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Guidance And Control Of Ocean Vehicles

Path planning and obstacle avoidance for AUV: A review

The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments. The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles (AUVs) because of its hostile and dynamic nature. The major constraints for path planning are limited data transmission capability, power and sensing technology available for underwater operations. The sea environment is subjected to a large set of challenging factors classified as atmospheric, coastal and gravitational. Based on whether the impact of these factors can be approximated or not, the underwater environment can be characterized as predictable and unpredictable respectively. The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner. But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted. Path planning is necessary for many applications involving AUVs. These are based upon planning safety routes with minimum energy cost and computation overheads. This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable. The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.

/pdf/a-comprehensive-review-of-path-planning-algorithms-for-26oxjt8fe7.pdf

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

This paper proposes a novel fixed-time output feedback control scheme for trajectory tracking of marine surface vessels (MSVs) subject to unknown external disturbances and uncertainties. A fixed-time extended state observer (FESO) is proposed to estimate unknown lumped disturbances and unmeasured velocities, and the observation errors will converge to zero in fixed time. Based on the estimated values, a novel fixed-time trajectory tracking controller is designed for an MSV to track a time-varying reference trajectory by the extension of an adding a power integrator (API), and the tracking errors can converge to zero in fixed time as well. Additionally, the convergence time of the controller and the FESO is independent of initial state values. Finally, simulation results and comparisons illustrate the superiority of the proposed control scheme.

Fixed-time output feedback trajectory tracking control of marine surface vessels subject to unknown external disturbances and uncertainties

AUV position tracking and trajectory control based on fast-deployed deep reinforcement learning method

This study proposed the 3D path planning of an autonomous underwater vehicle (AUV) by using the hierarchical deep Q network (HDQN) combined with the prioritized experience replay. The path planning task was divided into three layers, which realized the dimensionality reduction of state space and solved the problem of dimension disaster. An artificial potential field was used to design the positive rewards of the algorithm to shorten the training time. According to the different requirements of the task, this study modified the rewards in the training process to obtain different paths. The path planning simulation and field tests were carried out. The results of the tests corroborated that the training time of the proposed method was shorter than that of the traditional method. The path obtained by simulation training was proved to be safe and effective.

https://www.mdpi.com/2077-1312/8/2/145/pdf

AUV 3D Path Planning Based on the Improved Hierarchical Deep Q Network

This research is concerned with the motion planning problem encountered by underactuated autonomous underwater vehicles (AUVs) in a mapless environment. A motion planning system based on deep reinforcement learning is proposed. This system, which directly optimizes the policy, is an end-to-end motion planning system. It uses sensor information as input and continuous surge force and yaw moment as output. It can reach multiple target points in a sequence while simultaneously avoiding obstacles. In addition, this study proposes a reward curriculum training method to solve the problem in which the number of samples required for random exploration increases exponentially with the number of steps needed to obtain a reward. At the same time, the negative impact of intermediate rewards can be avoided. The proposed system demonstrates good planning ability for a mapless environment and excellent ability to migrate to other unknown environments. The system also has resistance to current disturbances. The simulation results show that the proposed mapless motion planning system can guide an underactuated AUV in navigating to its desired targets without colliding with any obstacles.

Mapless Motion Planning System for an Autonomous Underwater Vehicle Using Policy Gradient-based Deep Reinforcement Learning

AUV path following controlled by modified Deep Deterministic Policy Gradient

In recent years, the research of multiple unmanned surface vessels collaboration has received great attention. More and more researchers have proposed different methods of multiple unmanned surface vessels collaboration, such as cooperative collision avoidance, formation, and rendezvous. Based on the significant advantages of biological swarm intelligence applications in these collaborative methods, this article summarizes the research methods of multiple unmanned surface vessels collaborative search and hunting from the perspective of swarm intelligence. First of all, this article summarizes the key technologies of multiple unmanned surface vessels collaborative search and hunting from the aspects of the multi-robot system, group communication, environment modeling, collaboration mechanism, and path planning. Then, it reviews some classic swarm intelligence algorithms, analyzes the advantages and disadvantages of these algorithms, and proposes optimization directions for existing disadvantages based on relevant literature. Finally, the article points out some existing problems in every stage and suggestions for future research.

Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence

AUV near-wall-following control based on adaptive disturbance observer

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