Recent Advancements in Autonomous Robots and Their Technical Analysis

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.


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][2][3]. ese 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. e researchers in this regard are opting for some hybrid-type control algorithms to improve the tracking performance [5][6][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. ese 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].
is 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. e idea for embedding the UUV with a manipulator is introduced many times, that is, [11,12]. is 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. ese underactuated systems are very hard to control.
us, 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][14][15][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.
is type of unmanned vehicle got a great boom because of its aggressive maneuverability [19][20][21][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]. e 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]. ese 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]. us, in literature, one may find several types of manipulators as discussed by [30][31][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). e 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. e 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. e 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. en, Section 6 provides technical analysis, and lastly, Section 7 concludes the whole study.

State of the Art
e 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. is 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. e 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. e 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.

Unmanned Underwater Vehicle Equipped with Manipulator Design
For repairing the structures, mostly in the offshore oil industry, these UUVs are highly recommended. is is because of their capability to reach in the depth of the sea unlike humans [37]. ese UUVs have been blessed with two main abilities, that is, position stalking and dynamic stalking. is 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. ese 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.

Dynamic Model of UUV.
e 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). e 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. is strategy is applied on six DOFbased 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. e position and orientation with respect to the inertial frame are given as In equation (1), μ 1 (t) and μ 2 (t) are the vectors that describe the position and angular velocities of UUVs. e column matrix μ 2 (t) is also known as the attitude of the vehicle.
where T 1 (t) represents the translational velocities whereas T 2 (t) represents the angular velocities. By combining the translational and angular velocities as in [38], we get e rotation matrix can be derived using Newton-Euler methods as where c x means cos(x) and s x means sin(x). e dynamic model for UUV is shown as follows: In the above set of equations, M(t) is the inertial matrix that comprised hydrodynamic mass change and functions (e.g., one can see (.) and h(.) εR 6x1 ). M(t) is the sum of centripetal mass and Coriolis body mass mentioned as follows [40]:

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

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. e kinematics for this gripper/manipulator is stated by [42] via opting for direct kinematics.
is 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.
us, the nth number of joints can be expressed as q ∈ R n , whereas the position of the end effector is expressed as n p ∈ R 3 , n 0 ∈ R 3 . 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), m i is an additional mass due to the manipulator and the weight of the object that must be grasped by the manipulator. Moreover, r i is the vectoral distance from the origin frame I towards the center of gravity of the link. e variable a i is the translational acceleration from the origin of the frame, whereas _ T 2 (t) is the vector denoting the change in angular velocities where T 2 (t) is the vector consisting of rotational velocities.

Performance Analysis of UUV Equipped with Manipulator.
ere is an effective need for an autonomous unmanned underwater vehicle due to several issues. e 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].
Using the Newton-Euler dynamic method shown in Figure 5, we can derive the equation of motions [40]. is method is easy but has some limitations such as gimbal lock due to singularity issues.
After going through the latest papers and current stateof-the-art approaches, Table 2 presents the previously proposed techniques for UUVs, the hardware they are applied on, and their limitations.

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. is method is frequently opted for by various researchers because of less complexity. e 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. ese 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]. e focus for the UAVs in this paper is set on quadrotor type of UAVs. is is because of a fewer number of actuators that result in less power consumption and long battery time for flight [44][45][46]. Researchers modified the quadrotor with multirotors as well as the manipulator designs too. ere 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. e 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].

Position
Linear and angular velocities Forces and moments Degree of freedom

Performance Analysis of UAV Equipped with
Manipulator. e paper discusses two categories of unmanned aerial vehicles. One is a cable-driven UAV and the second one is equipped with a manipulator design. e current approaches for the trajectory tracking of UAVs are discussed by [50,51], in which an adaptive robust control law is proposed. e 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]. e 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 MITrules. e 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.

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]. e 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. e 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 righthand side is the vector form of rigid quadrotor body. e term like f q (U) is the control input force and torque given as T q (U, q j A ) for quadrotor whereas the force exerted by manipulator is given as f A (zq j A ) and torque as T A (zq j A ). m is the total mass and I is an identity matrix of (3 × 3) order. I(q j A ) is the total inertia. Moreover, _ v and _ ω are angular velocities.

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]. e part of deploying sensors is the core part that guides the UGV in indoor/outdoor space. e 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].

