Recent Advancements in Autonomous Robots and Their Technical Analysis

Liu, Boni

doi:https://doi.org/10.1155/2021/6634773

Mathematical Problems in Engineering

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Abstract Introduction Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Special Issue

Recent Trends in Advanced Robotic Systems

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Review Article | Open Access

Volume 2021 | Article ID 6634773 | https://doi.org/10.1155/2021/6634773

Recent Advancements in Autonomous Robots and Their Technical Analysis

Boni Liu¹

Academic Editor: Bhawani Shankar Chowdhry

Received08 Dec 2020

Revised07 Jan 2021

Accepted19 Jan 2021

Published03 Feb 2021

Abstract

The purpose of this paper is to discuss and present a technical analysis of the recent advancements in autonomous robots equipped with a manipulator. The autonomous robots include unmanned aerial vehicle (UAV), unmanned underwater vehicle (UUV), and unmanned ground vehicle (UGV). A manipulator can make an autonomous robot more adaptable and robust but it can also affect its performance as well. Several issues can arise because of the installation of a manipulator like the robot becoming unstable due to the extra weight, slow convergence, and errors in the path planning. Therefore, this study presents the numerous recent techniques that are in use to counter the aforementioned problems. The methodology and approach used in this paper are to first present the dynamic model of the autonomous robot. Then, the study offers a performance analysis of the specific robot in question. Finally, the paper formulates the limitations of the recently proposed techniques in the form of a table for each vehicle. The key findings of this study are a comprehensive review of the aforesaid techniques and their technical analysis. The unique contribution of this study is to present some of the limitations that these methods have so the researcher can better select the method according to the mission requirement.

1. Introduction

For the past few years, the researchers are engaged in evaluating the performance of autonomous vehicles with the addition of manipulator design due to the emerging demand in executing the number of flexible tasks in any dull, dirty, difficult, or dangerous environment [1–3]. These manipulators provide easy access to perform several jobs with merely small inertia, high load to weight ratio, and smart flexible structure [4]. For complex dynamic models with time delays in output variables and unmodeled dynamic factors, high-performance tracking has been observed as still one of the challenging tasks.

Acquiring the real and precise dynamics of the system during control design is among the complicated and strenuous activities of the procedure. The researchers in this regard are opting for some hybrid-type control algorithms to improve the tracking performance [5–7]. One may design such control designs, but they require the tuning of several parameters. In short, one researcher has two tough approaches either to acquire an exact mathematical dynamic model of autonomous vehicles or to estimate the numerous parameters for control design to produce refined input logic for the proposed system. Before going through the literature review, one should understand the types of autonomous unmanned vehicles. These vehicles are autonomous because of their ability to perform any sort of task without any intervention of human beings. Figure 1(a) shows a UUV with a manipulator [8], Figure 1(b) shows a UAV with a manipulator [9], and Figure 1(c) presents a UGV with a manipulator [10].

(a)

(b)

(c)

This entire review paper discusses the four types of unmanned vehicles embedded with gripper, that is, unmanned underwater vehicle (UUV), underactuated quadrotor unmanned aerial vehicle (QUAV), unmanned ground vehicle (UGV), and last but not least unmanned air-cushion vehicle (UACV). In addition to this, the paper addresses the constraints such as the occurrence of time delays and exogenous disturbances in a system.

The idea for embedding the UUV with a manipulator is introduced many times, that is, [11, 12]. This is because of enabling an ability to grasp the target in water. Most of the UUVs have six degrees of freedom but only four actuators which make them underactuated system. These underactuated systems are very hard to control. Thus, for the stabilization, various hybrid control designs are introduced by researchers. In the catalog of such controllers, one may see model reference adaptive control (MRAC), sliding mode control (SMC), and many other robust control strategies [13–16].

Since the last decade, the extensive use of UAVs has been observed in various fields, either for commercial purposes, that is, surveillance [17, 18], or for military-oriented tasks. This type of unmanned vehicle got a great boom because of its aggressive maneuverability [19–22] over a long field of distance. Researchers have also tried to embed smart manipulator/gripper mechanism [23, 24], with UAV in order to increase the utility of drones in multiple fields. Researchers previously proposed commonly 01 and 02 DOF-based manipulators with unmanned aerial vehicles, that is, quadrotor [25]. Researchers were engaged initially in optimizing the control performance for the control law associated with the above manipulators [26]. The researchers also proposed some advanced mechanical designs and typical construction of quadrotor embedded with grippers of lightweight but with great capability to grasp the object within the working envelop [27].

