摘要
为了实现机器人在动态环境下沿最优路径安全行驶,提出了基于新型启发蚁群算法的路径规划和避撞策略。建立了机器人工作环境的栅格模型,给出了路径质量评价标准。基于蚁群算法,引入了信息素浓度的梯度初始化方法,有效提高了算法前期搜索效率;给出了一种用于平滑路径的新型启发信息,提出了新型启发蚁群算法并用于全局路径规划。在设定场景下,给定了障碍物检测方法与正面避撞、侧面避撞策略。经仿真验证,新型启发蚁群算法规划路径长度短、收敛次数少、平滑性好,且避撞策略可以有效躲避障碍物,保证机器人安全行驶。
In order to realize the safe driving of robot along the optimal path in dynamic environment,a path planning and collision avoidance strategy based on a new heuristic ant colony algorithm is proposed. The grid model of robot working environment is established,and the path quality evaluation standard is given. Based on ant colony algorithm,the gradient initialization method of pheromone concentration is introduced to effectively improve the early search efficiency of the algorithm;A new heuristic information for smoothing path is given,and a new heuristic ant colony algorithm is proposed for global path planning. In the set scene,the obstacle detection method and frontal collision avoidance and side collision avoidance strategies are given. The simulation results show that the new heuristic ant colony algorithm has the advantages of short path length,less convergence times and good smoothness,and the collision avoidance strategy can effectively avoid obstacles and ensure the safe driving of the robot.
作者
贺道坤
周南
HE Dao-kun;ZHOU Nan(Nanjing Vocational College of Information Technology,School of Intelligent Manufacturing,Jingsu Nanjing 210023,China;Gollege of Finance and Statistic,Hunan University,Hu*nan Changsha 410082,China;Hu5nan Business College,Changsha Commerce&Tourism College,Hu’nan Changsha 410116,China)
出处
《机械设计与制造》
北大核心
2023年第1期295-299,304,共6页
Machinery Design & Manufacture
基金
江苏省教改基金项目(2019JSJG557)。
关键词
路径规划
新型启发蚁群算法
动态环境
避撞策略
信息素初始化
Path Planning
New Inspiring Ant Colony Algorithm
Dynamic Environment
Anti-Collision Strategy
Pheromone Initialization
