基于辐射扫描算法的机器人路径规划与仿真  

Traversal Path Planning and Simulation of Robot based on Radiation Scanning

作　　者：林彬[1] 韩光辉 宋晨晨 张雅静 Lin Bin;Han Guanghui;Song Chenchen;Zhang Yajing(College of Information Science and Technology,Dalian Maritime University,Dalian 116026,China)

机构地区：[1]大连海事大学信息科学技术学院,辽宁大连116026

出　　处：《系统仿真学报》2021年第1期84-90,共7页Journal of System Simulation

基　　金：国家自然科学基金(61971083,51939001);大连市科技创新基金重点学科重大课题(2019J11CY015);教育部产学合作协同育人项目(201802330004,201801225040,201902136002);大连海事大学校级教改项目(2020Y105)。

摘　　要：针对基于生物激励神经网络(Biologically Inspired Neural Network,BINN)算法的机器人路径存在重复率高、转弯次数多的问题,提出一种将模板模型与辐射扫描(Radiation Scanning,RS)算法相融合的改进遍历路径规划算法(Improved Traversal Path Planning Algorithm,ITPPA)。利用BINN算法制定无障碍物行走策略;设计多个避障路径模板,保证机器人有序的避开障碍物;利用RS算法引导机器人迅速逃离死区。仿真结果表明:与BINN算法相比,ITPPA能够有效降低路径重复率和转弯次数,同时帮助机器人快速逃离死区,降低机器人能耗,提高了工作效率。Aiming at the high repetition rate and many turns of robot paths based on BINN algorithm, an ITPPA combining the template model and the RS algorithm is proposed. The BINN algorithm is used to formulate the non-obstacle walking strategy. Multiple obstacle avoidance path templates are designed to ensure that the robot could avoid obstacles in an orderly manner. RS algorithm is used to guide the robot to escape the dead zone quickly. Simulation results show that, compared with BINN algorithm, ITPPA could not only effectively reduce the path repetition rate and the number of turning and energy consumption while helping the robot to quickly escape from the dead zone, but also improve the efficiency.

关 键 词：机器人 遍历路径规划 生物激励神经网络 模板模型 辐射扫描 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]