Drones have gradually been employed to search for unknown sources during leakage accidents.However,current studies have mainly focused on the single-source search problem,while in practical situations,the location and...Drones have gradually been employed to search for unknown sources during leakage accidents.However,current studies have mainly focused on the single-source search problem,while in practical situations,the location and quantity of the sources are commonly unknown.Existing multi-source search methods fail to accurately estimate the source term,primarily due to the inefficient utilization of concentration information.This limitation results in sub-optimal drone movement strategies.To address these issues,we propose a Dynamic Likelihood-Weighted Cooperative Infotaxis(DLW-CI)approach.The approach integrates the Infotaxis cognitive search strategy with multi-drone cooperation by optimizing both source term estimation and the cooperative mechanism.Specifically,we devise a novel source term estimation method that leverages multiple parallel particle filters,with each filter estimating the parameters of a potentially unknown source in scenarios.Subsequently,we introduce a cooperative mechanism based on dynamic likelihood weight to prevent multiple drones from concurrently estimating and searching for the same source.The results show that the success rate for the localization of 2-4 diffusion sources reaches 90%,78%,and 42% respectively when employing the DLW-CI approach,achieving a 37%average improvement over baseline methods.Our findings indicate that the proposed DLW-CI approach significantly improves estimation accuracy and search efficiency for multi-drone cooperative multi-source search,making a valuable contribution to environmental safety monitoring applications.展开更多
During the scenarios of cooperative tasks performed by a single truck and multiple drones,the route plan is prone to failure due to the unpredictable scenario change.In this situation,it is significant to replan the r...During the scenarios of cooperative tasks performed by a single truck and multiple drones,the route plan is prone to failure due to the unpredictable scenario change.In this situation,it is significant to replan the rendezvous route of the truck and drones as soon as possible,to ensure that all drones in flight can return to the truck before running out of energy.This paper addresses the problem of rendezvous route planning of truck and multi-drone.Due to the available time window constraints of drones,which limit not only the rendezvous time of the truck and drones but also the available period of each drone,there are obvious local optimum phenomena in the investigated problem,so it is difficult to find a feasible solution.A two-echelon heuristic algorithm is proposed.In the algorithm,the strategy jumping out of the local optimum and the heuristic generating the initial solution are introduced,to improve the probability and speed of obtaining a feasible solution for the rendezvous route.Simulation results show that the feasible solution of the truck-drones rendezvous route can be obtained with 88%probability in an average of 77 iterations for the scenario involving up to 25 drones.The influence of algorithm options on planning results is also analyzed.展开更多
为更好地完成大面积区域的全覆盖扫描,提出一种多车多无人机协同区域搜索模式。构建考虑卡车行驶路程限制与无人机总量限制的多车多无人机协同区域搜索模型,设计一种融合退火机制、禁忌策略与自适应大邻域搜索的三阶段优化算法(a three ...为更好地完成大面积区域的全覆盖扫描,提出一种多车多无人机协同区域搜索模式。构建考虑卡车行驶路程限制与无人机总量限制的多车多无人机协同区域搜索模型,设计一种融合退火机制、禁忌策略与自适应大邻域搜索的三阶段优化算法(a three stage optimization algorithm integrating annealing mechanism,tabu strategy,and adaptive large neighborhood search,ALNSAWPT),通过区域划分、车辆路径规划、无人机路径规划来求解该问题。在第一阶段,设计基于网格的区域划分方法将大面积区域划分为多个搜索任务。在第二阶段,将搜索任务分配给卡车并生成卡车的路径规划方案。由此,原问题简化为多组单车多无人机协同区域搜索问题。在第三阶段,为每个卡车上的无人机分派搜索任务并生成搜索路径规划方案。实验结果表明,ALNSAWPT明显优于其他5种对比算法,且多车多无人机协同区域搜索模式的效率明显优于单车多无人机区域搜索模式,证明了所提算法的有效性。展开更多
基金supported by the National Natural Science Foundation of China 62173337Youth Independent Innovation Foundation of NUDT(ZK-2023-21).
文摘Drones have gradually been employed to search for unknown sources during leakage accidents.However,current studies have mainly focused on the single-source search problem,while in practical situations,the location and quantity of the sources are commonly unknown.Existing multi-source search methods fail to accurately estimate the source term,primarily due to the inefficient utilization of concentration information.This limitation results in sub-optimal drone movement strategies.To address these issues,we propose a Dynamic Likelihood-Weighted Cooperative Infotaxis(DLW-CI)approach.The approach integrates the Infotaxis cognitive search strategy with multi-drone cooperation by optimizing both source term estimation and the cooperative mechanism.Specifically,we devise a novel source term estimation method that leverages multiple parallel particle filters,with each filter estimating the parameters of a potentially unknown source in scenarios.Subsequently,we introduce a cooperative mechanism based on dynamic likelihood weight to prevent multiple drones from concurrently estimating and searching for the same source.The results show that the success rate for the localization of 2-4 diffusion sources reaches 90%,78%,and 42% respectively when employing the DLW-CI approach,achieving a 37%average improvement over baseline methods.Our findings indicate that the proposed DLW-CI approach significantly improves estimation accuracy and search efficiency for multi-drone cooperative multi-source search,making a valuable contribution to environmental safety monitoring applications.
基金supported by Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515011313)in part by Guangdong Innovative and Entrepreneurial Research Team Program(Grant No.2019ZT08Z780)in part by Dongguan Introduction Program of Leading Innovative and Entrepreneurial Talents。
文摘During the scenarios of cooperative tasks performed by a single truck and multiple drones,the route plan is prone to failure due to the unpredictable scenario change.In this situation,it is significant to replan the rendezvous route of the truck and drones as soon as possible,to ensure that all drones in flight can return to the truck before running out of energy.This paper addresses the problem of rendezvous route planning of truck and multi-drone.Due to the available time window constraints of drones,which limit not only the rendezvous time of the truck and drones but also the available period of each drone,there are obvious local optimum phenomena in the investigated problem,so it is difficult to find a feasible solution.A two-echelon heuristic algorithm is proposed.In the algorithm,the strategy jumping out of the local optimum and the heuristic generating the initial solution are introduced,to improve the probability and speed of obtaining a feasible solution for the rendezvous route.Simulation results show that the feasible solution of the truck-drones rendezvous route can be obtained with 88%probability in an average of 77 iterations for the scenario involving up to 25 drones.The influence of algorithm options on planning results is also analyzed.
