Trans RINA, Vol 161, Part A4, Intl J Maritime Eng, Oct-Dec 2019
Wei et al. (2015) has developed a minimum fuel consumption based solution model to solve collision avoidance problems at sea. In this study, problem solving has been achieved by using the Cat Swarm Biological Algorithm (CSBA). The simulation tests have showed that the model is effective and applicable.
Lazarowska (2015) has presented a swarm intelligence application using Ant Colony Optimization (ACO) to form a decision support system. The capability of the system contains collision avoidance path planning in restricted waters as well as open sea. In this study, polygonal ship domain has been used instead of circular which is commonly used and static objects have also been taken into account to generate collision-free trajectories. The other ACO based method has been introduced by Tsou and Hsueh (2010). The main difference between these two studies is solution construction. The former one takes into account all target ships (TSs) in the vicinity at the same time to construct a collision avoidance path. The latter one, however, the TS with the highest collision risk is first to be disposed. The collision avoidance calculation has been performed with regard to collision risk degree of the TSs.
On the other hand, Lazarowska (2017) has also presented a deterministic approach, called the Trajectory Base Algorithm Decision Support System (TBADSS), to generate a decision support system providing an optimal and safe trajectory for ships. The system has been formed to solve the trajectory planning problem for complex environment with dynamic and static obstacles. The TBADSS composes of four submodules as Data Input Module, Database Module, TBA Module and Solution Output Module. The database constituting all possible solution trajectory has been created and the TBADSS aims to find the optimal one. The deterministic algorithm has been compared with the ACO-based method. The TBA- based approach provides better performance concerning lengths of path and execution time.
Nguyen et al. (2012) has developed a Bacteria Foraging Optimization based automatic tool for navigators by determining the optimal collision avoidance strategy. The proposed algorithm has been applied for various scenarios to confirm its efficiency. The scenario implementations have showed that the algorithm is robust, efficient and applicable.
Naeem et al. (2012) has proposed a COLREGs-based collision avoidance strategy for USV. The developed system is a reactive path planning algorithm providing feedback to autopilot of USV or navigator of manned ship for altering the course. A* algorithm and Line-of-Sight (LOS) algorithm has been used to generate a safe trajectory and both could produce a realistic trajectory.
Lisowski (2012) has introduced a game control process in marine navigation. In this study, multi-step matrix and multi-stage positional, cooperative and non-cooperative,
optimal and game control algorithms in encounter situation has been implemented. The simulation of control game algorithm has revealed that the model of game theory for the optimal manoeuvring has made it possible to form the safe route of the OS passing through a large number of TSs.
Tam and Bucknall (2013) has developed a deterministic based algorithm to generate a practical and COLREGs- complaint navigation trajectory for ships with collision risks. The algorithm structure has been categorized into two main subdivisions. The first subdivision is to determine the collision risk for each target in the vicinity. The second subdivision comprises the calculation of avoidance manoeuvre to overcome the collision risk. It is emphasized in the study that the algorithm is based on a deterministic form so, it can produce the exact and unique solution in every execution.
Xu (2014) has presented a Danger Immune Algorithm- based method to accomplish ship collision avoidance route optimization regarding economy and safety. In this study, ship domain and ship arena have been utilized to evaluate the collision risk to calculate the fitness function. The simulation tests have revealed that the algorithm is feasible and valid.
Zhang et al. (2015) have studied on a multi-ship collision avoidance decision support using Linear Extension Algorithm under the requirements of COLREGs. The model has been developed to form a safe path for the OS to keep her clearance from all the TSs in the navigation area. The study concludes that the speed changing to avoid collision gives better performance than course alteration for encounter situations with small crossing angle while course alteration performs better for large crossing angle.
Johansen et al. (2016) has described a model predictive control based approach for collision avoidance system for ship. A set of control behaviours for an autonomous ship have been constituted by diversifying two parameters: course command and propulsion command. Simulation experiment has showed that the model can be set to determine control behaviours for various cases and effectively manages complex situations with multiple obstacles.
Wang et al. (2017) has proposed a finite-time observer based accurate tracking control plan for path tracking of a marine vehicle with complex uncertainties. The simulation studies carried out in the study have showed that the marine vehicle system under the thoroughly uncertain dynamics conditions can be properly tracked regarding velocity and position.
Kim et al. (2017) has developed a Distributed Stochastic Search Algorithm (DSSA)-based method which enables each ship to alter her course in a stochastic manner according to the intentions of the TSs. The experimental test results have showed that the DSSA yields better
©2019: The Royal Institution of Naval Architects
A-347
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154 |
Page 155 |
Page 156 |
Page 157 |
Page 158 |
Page 159 |
Page 160 |
Page 161 |
Page 162 |
Page 163 |
Page 164 |
Page 165 |
Page 166
