迈向可靠的空地车辆协作：用于实地部署的集成规划与自主框架
Towards Reliable Aerial Ground Vehicle Collaboration: An Integrated Planning and Autonomy Framework for Field Deployment
AI总结：
研究针对无人机长时间任务飞行续航受限问题，提出集成规划与自主框架。通过深度强化学习规划器解决协同路由问题，引入标准化API弥合规划与执行差距，还有轻量级重新规划器，经实验验证系统在动态任务中有强大协同导航和适应性。
摘要：
有限的飞行续航能力严重限制了无人机在诸如监视和检查等长时间任务中的操作范围，此类任务需高效路由以确定访问顺序，这直接影响任务时间、能耗和整体可行性。将无人机与无人地面车辆配对进行移动充电是一个有前景的解决方案，但引入了一个紧密耦合的协同路由问题，涉及无人机路线规划、无人地面车辆道路受限移动、能量管理和不确定性下的会合调度。在这项工作中，我们提出了一个用于可靠实地部署的集成规划与自主框架。我们将问题表述为能量受限的协同路由任务，并使用基于深度强化学习的规划器来解决它，该规划器联合优化无人机访问顺序和与无人地面车辆的会合位置，在最小化总任务时间方面优于基线启发式方法。为弥合规划与执行之间的差距，我们引入了一个基于标准化两层YAML的任务API，它捕获环境状态并构建轻量级、同步的动作序列。该框架由一个完整的自主堆栈支持，使用PX4/MAVSDK进行无人机控制，ROS 2/Nav2进行无人地面车辆导航。此外，我们提出了一种轻量级的会合感知重新规划器（RARP），它在线运行以处理环境不确定性，将能量裕度违规从83.33%降低到20.00%。完整系统通过户外实地实验进行了验证，展示了在动态任务中的强大协同导航和适应性，包括基于视觉语言模型（VLM）的危险检测的搜索和救援场景。
英文摘要：
Limited flight endurance significantly restricts the operational range of unmanned aerial vehicles (UAVs) in long duration missions such as surveillance and inspection, where multiple spatially distributed Areas of Interest (AOIs) must be visited. These tasks require efficient routing determining the sequence of visits which directly impacts mission time, energy consumption, and overall feasibility. Pairing UAVs with unmanned ground vehicles (UGVs) for mobile recharging offers a promising solution, but introduces a tightly coupled cooperative routing problem involving UAV route planning, UGV road constrained movement, energy management, and rendezvous scheduling under uncertainty. In this work, we present an integrated planning and autonomy framework for reliable field deployment. We formulate the problem as an energy constrained cooperative routing task and solve it using a Deep Reinforcement Learning (DRL) based planner that jointly optimizes the UAV visitation sequence and rendezvous locations with the UGV, outperforming baseline heuristics in minimizing total mission time. To bridge the gap between planning and execution, we introduce a standardized two layer YAML based mission API that captures environment states and structures lightweight, synchronized action sequences. This framework is supported by a complete autonomy stack using PX4/MAVSDK for UAV control and ROS 2/Nav2 for UGV navigation. Furthermore, we propose a lightweight Rendezvous Aware Replanner (RARP) that operates online to handle environmental uncertainties, reducing energy margin violations from 83.33% to 20.00%. The full system is validated through outdoor field experiments, demonstrating robust cooperative navigation and adaptability in dynamic tasks, including a search and rescue scenario with vision language model (VLM) based hazard detection
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
