With the rapid development of low-altitude economy and unmanned aerial vehicles (UAVs) deployment technology, aerial-ground collaborative delivery (AGCD) is emerging as a novel mode of last-mile delivery, where the ve...With the rapid development of low-altitude economy and unmanned aerial vehicles (UAVs) deployment technology, aerial-ground collaborative delivery (AGCD) is emerging as a novel mode of last-mile delivery, where the vehicle and its onboard UAVs are utilized efficiently. Vehicles not only provide delivery services to customers but also function as mobile ware-houses and launch/recovery platforms for UAVs. This paper addresses the vehicle routing problem with UAVs considering time window and UAV multi-delivery (VRPU-TW&MD). A mixed integer linear programming (MILP) model is developed to mini-mize delivery costs while incorporating constraints related to UAV energy consumption. Subsequently, a micro-evolution aug-mented large neighborhood search (MEALNS) algorithm incor-porating adaptive large neighborhood search (ALNS) and micro-evolution mechanism is proposed. Numerical experiments demonstrate the effectiveness of both the model and algorithm in solving the VRPU-TW&MD. The impact of key parameters on delivery performance is explored by sensitivity analysis.展开更多
In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of ...In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of the system,we add a channel-separated attention head to a deep ReLU neural network to predict disturbances from ground effects,motor torques and rotation axis shift.The proposed neural network is Lipschitz continuous,has fewer parameters and performs better for disturbance estimation than the baseline deep ReLU neural network.Then,we design a sliding mode controller using these predictions and establish its input-to-state stability and error bounds.Experiments show improvements of the proposed neural network in training speed and robustness over a baseline ReLU network,and a 40%reduction in tracking error compared to a baseline PID controller.展开更多
The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage.To reach a balance between green transportation and competitiv...The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage.To reach a balance between green transportation and competitive edge,the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery,where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily services.In this paper,we propose the Vehicle Routing Problem with Drones under Time constraints(VRPD-T)and focus on the time constraints involved in realistic scenarios during the delivery.A mixed-integer linear programming model has been developed to minimize the total delivery completion time.Furthermore,to overcome the limitations of standard solvers in handling large-scale complex issues,a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality solution.The numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.展开更多
In this paper,an algorithm for solving the multi-target correlation and co-location problem of aerial-ground heterogeneous system is investigated.Aiming at the multi-target correlation problem,the fusion algorithm of ...In this paper,an algorithm for solving the multi-target correlation and co-location problem of aerial-ground heterogeneous system is investigated.Aiming at the multi-target correlation problem,the fusion algorithm of visual axis correlation method and improved topological similarity correlation method are adopted in view of large parallax and inconsistent scale between the aerial and ground perspectives.First,the visual axis was preprocessed by the threshold method,so that the sparse targets were initially associated.Then,the improved topological similarity method was used to further associate dense targets with the relative position characteristics between targets.The shortcoming of dense target similarity with small di®erence was optimized by the improved topological similarity method.For the problem of colocation,combined with the multi-target correlation algorithm in this paper,the triangulation positioning model was used to complete the co-location of multiple targets.In the experimental part,simulation experiments and°ight experiments were designed to verify the e®ectiveness of the algorithm.Experimental results show that the proposed algorithm can e®ectively achieve multi-target correlation positioning,and that the positioning accuracy is obviously better than other positioning methods.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(2024JBZX038)the National Natural Science Foundation of China(62076023).
文摘With the rapid development of low-altitude economy and unmanned aerial vehicles (UAVs) deployment technology, aerial-ground collaborative delivery (AGCD) is emerging as a novel mode of last-mile delivery, where the vehicle and its onboard UAVs are utilized efficiently. Vehicles not only provide delivery services to customers but also function as mobile ware-houses and launch/recovery platforms for UAVs. This paper addresses the vehicle routing problem with UAVs considering time window and UAV multi-delivery (VRPU-TW&MD). A mixed integer linear programming (MILP) model is developed to mini-mize delivery costs while incorporating constraints related to UAV energy consumption. Subsequently, a micro-evolution aug-mented large neighborhood search (MEALNS) algorithm incor-porating adaptive large neighborhood search (ALNS) and micro-evolution mechanism is proposed. Numerical experiments demonstrate the effectiveness of both the model and algorithm in solving the VRPU-TW&MD. The impact of key parameters on delivery performance is explored by sensitivity analysis.
文摘In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of the system,we add a channel-separated attention head to a deep ReLU neural network to predict disturbances from ground effects,motor torques and rotation axis shift.The proposed neural network is Lipschitz continuous,has fewer parameters and performs better for disturbance estimation than the baseline deep ReLU neural network.Then,we design a sliding mode controller using these predictions and establish its input-to-state stability and error bounds.Experiments show improvements of the proposed neural network in training speed and robustness over a baseline ReLU network,and a 40%reduction in tracking error compared to a baseline PID controller.
基金supported by the National Natural Science Foundation of China(No.61961146005)。
文摘The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage.To reach a balance between green transportation and competitive edge,the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery,where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily services.In this paper,we propose the Vehicle Routing Problem with Drones under Time constraints(VRPD-T)and focus on the time constraints involved in realistic scenarios during the delivery.A mixed-integer linear programming model has been developed to minimize the total delivery completion time.Furthermore,to overcome the limitations of standard solvers in handling large-scale complex issues,a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality solution.The numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61876187 and 61806217.
文摘In this paper,an algorithm for solving the multi-target correlation and co-location problem of aerial-ground heterogeneous system is investigated.Aiming at the multi-target correlation problem,the fusion algorithm of visual axis correlation method and improved topological similarity correlation method are adopted in view of large parallax and inconsistent scale between the aerial and ground perspectives.First,the visual axis was preprocessed by the threshold method,so that the sparse targets were initially associated.Then,the improved topological similarity method was used to further associate dense targets with the relative position characteristics between targets.The shortcoming of dense target similarity with small di®erence was optimized by the improved topological similarity method.For the problem of colocation,combined with the multi-target correlation algorithm in this paper,the triangulation positioning model was used to complete the co-location of multiple targets.In the experimental part,simulation experiments and°ight experiments were designed to verify the e®ectiveness of the algorithm.Experimental results show that the proposed algorithm can e®ectively achieve multi-target correlation positioning,and that the positioning accuracy is obviously better than other positioning methods.
