改进蚁群算法的森林采伐移动机器人路径规划  

Application of Improved Ant Colony Algorithm in Complex Forest Land

作　　者：徐海宁 罗梓桐 XU Hai-ning;LUO Zi-tong(College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin Heilongjiang 150040,China)

机构地区：[1]东北林业大学机电工程学院,黑龙江哈尔滨150040

出　　处：《林业机械与木工设备》2024年第9期84-90,96,共8页Forestry Machinery & Woodworking Equipment

摘　　要：为了解决森林采伐机器人在森林复杂地形的最优路径规划问题,针对传统蚁群算法在路径规划中存在路径过长、收敛速度慢、盲目搜索等缺陷,提出了一种改进的蚁群算法。该算法改变启发函数使信息素分布不均以缩小蚁群算法搜索范围,并结合高信息素浓度来引导蚂蚁进行搜索。此外,通过引入平滑因子和目标节点的代价,使算法在路径拐点上有较大优化,减少了能耗问题。同时引入安全因子,以避免算法陷入局部最优。此外,还改变了更新信息素的规则,使蚂蚁能够更快地向最优路径收敛。仿真结果表明,该算法在复杂森林道路场景下表现出较为明显的优化能力,可以极大地辅助无人驾驶采伐机械在此类环境下的路径规划。To address the issue of optimal path planning for forest harvesting robots in complex forest terrain,this paper proposes an enhanced ant colony algorithm that overcomes the limitations of traditional approaches,including excessive path length,slow convergence speed,and blind search.The proposed algorithm combines the A*algorithm for optimal path planning with high pheromone concentration to guide ants during their search process.Furthermore,by incorporating the cost of the next node and target node into consideration,we enhance the adaptive adjustment of the heuristic function and improve search efficiency.Additionally,regulatory factors are introduced to update pheromone levels more effectively,enabling ants to converge on the optimal path more rapidly.Simulation results demonstrate that our algorithm exhibits superior optimization capabilities in complex forest road scenarios and can significantly assist in unmanned logging machinery's path planning in such environments.

关 键 词：路径规划 蚁群算法 A*算法 改进算法 森林采伐 

分 类 号：S776[农业科学—森林工程] TP242[农业科学—林学]