Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels

Abstract

In this investigation, a smart collision avoidance control design, which integrates a collision avoidance navigation and a nonlinear optimal control method, is developed for unmanned surface vessels (USVs) under randomly incoming ships and fixed obstacle encounter situations. For achieving collision avoidance navigation, a fuzzy collision risk indicator and a fuzzy collision avoidance acting timing indicator are developed. These two risk indicators can offer effective pre-alarms for making the controlled USVs to perform dodge actions in time when obstacles appear. As to nonlinear optimal control law, it provides a precise trajectory tracking ability for the controlled USVs to follow a collision avoidance trajectory, which is generated via a smart collision avoidance trajectory generator. Finally, a power allocation method is used to transform the desired control law into available actuator outputs to guide the USVs to follow a desired collision avoidance trajectory. From simulation results, the proposed collision avoidance strategy reveals a promising collision avoidance performance and an accurate trajectory tracking ability with respect to fixed objects and randomly moving ships under the effect of environmental ocean disturbances.

Estimating Critical Latency Affecting Ship’s Collision in Re-Mote Maneuvering of Autonomous Ships

“…One of the possible causes for this Point M is the circumstance where due to a certain latency, the OS cannot be turning maneuvered at O1 but is turning maneuvered at O2. Furthermore, it is known that such a latency can be caused by various environments (i.e., communication network, control signal processing, human factors) in the case of remote maneuvering where communication networks are utilized [4][5][6][7][8][9]. In addition, the positions where the M1 or M2 points can occur may be varied by the degree of latency and/or by various environments (i.e., the encountering situation, rudder angle, speed, relative distance between the two vessels) [16][17][18][19][20][21].…”

“…Level 1 is the control level of the existing ship; Level 2 is the level of the existing ship combined with automatic controls; Level 3 is almost entirely an autonomy degrees; Level 4 is the fully autonomy degrees [3][4][5]. Currently, most MASS research is conducted at Levels 2 to 3 [6][7][8][9].…”

“…Remote maneuvering is included in the second or third level of MASS, which refers to the remote control of ships by a remote operator using a communication network (i.e., long-term evolution network, very high frequency, or very small aperture terminal (V-SAT)) in locations other than MASS (i.e., other ships/coasts) [3][4][5][6][7][8]. In this remote maneuvering, it is known that various types of latency can be caused due to various environments (i.e., loss and delay of communication, failure and delay of control signal processing, and human factors) [4][5][6][7][8][9]. In addition, latency is known to affect ship collisions, and latency is required to be minimal [3,4].…”

Artificial Intelligence-Based Methods for Decision Support to Avoid Collisions at Sea

“…On the other hand, many techniques have been proposed for solving the aforementioned problem of ship steering in specific cases, based on both conventional techniques and more developed ones. Herein, the main aim was to provide assistance to navigators in terms of making the appropriate and accurate maneuvering decisions under certain conditions, using such techniques as those proposed in the references [18][19][20][21][22][23][24][25]. The study presented in this paper was dedicated to the issue of determining the optimal safe trajectory of a ship at sea, where many other ships can be encountered in the vicinity, using selected methods based on artificial intelligence techniques, such as fuzzy logic, neural network, genetic algorithms, and particle swarm optimization algorithms.…”

“…The study presented in this paper was dedicated to the issue of determining the optimal safe trajectory of a ship at sea, where many other ships can be encountered in the vicinity, using selected methods based on artificial intelligence techniques, such as fuzzy logic, neural network, genetic algorithms, and particle swarm optimization algorithms. In fact, many papers propose methods for the determination of the safe trajectory of a ship in a collision situation based on fuzzy logic [24][25][26][27][28], while other researchers propose algorithms for solving the same problem based on neural network techniques [26,29,30]. Furthermore, other proposed methods that are used widely in this domain are based on evolutionary algorithms, genetic algorithms, and their modified variants, such as the works presented in [31][32][33].…”