Researchers also embedded some smart manipulators on such UGVs, that is, [28]. These smart manipulators have increased the manipulating ability to move up to 250 kg mass from one point to another. It is believed that a UGV must have good speed and navigation systems to monitor and manipulate the objects within harsh terrain [29]. Thus, in literature, one may find several types of manipulators as discussed by [30–32]. In most cases, it is recommended to use servo motors for ideal torque and mass ratio. In addition to this, a servo motor can be controlled easily. In today’s era, researchers proposed different microcontrollers for experimental design, that is, Raspberry Pi [30], Arduino [33], or any modular programmable logical controller (PLC).

The motivation behind this paper was to collate the research studies about autonomous robots in one place so that new authors and researchers can easily compare the benefits and limitations of each study and pick the one most optimal for their mission requirement.

The main contributions of the paper are to provide one comprehensive review and technical analysis of the old and new studies about the UUVs, UAVs, and UGVs, to shed light on the limitations of the aforementioned studies in the form of an easily accessible table.

The paper is arranged as follows: Section 2 presents some cutting-edge research into autonomous robots. Section 3 discusses the UUV with the manipulator, its dynamic model, and its performance analysis and finally sums up the limitations of the previous techniques in a table. Similarly, Section 4 deals with the UAV, and Section 5 handles the UGV. Then, Section 6 provides technical analysis, and lastly, Section 7 concludes the whole study.

2. State of the Art

The state-of-the-art approach for UUV is discussed in [34] where a nonlinear observer-based model is amalgamated with dual proportional integral derivative (Dual-PID) design. This research provides comparatively effective results for 06 degrees of freedom (DOF) UUV with 02 DOF manipulator.

Researchers in [35] present a state-of-the-art technique for UAVs using 5G networks in a smart city. The researchers use blockchain-based solutions to secure these 5G networks for industrial and defense purposes.

Academics in [36] offer a novel idea of integrating UGV and UAV for construction site data collection. The UGV is autonomous and travels using the help of its sensors and the UAV which alerts it of any danger not visible to UGV on the ground.

3. Unmanned Underwater Vehicle Equipped with Manipulator Design

For repairing the structures, mostly in the offshore oil industry, these UUVs are highly recommended. This is because of their capability to reach in the depth of the sea unlike humans [37]. These UUVs have been blessed with two main abilities, that is, position stalking and dynamic stalking. This means that a UUV can maintain all positions throughout time with respect to the body.

One should not forget about the underwater dynamics that can lead to huge turbulences. These underwater dynamic factors are hydrodynamic coefficients and the mass flow rate through water inlets [11].

Discussing the previous works related to UUV, in [14], the author addressed the behavior by using SMC. An extended version, that is, higher-order SMC (HOSMC) can also be seen in [16] where the chattering phenomena (high number of oscillations) were reduced using higher order of SMC [13]. In some of the research works, one may see the use of a dual control scheme, that is, using proportional derivative (PD) and proportional integral control (PID) controller to stabilize the underactuated dynamics of UUV. Since this PID and PD, dual scheme produces fine results but in the presence of nonlinearities, this will never hold up the response for so long and shall lead it towards instability.

Researchers [13] proposed multivariable sliding mode control for the stabilization of attitude and position of a UUV equipped with a manipulator. In this case, the proposed UUV is a fully actuated system (number of control inputs are equal to degrees of freedom) that is why it can grasp any object underwater easily but simultaneously the power consumption by the actuators is huge as compared to underactuated UUVs.

3.1. Dynamic Model of UUV

The dynamic model for UUV is achieved after going through the study of both frames of references, that is, Earth frame of reference (inertial frame) and fixed body frame (noninertial frame). The common design is comprised of six actuators that lead to six DOF easily [38]. In this subsection, the main idea related to the state-of-the-art approach is discussed.

Here, the Dual-PID control techniques are fused with the nonlinear model-based observer to stabilize the fully actuated underwater vehicle. This strategy is applied on six DOF-based UUVs embedded with a gripper/manipulator of two DOF; this makes in total eight DOF to control. Figure 2 shows a six DOF UUV physical model embedded with a manipulator [39]. Table 1 presents the orientation, translational, and angular velocities, forces, and moments along with the degrees of freedom for the UUV.

