Aerial-ground collaborative routing with time constraints

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.     

Sequential dynamic resource allocation in multi-beam satellite systems:A learning-based optimization method

Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources. One of the primary challenges accompanying with Multi-Beam Satellites(MBS) is an efficient Dynamic Resource Allocation(DRA) strategy. This paper presents a learning-based Hybrid-Action Deep Q-Network(HADQN) algorithm to address the sequential decision-making optimization problem in DRA. By using a parameterized hybrid action space,HADQN makes it possible to schedule the beam patte...     

Adaptive event-triggered distributed optimal guidance design via adaptive dynamic programming

In this paper, the multi-missile cooperative guidance system is formulated as a general nonlinear multi-agent system. To save the limited communication resources, an adaptive eventtriggered optimal guidance law is proposed by designing a synchronization-error-driven triggering condition, which brings together the consensus control with Adaptive Dynamic Programming(ADP) technique. Then, the developed event-triggered distributed control law can be employed by finding an approximate solution of eve...     

Model predictive control for close-proximity maneuvering of spacecraft with adaptive convexification of collision avoidance constraints

This study investigated model predictive control (MPC) for close-proximity maneuvering of spacecraft. It is essential for a designed MPC to effectively handle collision avoidance between the servicer spacecraft and the client spacecraft, especially while the client is rotating. The rotating motion of the client leads to dynamic changes in the collision avoidance constraints, which increases the difficulty of optimizing the control input in the MPC framework. Therefore, this study presents a method to improve the performance and computational efficiency of MPC for rendezvous and docking with a nonrotating or rotating client. An ellipsoid is adopted to model the client’s keep-out zone (KOZ). Given the spherical KOZ of the servicer, an expanded ellipsoid is introduced to describe the KOZ for the center of mass of the servicer and modeled as a nonlinear constraint. The linearization method for reference points located at the expanded ellipsoid is adopted to convexify the nonlinear constraints. The reference points are adaptively determined according to the positions of the servicer, client, and expanded ellipsoidal KOZ. The resulting hyperplanes are then used to describe the collision avoidance constraints. By utilizing the aforementioned strategies, combined with the calculated reference points, an adaptive convex programming algorithm suitable for real-time implementation of MPC is derived. The performance of the proposed method is demonstrated through numerical simulations.     

Smooth free-cycle dynamic soaring in unspecified shear wind via quadratic programming

Harvesting wind energy is promising for extending long-endurance flights, which can be greatly facilitated by a flight technique called dynamic soaring. The presented study is concerned with generating model-based trajectories with smooth control histories for dynamic soaring maneuvers exploiting wind gradients. The desired smoothness is achieved by introducing a trigonometric series parameterization for the controls, which are formulated with respect to the normalized time.Specifically, the per...