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Table 3 Algorithms for UUV full-coverage path planning
Algorithms Logic Benefits Drawbacks
(1) No need of initial
Random Coverage Traverse the operating area with multiple vehi- environmental information (1) Not complete full-coverage
Strategy [65] cles following the random coverage strategy (2) Collision avoidance (2) High repetition

(1) Only work for narrow paths
(2) Complete full-coverage
(1) Build the map based on sensor information cannot be realized in conditions
(2) Apply the diagram algorithms for modeling (1) Complete full-coverage of broad area
Sensor-based map building and initialize a full-coverage path by (2) Consider multi-vehicle (3) Lack of robustness
method [66–69] dividing the path collaboration (4) Lack of optimal multi-vehicle
into sections accordingly
task assignment
(5) High repetition
(1) Complete full-coverage;
(2) Collision avoidance due
(1) Low adaptiveness to the
to self-regulation;
Intelligent method-based full Apply intelligent methods such as GA or NN for (3) High efficiency of dynamic environment (GA)
coverage path planning [71–73] each single vehicle path planning (2) Large computation (NN)
shortest covering time
and lowest energy cost
(1) Easy to implement
Probabilistic priority-based Plan the path due to the predefined probabilis- (2) Complete full coverage Not adaptive to dynamic en-
full coverage path tic priority (3) Increasing efficiency vironment
planning [74–77]

3. TECHNOLOGIES OF UUV TRACKING CONTROL
Due to the complex environmental factors of the deep-water space, such as the high pressure, invisibility, or
unpredictable obstacles, UUVs are applied in most cases when operating underwater to guarantee the safety
and efficiency [2,3,78] . Therefore, achieving the robustness and accuracy of controlling the UUV to track the
desired trajectory is dramatically important for completing the real-time underwater navigation [79,80] . As
mentioned in the Introduction, UUVs are mainly divided into ROV and AUV. ROV can be directly controlled
through a control model for propagation, Robot operating system (ROS) modules, a visual processing pipeline,
and a dashboard interface for the end-user, where the user gives commands remotely step by step [81] . This is
known as remote control, and the ROV is controlled manually in this case, which is not the critical point of the
section as the manual control strategy is direct and simple. For AUVs, the control is realized in an autonomous
way, meaning the AUV has to recognize the surrounding areas and make the decision itself. Moreover, some
ROVs also support the autonomous mode as a AUV, e.g., the “Falcon” ROV. Hence, in this review, the tracking
control technologies emphasize the autonomy of UUVs, and applications on ROVs can also serve as examples
of autonomous trajectory tracking control.

To realize the satisfactory trajectory tracking of the UUV, the vehicle must follow the desired path following
the corresponding time period. In other words, the errors between the desired and actual trajectories have to
be minimized at the different degrees of freedom [82] . However, different from common unmanned vehicles
such as the land vehicle or the unmanned surface vehicle (USV), the UUV system contains more states, whose
degrees of freedom (DOF) can be extended to six.

For the kinematic equation of the UUV, the velocity vector v can be transformed into the time derivative of
position vector p by a transformation matrix J as

¤ p = J(p)v. (3)

where the velocity vector v is [ ] , as the velocity variable shown at each DOF in Figure 6.