The position and orientation with respect to the inertial frame are given as

In equation (1), and are the vectors that describe the position and angular velocities of UUVs. The column matrix is also known as the attitude of the vehicle.where represents the translational velocities whereas represents the angular velocities. By combining the translational and angular velocities as in [38], we get

The rotation matrix can be derived using Newton–Euler methods aswhere means and means . The dynamic model for UUV is shown as follows:

In the above set of equations, is the inertial matrix that comprised hydrodynamic mass change and functions (e.g., one can see ). is the sum of centripetal mass and Coriolis body mass mentioned as follows [40]:

3.2. Kinematics of UUV

As per the conventional study by [41], the kinematic set of equations are given as in equations (7) and (8):

3.3. Modeling of Manipulator Design

For modeling the manipulator design, one should consider the moments of the arm as an external torque. Since the attached manipulators connected with UUV are based on two links and one joint mostly using a simple servo motor. The kinematics for this gripper/manipulator is stated by [42] via opting for direct kinematics. This method helps to compute the orientation by finding the nth number of joints and compute the position of the end effector to grasp the object correctly.

Thus, the nth number of joints can be expressed as , whereas the position of the end effector is expressed as . One can now develop a relationship between position and orientation easily as provided as follows:

Researchers have used the Denavit–Hartenberg (DH) formulation to find the configuration of an end effector of the gripper. Moreover, the dynamics of UUV stated that the total forces and torque that are acting on the body of UUV in the deep sea can be expressed in generic as

In equation (10), is an additional mass due to the manipulator and the weight of the object that must be grasped by the manipulator. Moreover, is the vectoral distance from the origin frame I towards the center of gravity of the link. The variable is the translational acceleration from the origin of the frame, whereas is the vector denoting the change in angular velocities where is the vector consisting of rotational velocities.

3.4. Performance Analysis of UUV Equipped with Manipulator

There is an effective need for an autonomous unmanned underwater vehicle due to several issues. The important thing at this moment is to save the lives of our divers and get efficient results by using UUVs beneath the sea more than the depth covered by divers. Figure 3 shows a UUV equipped with a manipulator that has multiple links and joints to grasp the object [38].

Researchers also used the bioinspired dolphin algorithm for controlling the locomotion of UUVs like a real dolphin. Figure 4 presents a UUV hardware design based on a bioinspired dolphin algorithm [11].

(a)

(b)

Using the Newton–Euler dynamic method shown in Figure 5, we can derive the equation of motions [40]. This method is easy but has some limitations such as gimbal lock due to singularity issues.

After going through the latest papers and current state-of-the-art approaches, Table 2 presents the previously proposed techniques for UUVs, the hardware they are applied on, and their limitations.

4. Underactuated Quadrotor UAV Equipped with Manipulator Design

Like underwater vehicles, unmanned aerial vehicle and its dynamic model are also derived from the Newton–Euler method. This method is frequently opted for by various researchers because of less complexity. The only limitation of this approach is the gimbal lock due to the singularity issue which can be reduced, not eliminated completely through hyperbolic tangent function. These equations involve the trigonometric functions; hence, the computation time for these terms is usually huge, and therefore expensive programmable controller is selected which leads to an expensive hardware design [44].

The focus for the UAVs in this paper is set on quadrotor type of UAVs. This is because of a fewer number of actuators that result in less power consumption and long battery time for flight [44–46]. Researchers modified the quadrotor with multirotors as well as the manipulator designs too. There are also some hybrid control schemes too, previously proposed by [47, 48], for the stabilization of the entire behavior of UAV with a gripper mechanism. The same control laws that were proposed before for UAV are proposed here too such as model reference adaptive control, a hybridized version with sliding mode control for quadrotor UAV (QUAV) equipped with 2 DOF manipulator [49].

4.1. Performance Analysis of UAV Equipped with Manipulator

The paper discusses two categories of unmanned aerial vehicles. One is a cable-driven UAV and the second one is equipped with a manipulator design. The current approaches for the trajectory tracking of UAVs are discussed by [50, 51], in which an adaptive robust control law is proposed. The lumped dynamics are estimated using estimator design and in addition to this, the chattering phenomenon is also eliminated [52]. For unknown modeled factors, one can see several such hybrid techniques. Researchers have also proposed subcontrol blocks, that is, time delay estimator, supertwisting law, and fractional-order SMC technique to minimize the dynamic error in the system. Researchers in this way obtained 20% efficiency for quadrotor equipped with 2 DOF manipulator on a referred path [53]. The hybridized version of the regulation, pole-placement, and tracking (RST) control design with MRAC and the stability for tracking and grabbing the objects both are proved using MIT rules. The work proposed by [53], where quadrotor varies its mass from 0.5 kg to 5.0 kg, and the manipulation related parameters are achieved using the Denavit–Hartenberg principle. Table 3 presents the previously proposed techniques for quadrotor UAVs, the hardware they are applied on, and their limitations.