Failure Analysis of Acquired Data and Its Usage.
e 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

Techniques
Applied on Limitations References Self-tuning fuzzy proportional integral derivative (PID) nonsingular fast terminal sliding mode control (STF-PID-NFTSM)

PUMA560 robotic manipulator
(1) e average convergence time is 3.5 seconds in the presence of uncertainties (2) e response experiences an undershoot in the presence of unmodeled uncertainties [7] Nonlinear disturbance observer-based sliding mode control law UUV with 2 DOF manipulator (1) e proposed UUV is a fully actuated system and thus consumes huge power and process time (2) It has a small working envelope and does not consider unmodeled dynamic factors (3) For estimating the sudden hydrodynamic coefficients, the strategy is complex [39] Bioinspired dolphin algorithm embedded with disturbance rejection scheme UUV with fins like a dolphin (1) e disturbance taken in the simulation is limited up to 0.01 sin(t) (2) Undersea there are so many other factors, that is, hydrodynamic coefficients which were assumed through ANSYS and computational fluid dynamics [11] Adaptive iterative approach with boundary layer and hyperbolic tangent function 2nd-order nonlinear system (1) Bounded conditions have been defined already (2) Control input somehow experiences the chattering-like noise [6] Underwater long-arm manipulator (ULAM) with an improved hydraulic driving system (SHDS) with fuzzy-based PID control UUV with long-arm gripper/manipulator (1) Either in low or in high pressure, there is an average tracking error of 5.12 percent (2) Each joint experiences an average overshoot of 1.5% and a steady-state error of 0.015 (3) Fuzzy-based PID slows down the maneuverability and increases hardware cost [43] e nonlinear model-based observer design using the linearization of the model to estimate the current state UUV Visor3 (1) Chattering effect is available along with some transient and steady-state issues (2) Hardware implementation will be costly as compared to other previously proposed UUVs [40] DexROV, an EC Horizon 2020-funded project UUV with TITAN4 from shilling robotics (1) e output response, that is, angular and translational velocities, experiences a chattering effect too with reasonable tracking error because of a long length of gripper and time delays [38] Hybrid high-order terminal sliding mode (HHOTSM) control approach for (MIMO) uncertain nonlinear systems Underwater robotic manipulator (1) First convergence time but output has a response which has some overshoots too (2) Chattering is still there due to switching mode b/w terminal SMC and higher-order SMC [16] Sliding mode control-based dynamic positioning system Applied on ship model in the deep sea (1) parameters were assumed for fully loaded and ballasted conditions and did not consider unmodeled dynamic factors (2) Responses, that is, surge, sway, and yaw, experience 12%-20% overshoots in simulations and experimental work, respectively [14] Supervisory feedforward artificial neural network-(ANN-) based fuzzy control law to address friction and elasticity issues of a manipulator Flexible joint-based manipulator design (1) ere is a reasonable error in load and the motor trajectories (2) e convergence rate on the time axis is not suitable for any sensitive pick and drop tasks (3) e technique is based on ANN and a fuzzy set of rules; it will only be implemented on fieldprogrammable gate array (FPGA) or digital signal processing (DSP) kits that lead us to an expensive hardware design [5] PID guidance and control laws to perform basic control tasks such as autoheading, autospeed, and straight line (2) Due to these time delays, the responses have tracking errors [13] 6 Mathematical Problems in Engineering (1) Sudden fluctuations are experienced due to variation in altitude (2) Sluggish in grasping the objects (need an improvement for fast grasp) [53] Adaptive time delay control (ATDC) scheme using a fractional-order nonsingular terminal sliding mode (FONTSM)

Cable-driven manipulators
(1) e tuning function for ATDC is a complex procedure (2) e root means square error for joint 01/02 is 0.3 and 0.32. 5 chattering appears at torque responses in the presences of payload [50] Continuous nonsingular fast terminal sliding mode (CNFTSM) control scheme using a modified supertwisting algorithm (STA) QUAV with a cabledriven manipulator (1) e response experiences chattering in experimental work (2) e software simulations also show some overshoots too (3) ere is also a steady-state error available for manipulator [52] A composite controller scheme using 02 subcontrol blocks (one for arm/gripper and second for QUAV), gain tuning method QUAV with a cabledriven manipulator (1) Gimbal lock due to Newton-Euler method (2) Due to cosine and sine terms, the computation time increases; therefore, an expensive DSP kit is proposed for the algorithm (3) e chattering phenomenon appears while real flight takes off and path planning [48] An adaptive terminal sliding mode controller for the trajectory tracking of robotic manipulators using radial basis function neural networks (RBFNNs)

