B样条曲线融合蚁群算法的机器人路径规划

针对蚁群算法在静态环境下全局路径规划存在无法找到最短路径、收敛速度慢、路径搜索盲目性大、拐点多等问题，提出一种改进蚁群算法。以栅格地图为机器人运行环境，对初始信息素进行非均匀分布，使路径搜索更倾向于起点和目标点的连线附近；把当前节点、下一节点和目标点的信息加入启发式函数，同时引入动态调节因子，促使启发函数在迭代前期起主导作用，而后期则加强信息素引导；引入伪随机转移策略，以减少路径选择的盲目性，加快找到最短路径；动态调整挥发系数，使得前期挥发系数大，后期较小，从而避免算法陷入早熟；在最优解的基础上，引入B样条曲线平滑策略，以进一步优化最优解，使得到的路径更短且更加平滑。对改进算法的主要参数进行敏感性分析，并对该算法的各改进环节的可行性与有效性进行了实验，而且在20×20和50×50环境下与传统蚁群算法及其他改进蚁群算法进行仿真对比，实验结果验证了改进算法的可行性、有效性和优越性。

Robot path planning based on B-spline curve and ant colony algorithm

In view of the problems of ant colony algorithm in global path planning under static environment， such as being unable to find the shortest path， slow convergence speed， great blindness of path search and many inflection points， an improved ant colony algorithm was proposed. Taking the grid map as the running environment of the robot， the initial pheromones were distributed unevenly， so that the path search tended to be near the line between the starting point and the target point； the information of the current node， the next node and the target point was added into the heuristic function， and the dynamic adjustment factor was introduced at the same time， so as to achieve the purpose of strong guidance of the heuristic function in the early stage and strengthening the guidance of pheromone in the later stage； the pseudo-random transfer strategy was introduced to reduce the blindness of path selection and speed up finding the shortest path； the volatilization coefficient was adjusted dynamically to make the volatilization coefficient larger in the early stage and smaller in the later stage， avoiding premature convergence of the algorithm； based on the optimal solution， B-spline curve smoothing strategy was introduced to further optimize the optimal solution， resulting in shorter and smoother path. The sensitivity analysis of the main parameters of the improved algorithm was conducted， the feasibility and effectiveness of each improved step of the algorithm were tested， the simulations compared with the traditional ant colony algorithm and other improved ant colony algorithms under 20×20 and 50×50 environments were given， and the experimental results verified the feasibility， effectiveness and superiority of the improved algorithm.