让无人机在未知环境中安全飞行，还能动态管理资源消耗
Autonomy Architectures for Safe Planning in Unknown Environments Under Budget Constraints
- 在线构建多棵反向RRT*树，从可重置点出发确保安全
- 在无卫星信号环境下，将定位误差控制在预算范围内
- 适合需要实时安全决策的无人机或机器人系统
任务规划常被建模为在多重路径约束（如安全约束）和预算约束（如资源消耗限制）下的约束控制问题。在事前未知的环境中，验证离线解在所有时间都满足约束可能极为困难甚至不可能。我们提出ReRoot，一种新型基于采样的框架，可在未知环境中对非线性系统施加安全与预算约束。核心思想是：从可重置集（即预算重新初始化的位置）出发，在线生长多棵反向RRT*树。这些动态可行的备用轨迹可保证安全并减少资源消耗，可作为门控器安全验证架构中的合理备用策略。我们在固定翼无人机于无GNSS环境中的仿真中验证了该方法，设定定位误差存在预算限制，并可在视觉地标处更新。
原文摘要 · Abstract (English)
Mission planning can often be formulated as a constrained control problem under multiple path constraints (i.e., safety constraints) and budget constraints (i.e., resource expenditure constraints). In a priori unknown environments, verifying that an offline solution will satisfy the constraints for all time can be difficult, if not impossible. We present ReRoot, a novel sampling-based framework that enforces safety and budget constraints for nonlinear systems in unknown environments. The main idea is that ReRoot grows multiple reverse RRT* trees online, starting from renewal sets, i.e., sets where the budget constraints are renewed. The dynamically feasible backup trajectories guarantee safety and reduce resource expenditure, which provides a principled backup policy when integrated into the gatekeeper safety verification architecture. We demonstrate our approach in simulation with a fixed-wing UAV in a GNSS-denied environment with a budget constraint on localization error that can be renewed at visual landmarks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。