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Perception, Planning and Control of Marine Robots
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Perception, Planning and Control of Marine Robots

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 18892

Special Issue Editors

Prof. Dr. Rongxin Cui

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: underwater vehicles; nonlinear control; path planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhouhua Peng

E-Mail Website
Guest Editor
School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: cooperative control; adaptive control; optimization methods with applications in formation control of autonomous surface/underwater vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Marine robots, such as surface vessles, underwater vessles, have been widely used for ocean monitoring, survey, serach and rescure tasks. Perception, planning and control play an important role when performing such tasks. This special issue will focus on the use of sensors to address marine robotic challenges in the complex ocean enviroment, dealing with both the scientific foundations of sensors and their use and applications on marine robots.

Prof. Dr. Rongxin Cui
Dr. Zhouhua Peng
Guest Editors

Manuscript Submission Information

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Keywords

  • marine robots
  • perception
  • path planning
  • adaptive control
  • sensor fusion

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Published Papers (6 papers)

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Research

19 pages, 18006 KiB  
Article
Dynamic Obstacle Avoidance for USVs Using Cross-Domain Deep Reinforcement Learning and Neural Network Model Predictive Controller
by Jianwen Li, Jalil Chavez-Galaviz, Kamyar Azizzadenesheli and Nina Mahmoudian
Sensors 2023, 23(7), 3572; https://doi.org/10.3390/s23073572 - 29 Mar 2023
Cited by 1 | Viewed by 3578
Abstract
This work presents a framework that allows Unmanned Surface Vehicles (USVs) to avoid dynamic obstacles through initial training on an Unmanned Ground Vehicle (UGV) and cross-domain retraining on a USV. This is achieved by integrating a Deep Reinforcement Learning (DRL) agent that generates [...] Read more.
This work presents a framework that allows Unmanned Surface Vehicles (USVs) to avoid dynamic obstacles through initial training on an Unmanned Ground Vehicle (UGV) and cross-domain retraining on a USV. This is achieved by integrating a Deep Reinforcement Learning (DRL) agent that generates high-level control commands and leveraging a neural network based model predictive controller (NN-MPC) to reach target waypoints and reject disturbances. A Deep Q Network (DQN) utilized in this framework is trained in a ground environment using a Turtlebot robot and retrained in a water environment using the BREAM USV in the Gazebo simulator to avoid dynamic obstacles. The network is then validated in both simulation and real-world tests. The cross-domain learning largely decreases the training time (28%) and increases the obstacle avoidance performance (70 more reward points) compared to pure water domain training. This methodology shows that it is possible to leverage the data-rich and accessible ground environments to train DRL agent in data-poor and difficult-to-access marine environments. This will allow rapid and iterative agent development without further training due to the change in environment or vehicle dynamics. Full article
(This article belongs to the Special Issue Perception, Planning and Control of Marine Robots)
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33 pages, 1190 KiB  
Article
Simultaneous Control and Guidance of an AUV Based on Soft Actor–Critic
by Yoann Sola, Gilles Le Chenadec and Benoit Clement
Sensors 2022, 22(16), 6072; https://doi.org/10.3390/s22166072 - 14 Aug 2022
Cited by 9 | Viewed by 2526
Abstract
The marine environment is a hostile setting for robotics. It is strongly unstructured, uncertain, and includes many external disturbances that cannot be easily predicted or modeled. In this work, we attempt to control an autonomous underwater vehicle (AUV) to perform a waypoint tracking [...] Read more.
The marine environment is a hostile setting for robotics. It is strongly unstructured, uncertain, and includes many external disturbances that cannot be easily predicted or modeled. In this work, we attempt to control an autonomous underwater vehicle (AUV) to perform a waypoint tracking task, using a machine learning-based controller. There has been great progress in machine learning (in many different domains) in recent years; in the subfield of deep reinforcement learning, several algorithms suitable for the continuous control of dynamical systems have been designed. We implemented the soft actor–critic (SAC) algorithm, an entropy-regularized deep reinforcement learning algorithm that allows fulfilling a learning task and encourages the exploration of the