4.2. Dynamic Model of QUAV with 2 DOF Gripper

Researchers avoided the different parameters, that is, aerodynamic effect, ground effects, and flapping of blades, and proposed an overall dynamic model. Figure 6 shows the overall model of a dynamic quadrotor with a 2 DOF manipulator [53, 54]. The Newton–Euler method is the most frequent method used and stated by the majority of the people. One can find the separate models as well like in [54] but this will increase the complexity.

The overall mathematical model including the gripper dynamics is derived and stated as

In equation (11), the left-hand side is the dynamics of quadrotor and manipulator, respectively, whereas the right-hand side is the vector form of rigid quadrotor body. The term like is the control input force and torque given as for quadrotor whereas the force exerted by manipulator is given as and torque as . is the total mass and is an identity matrix of (3 × 3) order. is the total inertia. Moreover, and are angular velocities.

5. Unmanned Ground Vehicle Equipped with Manipulator Design

5.1. Dynamic Model of UGV with Manipulator Designs

As discussed, these unmanned vehicles are deployed in such tasks that are far away from human management. UGVs are among the prominent vehicles that are used for surveillance purposes and can tackle high-risk crises [28]. The part of deploying sensors is the core part that guides the UGV in indoor/outdoor space.

The main motive of proposing the UGV equipped with manipulator design is to navigate it in a space with a specific trajectory tracking and manipulating the objects to mentioned coordinates. Generally, two approaches are commonly adapted with control designs such as mentioned as follows: understanding of failure modes [55].

5.2. Failure Analysis of Acquired Data and Its Usage

The researchers opted for the Newton–Euler method most frequently for UGVs like UUVs and UAVs. Here, the robotic manipulator and the payload duly manipulated are driven using a free diagram as shown in Figure 7 [56]where “T” is the torque, “F” is the force in Newton, and “L” is the perpendicular distance between the point of rotation and applied force.

The term “m” is the mass “” which is the gravitation acceleration; hence, with equation (13), equation (12) can be transformed as

Researchers have proposed various dynamic models for UGV and its manipulators such as in [57]. Figure 8 presents an example of a UGV [31] and a manipulator design [56].

(a)

(b)

Table 4 presents the previously proposed techniques for UGVs, the hardware they are applied on, and their limitations. Moreover, Table 5 summarizes Tables 2–4 into one easily comprehensible table to further elucidate the shortcomings of all the techniques used for an unmanned vehicle.

6. Technical Analysis and Discussion

If someone is working on UUV, then one should work on the constraints, that is, chattering effect, producing cost-effective hardware design, minimizing the power consumption, and process time. This manuscript provides an opportunity to evaluate either robust or adaptive control laws with nonlinear observer designs.

Table 2 presents the previously proposed techniques for UUVs, the hardware they are applied on, and their limitations. It is a helpful guide for any future researchers to choose the best strategy according to their mission requirements.

For quadrotor UAV, one must concern the issues such as the elimination of gimbal lock, chattering noise, and some serious undershoots/overshoots due to unmodeled dynamic factors. The paper suggests a serious need for reviewing the adaptive control law and their amalgamation with state observer design. The emerging bioinspired algorithms such as the pigeon algorithm are recommended while designing the observer design.

Table 3 presents the previously proposed techniques for quadrotor UAVs, the hardware they are applied on, and their limitations. Any future academic researching this field would find this table useful for deciding the best technique for their study.

For UGV, the processing time and hardware designs are emerging issues, and hence paper reviewed some of the fuzzy logic-oriented designs which produce fine response outcomes but are slower due to the fuzzy inference system. Therefore, it is suggested to use a single dimension-based fuzzy logic controller as they minimize the processing time. Once the processing time will be reduced, then a hardware designer may opt for a cheap microcontroller for programming.

