The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains thr...The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.展开更多
针对知识推理模型在捕获实体之间的复杂语义特征方面难以捕捉多层次语义信息,同时未考虑单一路径的可解释性对正确答案的影响权重不同等问题,提出一种融合路径与子图特征的知识图谱(KG)多跳推理模型PSHAM(Hierarchical Attention Model ...针对知识推理模型在捕获实体之间的复杂语义特征方面难以捕捉多层次语义信息,同时未考虑单一路径的可解释性对正确答案的影响权重不同等问题,提出一种融合路径与子图特征的知识图谱(KG)多跳推理模型PSHAM(Hierarchical Attention Model fusing Path-Subgraph features)。PS-HAM将实体邻域信息与连接路径信息进行融合,并针对不同路径探索多粒度的特征。首先,使用路径级特征提取模块提取每个实体对之间的连接路径,并采用分层注意力机制捕获不同粒度的信息,且将这些信息作为路径级的表示;其次,使用子图特征提取模块通过关系图卷积网络(RGCN)聚合实体的邻域信息;最后,使用路径-子图特征融合模块对路径级与子图级特征向量进行融合,以实现融合推理。在两个公开数据集上进行实验的结果表明,PS-HAM在指标平均倒数秩(MRR)和Hit@k(k=1,3,10)上的性能均存在有效提升。对于指标MRR,与MemoryPath模型相比,PS-HAM在FB15k-237和WN18RR数据集上分别提升了1.5和1.2个百分点。同时,对子图跳数进行的参数验证的结果表明,PS-HAM在两个数据集上都在子图跳数在3时推理效果达到最佳。展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.51879049)DK-I Dynamic Positioning System Console Project
文摘The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.