environment simultaneously. We compared a SAC-based controller with a proportional integral derivative (PID) controller on a waypoint tracking task using specific performance metrics. All tests were simulated via the UUV simulator. We applied these two controllers to the RexROV 2, a six degrees of freedom cube-shaped remotely operated underwater Vehicle (ROV) converted in an AUV. We propose several interesting contributions as a result of these tests, such as making the SAC control and guiding the AUV simultaneously, outperforming the PID controller in terms of energy saving, and reducing the amount of information needed by the SAC algorithm inputs. Moreover, our implementation of this controller allows facilitating the transfer towards real-world robots. The code corresponding to this work is available on GitHub. Full article
(This article belongs to the Special Issue Perception, Planning and Control of Marine Robots)
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21 pages, 51818 KiB  
Article
Unmanned Surface Vehicle Collision Avoidance Path Planning in Restricted Waters Using Multi-Objective Optimisation Complying with COLREGs
by Yang Gu, Zhenwei Rong, Huzhou Tong, Jia Wang, Yulin Si and Shujie Yang
Sensors 2022, 22(15), 5796; https://doi.org/10.3390/s22155796 - 3 Aug 2022
Cited by 3 | Viewed by 2410
Abstract
Navigation safety is one of the primary operational requirements for unmanned surface vehicles (USVs) in a complex marine environment, mainly guaranteed by a reliable path planning system for collision avoidance. This work proposes a novel weighted sum multi-objective optimisation strategy for USV collision [...] Read more.
Navigation safety is one of the primary operational requirements for unmanned surface vehicles (USVs) in a complex marine environment, mainly guaranteed by a reliable path planning system for collision avoidance. This work proposes a novel weighted sum multi-objective optimisation strategy for USV collision avoidance path planning in restricted waters. In particular, the coefficients of different objectives could be tuned to emphasise the most critical design consideration under varying navigation scenarios. Moreover, in addition to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the terrain and weather constraints were also considered in the path planning system. The proposed USV collision avoidance path planning framework’s effectiveness was demonstrated through numerical simulations and hardware-in-the-loop (HIL) tests. The numerical simulation results indicate that the proposed method could avoid collision with dynamic and static obstacles, and it is also adaptive to different navigation restrictions and preferences. Moreover, a USV navigation platform was established by incorporating true Automatic Identification System (AIS) signals, and HIL tests were performed with real-time AIS data in a water channel in the Zhoushan archipelago. The results demonstrate that the proposed USV path planning strategy is applicable in restricted waters with complex terrains and weather constraints. Full article
(This article belongs to the Special Issue Perception, Planning and Control of Marine Robots)
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23 pages, 1407 KiB  
Article
Radar Target Tracking for Unmanned Surface Vehicle Based on Square Root Sage–Husa Adaptive Robust Kalman Filter
by Shuanghu Qiao, Yunsheng Fan, Guofeng Wang, Dongdong Mu and Zhiping He
Sensors 2022, 22(8), 2924; https://doi.org/10.3390/s22082924 - 11 Apr 2022
Cited by 19 | Viewed by 2713
Abstract
Dynamic information such as the position and velocity of the target detected by marine radar is frequently susceptible to external measurement white noise generated by the oscillations of an unmanned surface vehicle (USV) and target. Although the Sage–Husa adaptive Kalman filter (SHAKF) has [...] Read more.
Dynamic information such as the position and velocity of the target detected by marine radar is frequently susceptible to external measurement white noise generated by the oscillations of an unmanned surface vehicle (USV) and target. Although the Sage–Husa adaptive Kalman filter (SHAKF) has been applied to the target tracking field, the precision and stability of SHAKF remain to be improved. In this paper, a square root Sage–Husa adaptive robust Kalman filter (SR-SHARKF) algorithm together with the constant jerk model is proposed, which can not only solve the problem of filtering divergence triggered by numerical rounding errors, inaccurate system mathematics, and noise statistical models, but also improve the filtering accuracy. First, a novel square root decomposition method is proposed in the SR-SHARKF algorithm for decomposing the covariance matrix of SHAKF to assure its non-negative definiteness. After that, a three-segment approach is adopted to balance the observed and predicted states by evaluating the adaptive scale factor. Finally, the unbiased and the biased noise estimators are integrated while the interval scope of the measurement noise is constrained to jointly evaluate the measurement and observation noise for better adaptability and reliability. Simulation and experimental results demonstrate the effectiveness of the proposed algorithm in eliminating white noise triggered by the USV and target oscillations. Full article
