通过可达集实现神经辐射场的安全导航
Safe Navigation using Neural Radiance Fields via Reachable Sets
AI总结：
本文提出利用可达集和神经辐射场进行安全导航，通过约束最优控制解决路径规划问题，验证了在复杂环境中安全避障的能力。
摘要：
在复杂环境中实现安全导航是自主系统的重要挑战。机器人在障碍物、目标和自身物体存在的情况下需能安全导航。本文利用可达集表示机器人在状态空间中的实时能力，以捕捉安全导航需求。神经辐射场（NeRF）用于计算、存储和操作障碍物或自身车辆的体素表示。约束最优控制用于表示路径规划问题，涉及线性矩阵不等式约束。本文展示了在两种不同场景中存在众多障碍物时的路径规划仿真结果。通过在相应的约束最优控制问题中使用可达集，实现了安全导航。
英文摘要：
Safe navigation in cluttered environments is an important challenge for autonomous systems. Robots navigating through obstacle ridden scenarios need to be able to navigate safely in the presence of obstacles, goals, and ego objects of varying geometries. In this work, reachable set representations of the robot's real-time capabilities in the state space can be utilized to capture safe navigation requirements. While neural radiance fields (NeRFs) are utilized to compute, store, and manipulate the volumetric representations of the obstacles, or ego vehicle, as needed. Constrained optimal control is employed to represent the resulting path planning problem, involving linear matrix inequality constraints. We present simulation results for path planning in the presence of numerous obstacles in two different scenarios. Safe navigation is demonstrated through using reachable sets in the corresponding constrained optimal control problems.
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。
