Multiple unmanned surface vehicles-based path planning method for sea patrol

Abstract

Objective The artificial intelligence(AI)technology based intelligent unmanned equipments like unmanned surface vehicles have been developing for such domains of marine technology-derived military operation and counter-terrorism,resource exploration,seabed topography mapping,water quality testing,and marine search and rescue. In recent years,such issues of unmanned surface vehicles-related path planning have been concerned about. However,existing tasks are still focused on the path planning of single unmanned surface vehicle. The methods and simulations are only based on narrowed water areas like lakes and ports,and the problem of energy constraint is required to be melted into as well. For large-scale sea patrol scenarios,multi-unmanned surface vehicles can be incorporated into coverage path planning tasks in terms of quick response-relevant fast speed,high efficiency and low risk. However,small unmanned surface vessels for sea patrol are mainly driven electrically. Their navigating range and operating time are limited. It is necessary to divide the large-scale sea patrol task into multiple tasks and the problem of energy constraint is required to be resolved for unmanned surface ships. Therefore,we develop a solution of area coverage path planning based on multiple unmanned surface vehicles-integrated large-scale sea patrol tasks. Method First,we define and model the problem of sea patrol of multi-unmanned surface vehicles,a task-equal region division strategy and multiple evaluation criteria are proposed and implemented through constructing the sea grid map. The task-equal region division strategy can make all unmanned surface vehicles get similar size of task area and ensure that the full task can be completed to reach the minimum total time. Second,we analyse and compare several traditional traversal algorithms,including round-trip traversal algorithm or spiral traversal algorithm. The breadth-first search algorithm is combined with these traversal algorithms to perform the area coverage path planning task. We demonstrate two sort of traversal algorithms under two different conditions,that is,energy constraint is included or excluded. Especially,two energy constraint-derived charging path planning strategies are taken into account,including the original route or a new route after charging. Intuitively,the advantage of the former is a kind of decoupling path planning in terms of energy supply,while the limition is linked with the duplication of charging path. The redundancy is derived of repetition rate and wastes part of energy. The pros of the latter is concerned that the charging route is not traversed again,while the cons is that complexity of the task map can be increased due to redundant routes. It may cause more energy supply. Result A Python-related simulation platform is built up based on the sea map of more than 15 000 square kilometers around Sansha city of China. Specifically,such configurable settings are listed：the physics simulation engine is Pymunk,the rendering tool is Pygame,and the numerical calculation tool is Numpy. We carry our three sorts of key experiments and get some following results. 1)The advantages and disadvantages of different traversal algorithms are analysed and compared under the conditions of energy constraints-included or excluded. In detail,we adopt breadth-first search algorithm in related to round-trip traversal algorithm or spiral traversal algorithm. For non-energy constraint,round-trip coverage method is generally better than spiral coverage method. It means that the repetition rate,the total number of steps and the total time of the round-trip traversal algorithm are lower than spiral algorithm under the same circumstances. 2)The effectiveness of the proposed task-equal region division strategy is validated. For example,under the setting of five unmanned ships for operation,the coverage rates of the coveraged areas are 20. 8%,20. 4%,20%, 19. 8% and 19. 0% in the total area. Similar sized task areas can ensure consistent task time for different ships to minimize total task time. 3)Compared to non-energy constraints,energy constraint-related setting is challenged much more,and the energy constraint-relevant setting can illustrate higher repetition rate,total steps and total time on the basis of the same number of unmanned ships and the same traversal algorithm. Energy constraint can be used to validate the potentials of routes replanning after charging beyond the original path after charging. Furthermore,ablation experiments are carried out in relevance to parameters-based number of ships,coverage rate and battery range of the unmanned ship. Ablation experimental results demonstrate that：1)Increasing the number of unmanned ships will significantly reduce the total time to complete the task. There is an inverse relationship between the number of unmanned ships and the total task time. In practice, the increasment of the number of unmanned vessels can be as the most effective scheme to improve the efficiency within the range of purchase cost. 2)Appropriate reduction of the coverage requirement can reduce the repetition rate and the total task time. Actually,you can compensate the reduced coverage requirements to perform multiple tasks through changing the starting point. 3)Battery range of unmanned ships can be increased to improve task efficiency dramatically. Conclusion A solution of area coverage path planning is developed based on multiple unmanned surface vehicles cooperation for large-scale sea patrol tasks. The solution can deal with the problems of task division and energy supply of multiple unmanned surface vehicles,and be viewed as a certain reference for the practical application of multi-unmanned surface vehicles performing large-scale sea patrol tasks. Next,we will further study the relationship between charging equipment deployment strategy and algorithm efficiency in real sea environment,consider the problem of sudden collision avoidance during the navigation of unmanned ships,and expand the application scope of the proposed method,such as the seabed three-dimensional detection task.