多旋翼无人机的动态车辆定向问题
Dynamical Vehicle Orienteering Problem for Multi-Rotor Unmanned Aerial Vehicles
AI总结：
研究多旋翼无人机的动态车辆定向问题，提出结合非线性规划与混合整数线性规划的分支定界程序和大邻域搜索元启发式算法两种解决方案，实验表明改进显著，实际部署验证了所提质点模型解决方案轨迹。
AI中文摘要：
本文介绍了动态车辆定向问题（DVOP），它是定向问题（OP）的一种推广。OP在有限行程预算下最大化从空间目标收集的奖励，而DVOP通过考虑外力和车辆驱动力对其进行了扩展。我们在多旋翼无人机飞行规划的背景下研究DVOP，使用受最大速度和加速度大小约束且受重力加速度影响的三维质点模型（PMM），行程预算表示为最大飞行时间。由于DVOP将奖励最大化与时间最优轨迹规划相结合，无法简单地表述为图问题并精确求解。因此，我们提出了两种解决方案：一种是结合非线性规划（NLP）和混合整数线性规划（MILP）的分支定界（BnB）程序，以提供高质量的解决方案；另一种是大邻域搜索（LNS）元启发式算法，它提供初始奖励界限并可扩展到BnB难以处理的实例。BnB依赖于基于通过目标三元组的最短时间轨迹原语的行程成本的新颖MILP公式，产生紧密的奖励上限，而LNS使用有限推力分解来计算快速、高质量的PMM轨迹。在基准实例上的实验表明，相对于运动定向问题的现有解决方案，改进高达37%，并且在多旋翼无人机上的实际部署验证了所提出的PMM解决方案轨迹。
英文摘要：
This paper introduces the Dynamical Vehicle Orienteering Problem (DVOP), a generalization of the Orienteering Problem (OP). The OP maximizes the reward collected from spatial targets under a limited travel budget; the DVOP extends it by accounting for both external and vehicle-actuated forces. We study the DVOP in the context of multi-rotor Unmanned Aerial Vehicle (UAV) flight planning, using a three-dimensional Point-Mass Model (PMM) constrained by maximum velocity and acceleration magnitudes and subject to gravitational acceleration, with the travel budget expressed as a maximum flight time. Because the DVOP couples reward maximization with time-optimal trajectory planning, it cannot be formulated as a simple graph problem and solved exactly without relaxing or under-actuating the vehicle dynamics. We therefore propose two solution approaches: a Branch-and-Bound (BnB) procedure that combines Non-Linear Programming (NLP) and Mixed-Integer Linear Programming (MILP) to provide high-quality solutions, and a Large Neighborhood Search (LNS) metaheuristic that supplies an initial reward bound and scales to instances intractable for the BnB. The BnB relies on a novel MILP formulation of travel costs based on minimum-time trajectory primitives through target triplets, yielding a tight reward upper bound, while the LNS uses limited thrust decomposition to compute fast, high-quality PMM trajectories. Experiments on benchmark instances show improvements of up to 37 % over state-of-the-art solutions for the Kinematic Orienteering Problem, and a real-world deployment on a multi-rotor UAV verifies the proposed PMM solution trajectories.
