*

while scanning the surface, whilst the full video concept including moments of the experiment is available through the link given in [51].

## **6. Conclusions/discussion**

In this chapter the mechatronic aspects (hardware and software) of a heavy lift drone are presented. This drone can operate either indoors or outdoors in an autonomous manner. Equipped with spherical and PTZ cameras, the drone has environment perception capabilities and can collaborate with other drones. A robot manipulator is attached at the drone for physical interaction purposes. The ability to carry out the aforementioned tasks in an accurate and modular manner depicts the efficiency of the system for future robotic aerial applications of increased complexity. However, many challenges are yet to be examined. The authors' aim is to focus future research on autonomous navigation in confined environments as well as high interaction forces aerial manipulation [52].

In aerial manipulation, the challenge lies with the forces at the tip of a stiff 7-DoF manipulator being directly transferred to the main UAS frame. Additionally, their orientation can be varying, depending on the pose of the manipulator. Thus, the ability of the aerial manipulator to robustly maintain its position and attitude while performing the task is mandatory. Compared to the depicted experimentation of this book chapter the induced forces from such operation are calculated to be in the area of 10 to 100 N. Subsequently, although the existing position controller of the ArduCopter flight stack is able to withhold a proper pose while ultrasound scanning of inclined areas, advanced control techniques [49] will be utilized in the sequel. The authors intend to test the efficiency of the built-in attitude controller of the ArduCopter flight stack, as well as exploit the adaptive backstepping control strategies in [48, 49] and other (model predictive) control techniques. The implementation of such controllers relies on the ability to directly control the angular velocity of the drone's motors independently, at rates greater or equal to 1 kHz.