Table 4 presents the previously proposed techniques for UGVs, the hardware they are applied on, and their limitations. It delineates the drawbacks of the mentioned strategies and would help in selecting the best method for the UGV.

After going through several research papers, Table 5 has been stated in this paper. This table shows the limitations of all previously proposed control laws over the unmanned vehicles embedded with several manipulator types.

7. Conclusion

This review paper presents a detailed review of the current state-of-the-art approaches and control laws proposed already for three types of unmanned vehicles, that is, UUVs, UAVs (more specifically quadrotors), and UGVs. The manuscript comes up with the limitations in Table 5. By reading Tables 2 to 5, one may see the most frequent problems in such unmanned vehicles, especially when embedded with manipulator design. The control laws so far proposed are fine until the degree of freedom for the manipulator is 02.

If the DOF value increases, the tracking performance also degrades and one may experience the chattering noise and deviation from tracking for some time and refollow the path. In addition to this, there is also degradation in transient and steady-state performances, that is, steady-state error and slow convergence. These vehicles are designed for fast maneuvers and aggressive operations with greater reliability but with these constraints, these unmanned vehicles compromise on their overall performance.

For future research ideas, one could revisit the robust and adaptive control laws with an amalgamation of bioinspired algorithms and smart observer designs to manage these problems. Also, the current team is planning to evaluate the bioinspired algorithms for individual unmanned vehicle types and may come up with another review.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The author declares no conflicts of interest.

Acknowledgments

This paper was supported by the Science Research Fund of Xi’an Aeronautics University (Grant no. 2019KY0208).

