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针对冗余自由度机器人连续轨迹多目标逆解规划问题,分析了多目标决策方法的基本原理,提出并证明了积式决策方法的可行性,并提出商式外点罚函数法,以在积式决策模型框架下处理约束.积式决策优化模型具有复杂的局部极值点结构,需要求解器具有极强的全局极值点搜索能力.为此,设计了高斯巡游粒子群优化算法.以5组通用的无约束和约束最优化测试函数为对象,比较了本文提出的高斯巡游粒子群优化算法和标准粒子群优化算法的全局极值点搜索能力,分别求解100次,结果表明,本文所提算法的求解成功率高于标准粒子群算法.针对具备复杂局部极值点结构的7维优化测试函数,所提算法寻优成功率仍达80%,而标准粒子群算法的寻优成功率下降为0,证明了所提算法具备较强的寻优能力,尤其是在高维空间上,可应用于多自由度机器人路径规划问题求解.
Aiming at the multi-objective inverse solution programming problem of continuous trajectory for redundant degree-of-freedom robots, the basic principle of the multi-objective decision-making method is analyzed and the feasibility of the integrated decision-making method is proposed and proved. Style decision-making model.The integrated decision-making optimization model has a complex local extremum point structure, which requires that the solver has a strong global extremum point search capability.Therefore, Gaussian cruise particle swarm optimization algorithm is designed to 5 Group general purpose unconstrained and constrained optimization test functions are compared. The global extremum point search ability of Gaussian cruise particle swarm optimization algorithm and standard particle swarm optimization algorithm proposed in this paper are compared respectively. The results show that the proposed method The success rate of the algorithm is higher than that of the standard particle swarm optimization algorithm.For the 7-dimensional optimization test function with complex local extremum structure, the success rate of the proposed algorithm is still up to 80%, while the success rate of the standard particle swarm optimization algorithm is reduced Is 0, which proves that the proposed algorithm possesses strong searching ability. Especially in the high-dimensional space, it can be applied to the path planning of multi-degree-of-freedom robots.