用可达集提升机器人在复杂环境中的安全路径规划能力
Safe Navigation using Neural Radiance Fields via Reachable Sets
- 基于可达集建模机器人实时运动能力，结合约束优化求解路径
- 在多障碍场景中实现安全避障，路径规划满足严格几何约束
- 适合自动驾驶与机器人导航研究者，尤其关注安全性设计
在复杂环境中实现安全导航是自主系统的重要挑战。机器人在充满障碍物的场景中需具备在障碍物、目标点及不同几何形态自身物体存在下的安全导航能力。本文利用可达集表征机器人在状态空间中的实时能力，以捕捉安全导航需求。同时，采用神经辐射场（NeRF）来计算、存储和操作障碍物或自车的体素化表示。通过引入线性矩阵不等式约束的约束最优控制方法，建立路径规划问题。在两种不同场景下对大量障碍物环境进行了仿真，结果表明：通过在相应的约束优化问题中使用可达集，可有效实现安全导航。
原文摘要 · Abstract (English)
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.
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