Application of Improved Ant Colony Algorithm in Complex Forest Land
To address the issue of optimal path planning for forest harvesting robots in complex forest terrain,this pa-per proposes an enhanced ant colony algorithm that overcomes the limitations of traditional approaches,including ex-cessive 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 phero-mone levels more effectively,enabling ants to converge on the optimal path more rapidly.Simulation results demon-strate that our algorithm exhibits superior optimization capabilities in complex forest road scenarios and can signifi-cantly assist in unmanned logging machinery's path planning in such environments.
path planningthe ant colony algorithmthe A*algorithman enhanced algorithmforest cutting