Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario.In this study,we investigated the post-disaster rescue path planning probl...Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario.In this study,we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem(TSP)with life-strength constraints.To address this problem,we proposed an improved iterated greedy(IIG)algorithm.First,a push-forward insertion heuristic(PFIH)strategy was employed to generate a high-quality initial solution.Second,a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm’s exploration ability.Furthermore,three problem-specific swap operators were developed to improve the algorithm’s exploitation ability.Additionally,an improved simulated annealing(SA)strategy was used as an acceptance criterion to effectively prevent the algorithm from falling into local optima.To verify the effectiveness of the proposed algorithm,the Solomon dataset was extended to generate 27 instances for simulation.Finally,the proposed IIG was compared with five state-of-the-art algorithms.The parameter analysiswas conducted using the design of experiments(DOE)Taguchi method,and the effectiveness analysis of each component has been verified one by one.Simulation results indicate that IIGoutperforms the compared algorithms in terms of the number of rescue survivors and convergence speed,proving the effectiveness of the proposed algorithm.展开更多
This paper presents a human-like motion decision-making method for unmanned aerial vehicles(UAVs)navigating in trap environments.We proposed a space partitioning method based on sampling and consistency control to con...This paper presents a human-like motion decision-making method for unmanned aerial vehicles(UAVs)navigating in trap environments.We proposed a space partitioning method based on sampling and consistency control to conduct a preliminary analysis of the indoor environment based on architectural blueprints.This method reduces the dimensionality of the path planning problem,thereby enhancing the efficiency.Then,we designed a target-switching logic for the dynamic window approach.This improvement endows the UAV with the capability of both real-time obstacle avoidance and global navigation,enhancing the efficiency of the UAV in flying to task spots indoors.Additionally,by applying human-like methods of batch distance perception and obstacle perception to this scheme,we have further enhanced the robustness and efficiency of path decisions.Finally,considering the scenario of high-rise fire rescue,we conducted simulation verification.It demonstrates that our scheme enhances the efficiency and robustness of path planning.展开更多
矿井作业环境复杂,各类地质灾害以及水害极易影响井下安全生产,因而预先对灾害发生时的人员逃生路径进行规划很有必要。为获取矿井最短逃生路线,提出了一种改进灰狼优化算法的路径规划方法。该方法针对灰狼优化算法(Grey Wolf Optimizat...矿井作业环境复杂,各类地质灾害以及水害极易影响井下安全生产,因而预先对灾害发生时的人员逃生路径进行规划很有必要。为获取矿井最短逃生路线,提出了一种改进灰狼优化算法的路径规划方法。该方法针对灰狼优化算法(Grey Wolf Optimization,GWO)早熟收敛和易陷入局部最优解的不足,提出了一种基于Logistic映射和Tent映射组合的改进灰狼算法(LT-GWO),提高其全局搜索能力。结合矿井实际工作环境,将改进算法应用于井下逃生路径规划,并通过设定合理路径约束和限制条件,获得了较好的路径规划结果。研究表明:所提算法在平均路径长度、路径长度标准差、平均迭代次数和平均寻优耗时等指标上显著优于已有算法,并且具有较好的鲁棒性。所提算法对于矿井灾害等应急场景下的路径规划问题研究有一定的参考价值。展开更多
基金supported by the Opening Fund of Shandong Provincial Key Laboratory of Network based Intelligent Computing,the National Natural Science Foundation of China(52205529,61803192)the Natural Science Foundation of Shandong Province(ZR2021QE195)+1 种基金the Youth Innovation Team Program of Shandong Higher Education Institution(2023KJ206)the Guangyue Youth Scholar Innovation Talent Program support received from Liaocheng University(LCUGYTD2022-03).
文摘Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario.In this study,we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem(TSP)with life-strength constraints.To address this problem,we proposed an improved iterated greedy(IIG)algorithm.First,a push-forward insertion heuristic(PFIH)strategy was employed to generate a high-quality initial solution.Second,a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm’s exploration ability.Furthermore,three problem-specific swap operators were developed to improve the algorithm’s exploitation ability.Additionally,an improved simulated annealing(SA)strategy was used as an acceptance criterion to effectively prevent the algorithm from falling into local optima.To verify the effectiveness of the proposed algorithm,the Solomon dataset was extended to generate 27 instances for simulation.Finally,the proposed IIG was compared with five state-of-the-art algorithms.The parameter analysiswas conducted using the design of experiments(DOE)Taguchi method,and the effectiveness analysis of each component has been verified one by one.Simulation results indicate that IIGoutperforms the compared algorithms in terms of the number of rescue survivors and convergence speed,proving the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(Grant Nos.62033003,62373113,and U23A20341)the Natural Science Foundation of Guangdong Province(Grant Nos.2023A1515011527and 2022A1515011506)。
文摘This paper presents a human-like motion decision-making method for unmanned aerial vehicles(UAVs)navigating in trap environments.We proposed a space partitioning method based on sampling and consistency control to conduct a preliminary analysis of the indoor environment based on architectural blueprints.This method reduces the dimensionality of the path planning problem,thereby enhancing the efficiency.Then,we designed a target-switching logic for the dynamic window approach.This improvement endows the UAV with the capability of both real-time obstacle avoidance and global navigation,enhancing the efficiency of the UAV in flying to task spots indoors.Additionally,by applying human-like methods of batch distance perception and obstacle perception to this scheme,we have further enhanced the robustness and efficiency of path decisions.Finally,considering the scenario of high-rise fire rescue,we conducted simulation verification.It demonstrates that our scheme enhances the efficiency and robustness of path planning.
文摘矿井作业环境复杂,各类地质灾害以及水害极易影响井下安全生产,因而预先对灾害发生时的人员逃生路径进行规划很有必要。为获取矿井最短逃生路线,提出了一种改进灰狼优化算法的路径规划方法。该方法针对灰狼优化算法(Grey Wolf Optimization,GWO)早熟收敛和易陷入局部最优解的不足,提出了一种基于Logistic映射和Tent映射组合的改进灰狼算法(LT-GWO),提高其全局搜索能力。结合矿井实际工作环境,将改进算法应用于井下逃生路径规划,并通过设定合理路径约束和限制条件,获得了较好的路径规划结果。研究表明:所提算法在平均路径长度、路径长度标准差、平均迭代次数和平均寻优耗时等指标上显著优于已有算法,并且具有较好的鲁棒性。所提算法对于矿井灾害等应急场景下的路径规划问题研究有一定的参考价值。
