针对移动机器人在路径规划中存在初期容易出现搜索盲目性、转弯次数多、路径平滑性差、容易陷入局部最优等问题。提出一种基于多目标约束的机器人路径优化策略,该策略在传统蚁群算法基础之上进行改进,首先引入A*算法对邻近可行路径的信息素浓度进行加强,减少蚂蚁初期搜索盲目性,提高算法收敛速度；其次,改进距离启发函数,加入待选节点和目标节点距离关系,增强目标节点对于全局最优路径的导向性；再次,提出转弯启发函数,该函数由障碍物浓度约束函数,转弯约束函数,平滑性约束函数三者共同构成,将转弯启发函数加入到转移概率公式,引导蚂蚁向转弯次数更少,距离更短更平滑的路径前进；最后改进信息素挥发因子,有效使局部搜索和全局搜索的权重得到了很好的平衡,改进信息素更新规则,从路径长度和转弯次数两个方面对信息素更新设置奖惩机制,降低信息素冗余量,提高算法效率,同时对信息素浓度进行限制,防止算法陷入早熟的问题。仿真实验表明,在相同实验条件下,改进算法相较于其他算法,运行时间更短,最优路径长度更短,路径更平滑,收敛速度更快。

For mobile robots in path planning, there exist problems such as blind search in the initial stage, excessive number of turns, poor path smoothness, and easy trapping in local optimum. A robot path optimization strategy based on multi-objective constraints is proposed. This strategy is an improvement on the traditional ant colony algorithm. Firstly, the A* algorithm is introduced to enhance the pheromone concentration of adjacent feasible paths, reducing the blindness of the initial search and improving the convergence speed of the algorithm. Secondly, the distance heuristic function is improved by adding the distance relationship between the candidate node and the target node to enhance the guidance of the target node to the global optimal path. Thirdly, a turning heuristic function is proposed. This function is composed of the obstacle concentration constraint function, the turning constraint function, and the smoothness constraint function. The turning heuristic function is added to the transition probability formula to guide ants to move towards paths with fewer turns, shorter distance and smoother. Finally, the pheromone evaporation factor is improved, effectively balancing the weights of local search and global search. The pheromone update rule is also improved, and a reward and punishment mechanism is set for pheromone update from two aspects of path length and the number of turns, reducing the redundancy of pheromone and improving the algorithm efficiency. Meanwhile, the pheromone concentration is limited to prevent the algorithm from falling into premature problems. Simulation experiments show that under the same experimental conditions, compared with other algorithms, the improved algorithm has a shorter running time, a shorter optimal path length, a smoother path, and a faster convergence speed.

教育部产学合作协同育人项目(230902874185631,230803131304136);四川省高等学校人文社会科学重点研究基地-新建院校改革与发展研究中心项目(XJYX2023B05)；成都市交通+旅游大数据应用技术研究基地项目(2022107)；成都市哲学社会科学研究基地“新时代统一战线文化创新研究基地”项目(TZWC20233)