基于细菌觅食行为的移动机器人动态路径规划

将自然界中细菌的自适应觅食现象与移动机器人动态路径规划相类比,设计基于细菌最优觅食理论的新型生物启发计算方法(DBFO)。通过对无约束复杂动态多峰测试函数库测试,证实DBFO算法具有较高的准确性和稳定性,具备动态优化能力。并以Sphere函数作为机器人路径寻优的仿真测试环境,DBFO算法驱动的搜索主体可以顺利避开障碍并快速找到目标地点,有效节约了行走时间,验证了其是一种高效、稳定、有竞争力的仿生智能优化方法,在求解实际复杂工程优化问题中体现了极为优越的搜索效率和求解精度。

Mobile robot dynamic path planning based on bacterial foraging behavior

Dynamic Optimization Problem (DOP) is a kind of problems with dynamic fitness functions, problem instances and limiting conditions. Mobile robot dynamic path planning must face dynamic variations of the environment, which is also a typical DOP. By analogizing the natural phenomena of bacterial adaptive foraging and mobile robot dynamic path planning, a Dynamic Bacterial Foraging Optimization (DBFO) is proposed. A complex dynamic multimodal test function is adopted to test its performance. The results show that DBFO exhibits a high accuracy, stabilization and an ability of dynamic optimization. Using the Sphere function as the simulation environment for Robot path planning optimization, searching subject driven by DBFO can smoothly avoid obstacles, quickly find the target site and effectively saves walking time. The bionic intelligent optimization approach is effective, stable, and competitive, and it is good for the searching efficiency and solution accuracy in the solving of complex engineering optimization.