The paper presents an innovative approach (CBNNTAP) that addresses the
complexities and challenges introduced by ocean currents when optimizing target
assignment and motion planning for a multi-unmanned underwater vehicle (UUV)
system. The core of the proposed algorithm involves the integration of several
key components. Firstly, it incorporates a bio-inspired neural network-based
(BINN) approach which predicts the most efficient paths for individual UUVs
while simultaneously ensuring collision avoidance among the vehicles. Secondly,
an efficient target assignment component is integrated by considering the path
distances determined by the BINN algorithm. In addition, a critical innovation
within the CBNNTAP algorithm is its capacity to address the disruptive effects
of ocean currents, where an adjustment component is seamlessly integrated to
counteract the deviations caused by these currents, which enhances the accuracy
of both motion planning and target assignment for the UUVs. The effectiveness
of the CBNNTAP algorithm is demonstrated through comprehensive simulation
results and the outcomes underscore the superiority of the developed algorithm
in nullifying the effects of static and dynamic ocean currents in 2D and 3D
scenarios.

该论文介绍了一种创新的方法（CBNNTAP），解决了多无人水下航行器系统在优化目标分配和运动规划时由海洋洋流引入的复杂性和挑战。该算法集成了生物启发的神经网络、有效的目标分配和适应海洋洋流影响的调整组件，通过综合模拟结果证明了其优越性。

多个无人水下航行器系统的无干扰优化目标分配和运动规划

Current Effect-eliminated Optimal Target Assignment and Motion Planning  for a Multi-UUV System

Low-propulsion vessels can take advantage of powerful ocean currents to
navigate towards a destination. Recent results demonstrated that vessels can
reach their destination with high probability despite forecast errors. However,
these results do not consider the critical aspect of safety of such vessels:
because of their low propulsion which is much smaller than the magnitude of
currents, they might end up in currents that inevitably push them into unsafe
areas such as shallow areas, garbage patches, and shipping lanes. In this work,
we first investigate the risk of stranding for free-floating vessels in the
Northeast Pacific. We find that at least 5.04% would strand within 90 days.
Next, we encode the unsafe sets as hard constraints into Hamilton-Jacobi
Multi-Time Reachability (HJ-MTR) to synthesize a feedback policy that is
equivalent to re-planning at each time step at low computational cost. While
applying this policy closed-loop guarantees safe operation when the currents
are known, in realistic situations only imperfect forecasts are available. We
demonstrate the safety of our approach in such realistic situations empirically
with large-scale simulations of a vessel navigating in high-risk regions in the
Northeast Pacific. We find that applying our policy closed-loop with daily
re-planning on new forecasts can ensure safety with high probability even under
forecast errors that exceed the maximal propulsion. Our method significantly
improves safety over the baselines and still achieves a timely arrival of the
vessel at the destination.

研究了低推进船只在使用强海流导航时面临的安全问题，并提出了利用 Hamilton-Jacobi 多时间可达性方法来改善安全性的策略。该方法能够在考虑海流和天气预报误差的情况下，以低计算成本对船只进行安全操作和导航。

复杂海洋洋流中欠驱船只的搁浅风险：分析与控制器

Stranding Risk for Underactuated Vessels in Complex Ocean Currents:  Analysis and Controllers

Seaweed biomass offers significant potential for climate mitigation, but
large-scale, autonomous open-ocean farms are required to fully exploit it. Such
farms typically have low propulsion and are heavily influenced by ocean
currents. We want to design a controller that maximizes seaweed growth over
months by taking advantage of the non-linear time-varying ocean currents for
reaching high-growth regions. The complex dynamics and underactuation make this
challenging even when the currents are known. This is even harder when only
short-term imperfect forecasts with increasing uncertainty are available. We
propose a dynamic programming-based method to efficiently solve for the optimal
growth value function when true currents are known. We additionally present
three extensions when as in reality only forecasts are known: (1) our methods
resulting value function can be used as feedback policy to obtain the
growth-optimal control for all states and times, allowing closed-loop control
equivalent to re-planning at every time step hence mitigating forecast errors,
(2) a feedback policy for long-term optimal growth beyond forecast horizons
using seasonal average current data as terminal reward, and (3) a discounted
finite-time Dynamic Programming (DP) formulation to account for increasing
ocean current estimate uncertainty. We evaluate our approach through 30-day
simulations of floating seaweed farms in realistic Pacific Ocean current
scenarios. Our method demonstrates an achievement of 95.8% of the best possible
growth using only 5-day forecasts. This confirms the feasibility of using
low-power propulsion and optimal control for enhanced seaweed growth on
floating farms under real-world conditions.

利用动态规划优化控制来利用海流控制海藻生长，可实现在真实世界条件下通过低功率推进和优化控制实现漂浮海藻场的增强生长。