针对传统人工势场(traditional artificial potential field,T-APF)算法在自主船舶应急避碰场景中存在的局部最优问题和动态障碍物避碰局限性问题,提出一种基于最近会遇时间(time to closest point of approach,TCPA)和最近会遇距离(dis...针对传统人工势场(traditional artificial potential field,T-APF)算法在自主船舶应急避碰场景中存在的局部最优问题和动态障碍物避碰局限性问题,提出一种基于最近会遇时间(time to closest point of approach,TCPA)和最近会遇距离(distance to closest point of approach,DCPA)的优化人工势场(enhanced artificial potential field,E-APF)算法,通过重构斥力势场函数,引入动态权重调整机制,并结合相对运动态势设计自适应斥力方向策略。仿真结果表明:在静态障碍物场景中,E-APF算法比T-APF算法能更早识别碰撞风险并规划更优路径;在动态障碍物场景中,可有效增大安全距离并减小转向幅度,显著提高障碍物风险评估和避碰决策的准确性。展开更多
Compared with accurate diagnosis, the system’s selfdiagnosing capability can be greatly increased through the t/kdiagnosis strategy at most k vertexes to be mistakenly identified as faulty under the comparison model,...Compared with accurate diagnosis, the system’s selfdiagnosing capability can be greatly increased through the t/kdiagnosis strategy at most k vertexes to be mistakenly identified as faulty under the comparison model, where k is typically a small number. Based on the Preparata, Metze, and Chien(PMC)model, the n-dimensional hypercube network is proved to be t/kdiagnosable. In this paper, based on the Maeng and Malek(MM)*model, a novel t/k-fault diagnosis(1≤k≤4) algorithm of ndimensional hypercube, called t/k-MM*-DIAG, is proposed to isolate all faulty processors within the set of nodes, among which the number of fault-free nodes identified wrongly as faulty is at most k. The time complexity in our algorithm is only O(2~n n~2).展开更多
文摘针对传统人工势场(traditional artificial potential field,T-APF)算法在自主船舶应急避碰场景中存在的局部最优问题和动态障碍物避碰局限性问题,提出一种基于最近会遇时间(time to closest point of approach,TCPA)和最近会遇距离(distance to closest point of approach,DCPA)的优化人工势场(enhanced artificial potential field,E-APF)算法,通过重构斥力势场函数,引入动态权重调整机制,并结合相对运动态势设计自适应斥力方向策略。仿真结果表明:在静态障碍物场景中,E-APF算法比T-APF算法能更早识别碰撞风险并规划更优路径;在动态障碍物场景中,可有效增大安全距离并减小转向幅度,显著提高障碍物风险评估和避碰决策的准确性。
基金supported by the National Natural Science Foundation of China(61363002)
文摘Compared with accurate diagnosis, the system’s selfdiagnosing capability can be greatly increased through the t/kdiagnosis strategy at most k vertexes to be mistakenly identified as faulty under the comparison model, where k is typically a small number. Based on the Preparata, Metze, and Chien(PMC)model, the n-dimensional hypercube network is proved to be t/kdiagnosable. In this paper, based on the Maeng and Malek(MM)*model, a novel t/k-fault diagnosis(1≤k≤4) algorithm of ndimensional hypercube, called t/k-MM*-DIAG, is proposed to isolate all faulty processors within the set of nodes, among which the number of fault-free nodes identified wrongly as faulty is at most k. The time complexity in our algorithm is only O(2~n n~2).