References

N. U. Rehman, K. Kumar, and G. E. M. Abro, “Implementation of an autonomous path planning & obstacle avoidance UGV using SLAM,” in Proceedings of the 2018 International Conference on Engineering and Emerging Technologies, ICEET 2018, pp. 1–5, Lahore, Pakistan, January 2018.
View at: Publisher Site | Google Scholar
J. Sánchez-García, “A survey on unmanned aerial and aquatic vehicle multi-hop networks: wireless communications, evaluation tools and applications,” Computer Communications, vol. 119, pp. 43–65, 2018.
View at: Publisher Site | Google Scholar
G. E. M. Abro, B. Jabeen, and A. Manan, “Stabilization of non-holonomic 03 dof hovercraft using robust RST control design,” in Proceedings of the Sukkur IBA Journal of Emerging Technologies, vol. 2, no. 1, pp. 45–50, Sukkur, Pakistan, August 2019.
View at: Google Scholar
Y. Wang, S. Jiang, B. Chen, and H. Wu, “A new continuous fractional-order nonsingular terminal sliding mode control for cable-driven manipulators,” Advances in Engineering Software, vol. 119, no. January, pp. 21–29, 2018.
View at: Publisher Site | Google Scholar
H. Chaoui, P. Sicard, and W. Gueaieb, “ANN-based adaptive control of robotic manipulators with friction and joint elasticity,” IEEE Transactions on Industrial Electronics, vol. 56, no. 8, pp. 3174–3187, 2009.
View at: Publisher Site | Google Scholar
J. Wei, “Adaptive iterative learning control of a class of nonlinear time-delay systems with unknown backlash-like hysteresis input and control direction,” ISA Transactions, vol. 70, pp. 79–92, 2017.
View at: Publisher Site | Google Scholar
M. Van, X. P. Do, and M. Mavrovouniotis, “Self-tuning fuzzy PID-nonsingular fast terminal sliding mode control for robust fault tolerant control of robot manipulators,” ISA Transactions, vol. 96, pp. 60–68, 2020.
View at: Publisher Site | Google Scholar
C. Shen, B. Buckham, and Y. Shi, “Modified C/GMRES algorithm for fast nonlinear model predictive tracking control of AUVs,” IEEE Transactions on Control Systems Technology, vol. 25, no. 5, pp. 1896–1904, 2016.
View at: Google Scholar
M. J. Kim, K. Kondak, and C. Ott, “A stabilizing controller for regulation of UAV with manipulator,” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 1719–1726, 2018.
View at: Publisher Site | Google Scholar
J. Carius, M. Wermelinger, B. Rajasekaran, K. Holtmann, and M. Hutter, “Deployment of an autonomous mobile manipulator at MBZIRC,” Journal of Field Robotics, vol. 35, no. 8, pp. 1342–1357, 2018.
View at: Publisher Site | Google Scholar
Z. Wu, J. Yu, J. Yuan, and M. Tan, “Towards a gliding robotic dolphin: design, modeling, and experiments,” IEEE/ASME Transactions on Mechatronics, vol. 24, no. 1, pp. 260–270, 2019.
View at: Publisher Site | Google Scholar
C. Laschi, B. Mazzolai, and M. Cianchetti, “Soft robotics: technologies and systems pushing the boundaries of robot abilities,” Science Robotics, vol. 1, no. 1, 2016.
View at: Publisher Site | Google Scholar
M. Caccia, M. Bibuli, R. Bono, and G. Bruzzone, “Basic navigation, guidance and control of an unmanned surface vehicle,” Autonomous Robots, vol. 25, no. 4, pp. 349–365, 2008.
View at: Publisher Site | Google Scholar
E. A. Tannuri, “Dynamic positioning systems: an experimental analysis of sliding mode control,” Control Engineering Practice, vol. 18, no. 10, pp. 1121–1132, 2010.
View at: Publisher Site | Google Scholar
F. A. Azis, M. S. M. Aras, M. Z. A. Rashid, M. N. Othman, and S. S. Abdullah, “Problem identification for underwater remotely operated vehicle (rov): a case study,” Procedia Engineering, vol. 41, pp. 554–560, 2012.
View at: Publisher Site | Google Scholar
X.-T. Tran and H.-J. Kang, “Adaptive hybrid High-Order terminal sliding mode control of MIMO uncertain nonlinear systems and its application to robot manipulators,” International Journal of Precision Engineering and Manufacturing, vol. 16, no. 2, pp. 255–266, 2015.
View at: Publisher Site | Google Scholar
J. Lee, D. H. Shim, S. Cho et al., “A mission management system for complex aerial logistics by multiple unmanned aerial vehicles in MBZIRC 2017,” Journal of Field Robotics, vol. 36, no. 5, pp. 919–939, 2019.
View at: Publisher Site | Google Scholar
L. Li, Y. Zhang, M. Ripperger, J. Nicho, M. Veeraraghavan, and A. Fumagalli, “Autonomous object pick-and-sort procedure for industrial robotics application,” International Journal of Semantic Computing, vol. 13, no. 2, pp. 161–183, 2019.
View at: Publisher Site | Google Scholar
N. Ammour, H. Alhichri, Y. Bazi, B. Benjdira, N. Alajlan, and M. Zuair, “Deep learning approach for car detection in UAV imagery,” Remote Sensing, vol. 9, no. 4, p. 312, 2017.
View at: Publisher Site | Google Scholar
