Abstract
Safety patrol inspection in chemical industrial parks is a complex multi-objective task with mul-tiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false posi-tives/negatives and poor timeliness.To address the above problems,this study proposes a path plan-ning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Fur-ther,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantifica-tion cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment.
基金项目
国家重点研发计划(2021YFE0105000)
国家自然科学基金(52074213)
Shaanxi Key Research and Development Plan Project(2021SF-472)
Yulin Science and Technology Plan Project(CXY-2020-036)
国家科技期刊平台
NSTL
万方数据