With the rapid development of warehouse robots in logistics and other industries,research on their path planning has become increasingly important.Based on the analysis of various conflicts that occur when the warehou...With the rapid development of warehouse robots in logistics and other industries,research on their path planning has become increasingly important.Based on the analysis of various conflicts that occur when the warehouse robot travels,this study proposes a two-level vehicle path planning model for multi-warehouse robots,which integrates static and dynamic planning to improve operational efficiency and reduce operating costs.In the static phase,the blockage factor is introduced to enhance the ant colony optimization(ACO)algorithm as a negative feedback mechanism to effectively avoid the blockage nodes during movement.In the dynamic stage,a dynamic priority mechanism is designed to adjust the routing strategy in real time and give the optimal path according to the real situation.To evaluate the model’s effectiveness,simulations were performed under different operating environments and application strategies based on an actual grid environment map.The simulation results confirm that the proposed model outperforms other methods in terms of average running distance,number of blocked nodes,percentage of replanned paths,and average running time,showing great potential in optimizing warehouse operations.展开更多
As the adoption of Electric Vehicles(EVs)intensifies,two primary challenges emerge:limited range due to battery constraints and extended charging times.The traditional charging stations,particularly those near highway...As the adoption of Electric Vehicles(EVs)intensifies,two primary challenges emerge:limited range due to battery constraints and extended charging times.The traditional charging stations,particularly those near highways,exacerbate these issues with necessary detours,inconsistent service levels,and unpredictable waiting durations.The emerging technology of dynamic wireless charging lanes(DWCLs)may alleviate range anxiety and eliminate long charging stops;however,the driving speed on DWCL significantly affects charging efficiency and effective charging time.Meanwhile,the existing research has addressed load balancing optimization on Dynamic Wireless Charging(DWC)systems to a limited extent.To address this critical issue,this study introduces an innovative eco-driving speed control strategy,providing a novel solution to the multi-objective optimization problem of speed control on DWCL.We utilize mathematical programming methods and incorporate the longitudinal dynamics of vehicles to provide an accurate physical model of EVs.Three objective functions are formulated to tackle the challenges at hand:reducing travel time,increasing charging efficiency,and achieving load balancing on DWCL,which corresponds to four control strategies.The results of numerical tests indicate that a comprehensive control strategy,which considers all objectives,achieves a minor sacrifice in travel time reduction while significantly improving energy efficiency and load balancing.Furthermore,by defining the energy demand and speed range through an upper operation limit,a relatively superior speed control strategy can be selected.This work contributes to the discourse on DWCL integration into modern transportation systems,enhancing the EV driving experience on major roads.展开更多
基金funded by the Basic and Applied Research Foundation of Guangdong Province(2020A1515111024).
文摘With the rapid development of warehouse robots in logistics and other industries,research on their path planning has become increasingly important.Based on the analysis of various conflicts that occur when the warehouse robot travels,this study proposes a two-level vehicle path planning model for multi-warehouse robots,which integrates static and dynamic planning to improve operational efficiency and reduce operating costs.In the static phase,the blockage factor is introduced to enhance the ant colony optimization(ACO)algorithm as a negative feedback mechanism to effectively avoid the blockage nodes during movement.In the dynamic stage,a dynamic priority mechanism is designed to adjust the routing strategy in real time and give the optimal path according to the real situation.To evaluate the model’s effectiveness,simulations were performed under different operating environments and application strategies based on an actual grid environment map.The simulation results confirm that the proposed model outperforms other methods in terms of average running distance,number of blocked nodes,percentage of replanned paths,and average running time,showing great potential in optimizing warehouse operations.
基金funded by the National Natural Science Foundation of China(72201149)Xinjiang Key Laboratory of Green Mining of Coal resources,Ministry of Education(KLXGY-KB2420)Guangzhou Basic and Applied Basic Research(SL2023A04J00802).
文摘As the adoption of Electric Vehicles(EVs)intensifies,two primary challenges emerge:limited range due to battery constraints and extended charging times.The traditional charging stations,particularly those near highways,exacerbate these issues with necessary detours,inconsistent service levels,and unpredictable waiting durations.The emerging technology of dynamic wireless charging lanes(DWCLs)may alleviate range anxiety and eliminate long charging stops;however,the driving speed on DWCL significantly affects charging efficiency and effective charging time.Meanwhile,the existing research has addressed load balancing optimization on Dynamic Wireless Charging(DWC)systems to a limited extent.To address this critical issue,this study introduces an innovative eco-driving speed control strategy,providing a novel solution to the multi-objective optimization problem of speed control on DWCL.We utilize mathematical programming methods and incorporate the longitudinal dynamics of vehicles to provide an accurate physical model of EVs.Three objective functions are formulated to tackle the challenges at hand:reducing travel time,increasing charging efficiency,and achieving load balancing on DWCL,which corresponds to four control strategies.The results of numerical tests indicate that a comprehensive control strategy,which considers all objectives,achieves a minor sacrifice in travel time reduction while significantly improving energy efficiency and load balancing.Furthermore,by defining the energy demand and speed range through an upper operation limit,a relatively superior speed control strategy can be selected.This work contributes to the discourse on DWCL integration into modern transportation systems,enhancing the EV driving experience on major roads.