M. Hassanalian and A. Abdelkefi, “Classifications, applications, and design challenges of drones: a review,” Progress in Aerospace Sciences, vol. 91, pp. 99–131, 2017.
View at: Publisher Site | Google Scholar
M. Silvagni, “Multipurpose UAV for search and rescue operations in mountain avalanche events,” Geomatics, Natural Hazards and Risk, vol. 8, no. 1, pp. 18–33, 2017.
View at: Publisher Site | Google Scholar
G. Ding, Q. Wu, L. Zhang, Y. Lin, T. A. Tsiftsis, and Y.-D. Yao, “An amateur drone surveillance system based on the cognitive internet of things,” IEEE Communications Magazine, vol. 56, no. 1, pp. 29–35, 2018.
View at: Publisher Site | Google Scholar
H. Bonyan Khamseh, F. Janabi-Sharifi, and A. Abdessameud, “Aerial manipulation—a literature survey,” Robotics and Autonomous Systems, vol. 107, pp. 221–235, 2018.
View at: Publisher Site | Google Scholar
X. Ding, P. Guo, K. Xu, and Y. Yu, “A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems,” Chinese Journal of Aeronautics, vol. 32, no. 1, pp. 200–214, 2019.
View at: Publisher Site | Google Scholar
B. W. Davis, J. T. Diep, and S. Jose, (12) United States Patent, vol. 2, no. 12, Moderna, Cambridge, MA, USA, 2017.
G. Heredia, “Control of a multirotor outdoor aerial manipulator,” in Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pp. 3417–3422, Chicago, IL, USA, September 2014.
View at: Publisher Site | Google Scholar
V. Ghadiok, J. Goldin, and W. Ren, “On the design and development of attitude stabilization, vision-based navigation, and aerial gripping for a low-cost quadrotor,” Autonomous Robots, vol. 33, no. 1-2, pp. 41–68, 2012.
View at: Publisher Site | Google Scholar
D. Doroftei, A. Matos, and G. de Cubber, “Designing search and rescue robots towards realistic user requirements,” Applied Mechanics and Materials, vol. 658, pp. 612–617, 2014.
View at: Publisher Site | Google Scholar
L. Li, A. Haghighi, and Y. Yang, “A novel 6-axis hybrid additive-subtractive manufacturing process: design and case studies,” Journal of Manufacturing Processes, vol. 33, no. March, pp. 150–160, 2018.
View at: Publisher Site | Google Scholar
A. W. Y. Ko and H. Y. K. Lau, “Intelligent robot-assisted humanitarian search and rescue system,” International Journal of Advanced Robotic Systems, vol. 6, no. 2, pp. 12–128, 2009.
View at: Publisher Site | Google Scholar
J. Suthakorn, “On the design and development of a rough terrain robot for rescue missions,” in Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics, ROBIO 2008, pp. 1830–1835, Chicago, IL, USA, September 2001.
View at: Publisher Site | Google Scholar
J. M. Ed and D. Hutchison, “Autonomous vehicles,” Auto Tech Review, vol. 64, 2012.
View at: Publisher Site | Google Scholar
G. Bayar, A. B. Koku, and E. I. Konukseven, “CoMoRAT: a configurable all terrain mobile robot,” in Proceedings of the 11th Wseas International Conference on Automatic Control, Modelling and Simulation, pp. 507–512, Istanbul, Turkey, October 2009.
View at: Google Scholar
Z. A. Ali, X. Li, and M. A. Tanveer, “Controlling and stabilizing the position of remotely operated underwater vehicle equipped with a gripper,” Wireless Personal Communications, vol. 116, 2019.
View at: Publisher Site | Google Scholar
P. Mehta, R. Gupta, and S. Tanwar, “Blockchain envisioned UAV networks: challenges, solutions, and comparisons,” Computer Communications, vol. 151, pp. 518–538, 2020.
View at: Publisher Site | Google Scholar
K. Asadi, A. Kalkunte Suresh, A. Ender et al., “An integrated UGV-UAV system for construction site data collection,” Automation in Construction, vol. 112, p. 103068, 2020.
View at: Publisher Site | Google Scholar
P. I. Macreadie, D. L. McLean, P. G. Thomson et al., “Eyes in the sea: unlocking the mysteries of the ocean using industrial, remotely operated vehicles (ROVs),” Science of the Total Environment, vol. 634, pp. 1077–1091, 2018.
View at: Publisher Site | Google Scholar
P. A. Di Lillo, E. Simetti, D. De Palma et al., “Advanced ROV autonomy for efficient remote control in the DexROV project,” Marine Technology Society Journal, vol. 50, no. 4, pp. 67–80, 2016.
View at: Publisher Site | Google Scholar
N. R. Sinatra, B. Clark, D. M. Vogt, K. Kit Parker, D. F. Gruber, and R. J. Wood, “Ultragentle manipulation of delicate structures using a soft robotic gripper,” Science Robotics, vol. 4, p. 33, 2019.
View at: Publisher Site | Google Scholar
S. Rúa and R. E. Vásquez, “Development of a low-level control system for the ROV Visor3,” International Journal of Navigation and Observation, vol. 2016, Article ID 8029124, 12 pages, 2016.
View at: Publisher Site | Google Scholar
A. J. Healey, “Model-based maneuvering controls for autonomous underwater vehicles,” Journal of Dynamic Systems, Measurement, and Control, vol. 114, no. 4, pp. 614–622, 1992.
