Research On Adjustable Autonomy Mechanism For Unmanned Surface Vehicle System Based On Multi-objective Decision Theory

Unmanned surface vehicle,referred as USV,is a kind of surface vessels having certain autonomy and can be operated on the water surface without human.By loading intelligent task system,USV can carry out dangerous tasks and tasks unsuitable for boat with man,including different kinds of military tasks and non-military tasks.Thus US Vs are getting much attention by navies of different countries.These years,along with the development of the USV,its autonomy ability has been increased greatly.Meanwhile people’s expectations for USV performing complex tasks increase highly too.The problem of limited ability of USV on dealing with uncertain events thus emerges.Currently researchers agree that the use of adjustable autonomy mechanism can solve the problem.But there are still many challenges for USVs surfing on the sea surface.Therefore,explore on adjustable autonomy mechanisms of USVs has important research value,whether from a theoretical or application prospect.This thesis takes the mechanism of adjustable autonomy for USVs as the core problem for research,and researches the structure of adjustable autonomy system model for USVs,the decision method for autonomy levels,the situation awareness based on context awareness and the modeling mechanism of users’ factor respectively.The main research work of this paper can be summarized as followings:(1)A model of system supporting adjustable autonomy control mode is researched,which solves the problem of how user joins the control dynamically while influenced by variety of uncertain factors.The traditional control process of observation,orientation,decision-making and action is integrated with multi-objective decision theory,thus build up new control process of Observe-Orient-Adjustable Autonomy-Decide-Act,OOADA.Further,the adjustable autonomy system model for USVs is researched in detail,including the detailed design of adjustable autonomy module.Proved by deployed in simulation platform and experiments,the adjustable autonomy system model based multi-object decision theory can realize adjustable autonomy control mode.And because the structure of this model is build upon decision theory,it has solid theoretical foundation and can support the expansion requirements of task,object sets and diversity of autonomy levels,which have great and general value.(2)For navigation tasks,the method for situation awareness the contextual perception model in the adaptive autonomy control mode is researched,including context information collecting and reasoning.For the problem of autonomy ability of USVs often intervening by a variety uncertainties,the method for reasoning situation of human-USV interacting is build based on context awareness theory.The method includes definition and collection of the human-USV interaction information and the USV system performance analysis used multi-attribute utility function.Simulation experiments simulate different kinds of uncertain events to testify the ability of USV’s situation reasoning during the navigation tasks.The result shows uncertain events monitoring can identify different types and different degrees of uncertain events.At the same time it can also prove that context reasoning model can predict the different performance indicators’ trends under different autonomy levels,which can satisfy the needs of autonomy decision on system perform reasoning before and after the adjustment.(3)The thesis researches the user model under the contextual awareness by dividing it into two dimensions:the user preferences and user situation cognitive ability,which are both used to connect with autonomy levels.The user performance uses multi-attribute decision theory to evaluate the influence of users’ workload on the autonomy level.While user situation cognitive ability takes Markov decision model predict the influence of the result of user cognition on autonomy levels.In the simulation experiments,USV navigation tasks are used to test the situation recognition ability of the system on different users and same user with different preferences,along with the impact on the result of autonomy levels decision.The results prove that identifying user is necessary and beneficial.At the same time the user model controls the workload of the user,and decreases the instability of the system performance brought by changing users.(4)The paper researches the algorithm based on multiple attribute decision making to decide the autonomy level of USV.Using preference ranking organization method for enrichment evaluation solve the problem such as incommensurability of autonomy decision objects.The priority functions are defined to evaluate the advantages of each performance indicator,thus avoid the estimation error brought by data preprocessing.Then the algorithm builds a directed diagraph to merge the utility of each autonomy level,after which the difference advantages of autonomy levels are get.Simulation experiments of comparing fully autonomy with adjustable autonomy based on multi-object decision theory are conducted to show the changing of the system performance in navigation task.The performance of the USV system under adjustable autonomy mode is analyzed and evaluated comprehensively.The results prove that the adjustable autonomy based multi-object decision can dramatically increased system performance with a little increasing of user participation.