Robotic manipulators
(1) In the presence of any disturbance factor, that is, wind disturbance, the robotic manipulator will be diverted from trajectory for some time and refollow the trajectory again [49] Combination of gain scheduling and Lyapunovbased model reference adaptive control (MRAC) UAV for manipulation (1) ere is undershoot and overshoot in yaw angular velocity (2) While performing pitch, the rotors experience oscillations for 10 seconds [47] Backstepping control design using an admittance subcontrol block for the manipulator design Octa-copter UAV with 07 DOF manipulator (1) Due to the change of the reference values, too fast several joints and their servos are not able to follow, and the response lags the reference for some time [26] Proportional integral derivative-(PID-) based wireless control of quadrotor at hovering state Quadrotor with a payload of 250 grams (1) e major shortcoming is the control using the Zigbee module which is sluggish and hence QUAV will never be able to perform aggressive maneuvers (2) At the payload of 250 grams, the QUAV cannot balance itself in a hovering state [46] Nested controller scheme for attitude stabilization, vision-based navigation, and guidance, with the aerial gripping QUAV with aerial gripping task (1) ere is an error in both hovering and path tracking states (i.e., the deviation in between ±13 cm). At outdoor operations, this error increases up to ±20 cm.
(2) During wind disturbance, the quadrotor deviates from the path for a few seconds and refollows the path again. [27] Direct approximate-adaptive control using cerebellar model arithmetic computer (CMAC) nonlinear approximators Quadrotor with multiple payload variation (1) ere must not be extreme variation in payload mass, and this will lead to bursting or instability because of the previously trained weights [45] Discrete proportional integral derivative control design QUAV with 4 g weight and 1 kg payload mass (1) After visualizing the pitch and roll response, there are drastic oscillations in the output response due to the ground effect [44] Mathematical Problems in Engineering 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.
e term "m" is the mass "g" 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]. 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.

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. is 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. e paper suggests a serious need for reviewing the adaptive control law and their amalgamation with state observer design. e 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. erefore, 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. is table shows the limitations of R1 R4 R3 R2 Figure 6: UAV with a manipulator.  (3) Without load, its RPMs are between 50 and 70. e approach may not be affordable for heavy payloads. e image processing approach is restricted to only color. It is studied that various approaches recommend tracking object with respect to color, shape, and histogram [56] Computer vision and Internet of things (IoT) control approach UGV with sharp rudder to clear sewerage pipelines (1) e response and work are fine but the time delay is experienced in the response due to the internet of things and the sensor's resolution factor [58] Novel hybrid additive-subtractive manufacturing (HASM)

DOF robotic manipulator
(1) Due to fast response, there is a time delay in system response (2) Sudden overshoots are also experienced [29] DTMF-based control law for robotic arm Robotic arm for a coconut tree climber (1) e simple dual tone multiple frequency-based control is proposed which may face serious distortions in the presence of any external noise.
(2) e approach is involved in cutting and climbing on coconut trees to pick and drop the coconuts. us, the minor error may destabilize the system and the cutter part may harm any human standing beneath (hazardous in nature). [59] European ICARUS project towards the development of unmanned search and rescue (SAR) robots UGV with large gripper (1) Focused on only rescue missions but there are some huge tracking errors while performing manipulation of the objects [28] An alternative and comprehensive mapgenerating algorithm UGV with 2 DOF gripper (1) e trajectories are fine with no error but in the absence of unmodeled factors 2 e output response for gripper experiences the steady-state error of 0.55% [31] e design and control of robotic search and rescue system based on an immunocontrol framework UGV with navigation systems (1) e communication is performed using a Zigbee module, which is sluggish and thus brings time delay in responses [30] CoMoRAT (configurable mobile robot for all-terrain applications) for the installation of variable payloads Modular UGV (1) e modular approach is presented that leads to the installation of additional components on UGV to make it affordable for variable load conditions (2) Expensive hardware approach [33]

Conclusion
is 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. e 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. e 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. ese 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
e data used to support the findings of this study are included within the article.

Conflicts of Interest
e author declares no conflicts of interest.