View at: Publisher Site | Google Scholar
G. Antonelli, “Underwater robots fourth edition,” 2018, http://www.springer.com/series/5208.
View at: Google Scholar
C. Yang, Y. Wang, and F. Yao, “Driving performance of underwater long-arm hydraulic manipulator system for small autonomous underwater vehicle and its positioning accuracy,” International Journal of Advanced Robotic Systems, vol. 14, no. 6, pp. 172988141774710–172988141774718, 2017.
View at: Publisher Site | Google Scholar
P. Pounds, R. Mahony, and P. Corke, “Modelling and control of a large quadrotor robot,” Control Engineering Practice, vol. 18, no. 7, Elsevier, 2010.
View at: Publisher Site | Google Scholar
C. Nicol, C. J. B. MacNab, and A. Ramirez-Serrano, “Robust adaptive control of a quadrotor helicopter,” Mechatronics, vol. 21, no. 6, Elsevier, 2011.
View at: Publisher Site | Google Scholar
D. Hanafi, M. Qetkeaw, R. Ghazali, M. N. M. Than, W. M. Utomo, and R. Omar, “Simple GUI wireless controller of quadcopter,” International Journal of Communications, Network and System Sciences, vol. 6, no. 1, pp. 52–59, 2013.
View at: Publisher Site | Google Scholar
M. Orsag, C. M. Korpela, S. Bogdan, and P. Y. Oh, “Hybrid adaptive control for aerial manipulation,” Journal of Intelligent & Robotic Systems, vol. 73, no. 1–4, pp. 693–707, 2014.
View at: Publisher Site | Google Scholar
Y. Yu, X. Ding, and J. J. Zhu, “Dynamic modeling and control for aerial arm-operating of a multi-propeller multifunction aerial robot,” Advanced Robotics, vol. 31, no. 13, pp. 665–679, 2017.
View at: Publisher Site | Google Scholar
M.-D. Tran and H.-J. Kang, “A novel adaptive finite-time tracking control for robotic manipulators using nonsingular terminal sliding mode and RBF neural networks,” International Journal of Precision Engineering and Manufacturing, vol. 17, no. 7, pp. 863–870, 2016.
View at: Publisher Site | Google Scholar
Y. Wang, F. Yan, J. Chen, F. Ju, and B. Chen, “A new adaptive time-delay control scheme for cable-driven manipulators,” IEEE Transactions on Industrial Informatics, vol. 15, no. 6, pp. 3469–3481, 2019.
View at: Publisher Site | Google Scholar
Y. Wang, L. Liu, D. Wang, F. Ju, and B. Chen, “Time-delay control using a novel nonlinear adaptive law for accurate trajectory tracking of cable-driven robots,” IEEE Transactions on Industrial Informatics, vol. 16, no. 8, pp. 5234–5243, 2020.
View at: Publisher Site | Google Scholar
Y. Wang, F. Yan, J. Chen, and B. Chen, “Continuous nonsingular fast terminal sliding mode control of cable-driven manipulators with super-twisting algorithm,” IEEE Access, vol. 6, pp. 49626–49636, 2018.
View at: Publisher Site | Google Scholar
Z. A. Ali and X. Li, “Controlling of an under-actuated quadrotor UAV equipped with a manipulator,” IEEE Access, vol. 8, pp. 34664–34674, 2020.
View at: Publisher Site | Google Scholar
M. Orsag, C. Michael Korpela, S. Bogdan, and P. Y. Oh, “Hybrid adaptive control for aerial manipulation,” Journal of Intelligent & Robotic Systems, vol. 73, no. 1–4, pp. 693–707, 2014.
View at: Publisher Site | Google Scholar
M. Ahsan, “Modification of a toy helicopter into a highly cost effective, semi-autonomous, reconnaissance unmanned aerial vehicle,” in Proceedings of the 2012 International Conference on Robotics and Artificial Intelligence, ICRAI 2012, pp. 49–54, IEEE, Rawalpindi, Pakistan, October 2012.
View at: Publisher Site | Google Scholar
U. F. Ahmed, S ur-Rehman, Z. Hameed, F. Ahmed, T. A. U’Chong, and M. M. A. Asif, “Design and development of control system for unmanned ground vehicle and its manipulator,” in Proceedings of the 2020 International Conference on Engineering and Emerging Technologies (ICEET), pp. 1–8, Lahore, Pakistan, February 2020.
View at: Publisher Site | Google Scholar
M. Mustafa, Modeling and Dynamic Control of Autonomous Ground Mobile Manipulators, University of Calgary, Calgary, Canada, 2016.
G. M. Abro, B. Jabeen, Ajodhia et al., “Designing smart sewerbot for the identification of sewer defects and blockages,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 2, pp. 615–619, 2019.
View at: Publisher Site | Google Scholar
R. Jabeen, T. Pathmakumar, T. Venugopal, G. Maruthiyodan, and A. Philip, “DTMF based robotic arm design and control for robotic coconut tree climber,” in Proceedings of the 2015 International Conference on Computer, Communication and Control (IC4), Indore, India, September 2015.
View at: Publisher Site | Google Scholar

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Copyright © 2021 Boni Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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