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Zhu et al. Intell Robot 2022;2(3):200­222 I http://dx.doi.org/10.20517/ir.2022.13 Page 202

Figure 1. The underwater motion planning scenario of the UUV.

2. TECHNOLOGIES OF UUV MOTION PLANNING
In this section, technologies for motion planning of UUVs are presented. Motion planning of UUVs can be
mainly categorized into steps of task assignment and path planning, where the path planning is usually split
into point-to-point path planning and full-coverage path planning.

Underwater motion planning is the crucial part that decides the efficiency of a UUV navigation. The optimal
vehicle motion has to be addressed in the requirement of the shortest total distance and time to arrive at the
target. As shown by the underwater motion planning scenario in Figure 1, under the effect of ocean currents
and obstacles, for the multi-UUV system, the optimal task assignment between multiple vehicles (in orange)
upon multiple targets (in the red triangle) is considered as the preparation for assessing satisfactory planned
paths. For the UUV path planning, the point-to-point path planning decides the initial navigation path from
the vehicle to the target, while the full-coverage path planning instructs the vehicle’s traversing operation after
arriving at the target area (area within the black circle).

2.1. Task assignment of Multi­UUV system
Originated from the last century, strategies applied to the task assignment of the multi-UUV system are mostly
realized by directly imitating animal behaviors. These assignments are designed through sensor-collected in-
formation, and the vehicle tasks are arranged referring to actual creature grouping behaviors [12,13] . Mataric et
al. proposed a task assignment algorithm that imitates the animal grouping behaviors such as swarming and
distributing [14] . Parkers established a distributed system that divides the assignment into smaller computing
sections based on vehicle behaviors [12] . Miyata developed a behavior-based algorithm that independently as-
signed the task for vehicles based on the time priority [15] . These studies verify the directness, simple operating
procedure, and no delays of behavior-based algorithms. However, they stay at the low administrative levels
of imitation, which are short of self-regulation/optimization, and the unsatisfactory collaboration leads to the
inefficiency of the algorithm and the requirement of intelligent task assignment methods.

Agent-based algorithms have been commonly applied to the task assignment of the multi-vehicle system [16–18] .