(This article belongs to the Special Issue Perception, Planning and Control of Marine Robots)
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14 pages, 1168 KiB  
Article
A Hierarchical Stabilization Control Method for a Three-Axis Gimbal Based on Sea–Sky-Line Detection
by Zhanhua Xin, Shihan Kong, Yuquan Wu, Gan Zhan and Junzhi Yu
Sensors 2022, 22(7), 2587; https://doi.org/10.3390/s22072587 - 28 Mar 2022
Cited by 9 | Viewed by 2736
Abstract
Obtaining a stable video sequence for cameras on surface vehicles is always a challenging problem due to the severe disturbances in heavy sea environments. Aiming at this problem, this paper proposes a novel hierarchical stabilization method based on real-time sea–sky-line detection. More specifically, [...] Read more.
Obtaining a stable video sequence for cameras on surface vehicles is always a challenging problem due to the severe disturbances in heavy sea environments. Aiming at this problem, this paper proposes a novel hierarchical stabilization method based on real-time sea–sky-line detection. More specifically, a hierarchical image stabilization control method that combines mechanical image stabilization with electronic image stabilization is adopted. With respect to the mechanical image stabilization method, a gimbal with three degrees of freedom (DOFs) and with a robust controller is utilized for the primary motion compensation. In addition, the electronic image stabilization method based on sea–sky-line detection in video sequences accomplishes motion estimation and compensation. The Canny algorithm and Hough transform are utilized to detect the sea–sky line. Noticeably, an image-clipping strategy based on prior information is implemented to ensure real-time performance, which can effectively improve the processing speed and reduce the equipment performance requirements. The experimental results indicate that the proposed method for mechanical and electronic stabilization can reduce the vibration by 74.2% and 42.1%, respectively. Full article
(This article belongs to the Special Issue Perception, Planning and Control of Marine Robots)
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29 pages, 5171 KiB  
Article
A Novel Reinforcement Learning Collision Avoidance Algorithm for USVs Based on Maneuvering Characteristics and COLREGs
by Yunsheng Fan, Zhe Sun and Guofeng Wang
Sensors 2022, 22(6), 2099; https://doi.org/10.3390/s22062099 - 8 Mar 2022
Cited by 19 | Viewed by 3322
Abstract
Autonomous collision avoidance technology provides an intelligent method for unmanned surface vehicles’ (USVs) safe and efficient navigation. In this paper, the USV collision avoidance problem under the constraint of the international regulations for preventing collisions at sea (COLREGs) was studied. Here, a reinforcement [...] Read more.
Autonomous collision avoidance technology provides an intelligent method for unmanned surface vehicles’ (USVs) safe and efficient navigation. In this paper, the USV collision avoidance problem under the constraint of the international regulations for preventing collisions at sea (COLREGs) was studied. Here, a reinforcement learning collision avoidance (RLCA) algorithm is proposed that complies with USV maneuverability. Notably, the reinforcement learning agent does not require any prior knowledge about USV collision avoidance from humans to learn collision avoidance motions well. The double-DQN method was used to reduce the overestimation of the action-value function. A dueling network architecture was adopted to clearly distinguish the difference between a great state and an excellent action. Aiming at the problem of agent exploration, a method based on the characteristics of USV collision avoidance, the category-based exploration method, can improve the exploration ability of the USV. Because a large number of turning behaviors in the early steps may affect the training, a method to discard some of the transitions was designed, which can improve the effectiveness of the algorithm. A finite Markov decision process (MDP) that conforms to the USVs’ maneuverability and COLREGs was used for the agent training. The RLCA algorithm was tested in a marine simulation environment in many different USV encounters, which showed a higher average reward. The RLCA algorithm bridged the divide between USV navigation status information and collision avoidance behavior, resulting in successfully planning a safe and economical path to the terminal. Full article
(This article belongs to the Special Issue Perception, Planning and Control of Marine Robots)
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