基于椭圆动态限制和免疫机理的路径规划算法

为更好地解决机器人路径规划问题，基于椭圆动态限制和免疫机理提出一种路径规划算法。首先，在全向空间内依据疫苗启发因子生成初始抗体种群。其次，将节点作为基本计算单元构建节点存储结构，避免局部路径信息重复计算，节点变异的同时更新节点信息。然后，根据路径值构建100%置信水平下的椭圆搜索区域，在不影响最优路径求解的同时动态缩小搜索区域，通过节点删除的两层限制不断删除无效节点，提高算法搜索效率。最后，将本文算法与其他3种算法对比，仿真结果表明本文算法搜索时间平均减少了77.24%，搜索的节点数量平均减少了55.54%。

Path Planning Algorithm Based on the Dynamic Restriction of Ellipse and Immune Mechanism

This study proposes an algorithm based on the dynamic restriction of an ellipse and immune mechanism to solve robot path planning problems. First， the initial antibody population is generated in the omnidirectional space by the vaccine-inspired factors. Next， a node storage structure is introduced as the primary computing unit to avoid repeated calculation of local path information. Node mutation is used to update node information. Then， an ellipse search area is then constructed under the confidence level of 100% by the path value， and the ellipse search area is dynamically reduced without affecting the optimal path solution. Invalid nodes are deleted continuously through the node deletion under two-level restrictions， which improves the search efficiency. Finally， the proposed algorithm was compared with other algorithms， where the simulation results verified the certainty and effectiveness of the algorithm. The search time is reduced by 77.24%， and the number of nodes is reduced by 55.54%.