"RG-HDP-VD: A Physics-Aware Cooperative Trajectory Plan-ning Framework for Heterogeneous Multi-UAVs"

"This paper presents RG-HDP-VD, a physics-aware cooperative trajectory planning framework for heterogeneous UAV relief delivery in post-earthquake, non-convex canyon environments. RG-HDP-VD addresses two prevalent failure modes: energy-driven deadlocks caused by ignoring time-varying payload dynamics, and the collapse of feasible sets due to strict arrival windows in fixed-speed planning. We construct a mass-augmented energy topology and use a mass-augmented energy-aware A* search to extract baseline physical metrics\u2014path length, total energy, and unit-distance energy\u2014for each UAV. Regret-Guided (RG) arbitration then quantifies the relative energy cost of waiting versus detouring at conflicts and grants right-of-way to heavy-load, high-cost platforms. These priorities are embedded into Heuristic Decentralized Prioritized Planning (HDP), which maintains a global spatiotemporal occupancy map and serializes planning to eliminate deadlocks. To satisfy tight time windows, Velocity Decomposition (VD) maps 4D temporal constraints into a 3D path-length feasible interval and is realized via an improved VD-TSRRT* sampling-based planner. In 500 m \u00d7 300 m high-fidelity canyon simulations, the framework improves success from 0% to 100% and reduces computation time by ~45% in saturation-convergence tasks. In group delivery, it cuts total system energy by 6.7% and reduces heavy-load hovering energy by 15.0%, demonstrating robust and energy-aware fairness coordination."

本文提出RG-HDP-VD，一种面向震后非凸峡谷环境下异构无人机（UAV）救援配送的物理感知协同轨迹规划框架。RG-HDP-VD针对两类典型失效模式展开优化：一是因忽略时变有效载荷动力学特性引发的能耗驱动型死锁，二是固定速度规划中严格到达时间窗导致的可行域（feasible sets）崩塌。我们构建增广质量能量拓扑（mass-augmented energy topology），并采用增广质量能量感知A*搜索（energy-aware A* search）提取每架无人机的基准物理指标——路径长度、总能耗与单位距离能耗。随后，后悔引导（Regret-Guided, RG）仲裁模块对冲突场景下等待与绕行的相对能耗成本进行量化，并为重载高成本平台分配通行优先权。该优先权被嵌入启发式分布式优先规划（Heuristic Decentralized Prioritized Planning, HDP），该模块维护全局时空占用地图，并通过序列化规划消除死锁。为满足严苛的时间窗约束，速度分解（Velocity Decomposition, VD）将四维时间约束映射为三维路径长度可行区间，并通过改进的基于采样的VD-TSRRT*规划器实现。在500米×300米的高保真峡谷仿真实验中，该框架将饱和收敛任务的成功率从0%提升至100%，并将计算耗时降低约45%；在集群配送任务中，其系统总能耗降低6.7%，重载悬停能耗降低15.0%，展现出鲁棒且兼顾能耗公平的协同协调性能。