Safe Navigation using Neural Radiance Fields via Reachable Sets

TL;DR

This paper introduces a method for safe robot navigation in cluttered environments by combining neural radiance fields with reachable set representations and constrained optimal control, demonstrated through simulations.

Contribution

It integrates neural radiance fields with reachable sets and optimal control for real-time safe navigation in obstacle-rich scenarios, a novel combination.

Findings

Successful path planning in simulation with multiple obstacles

Demonstrated safety through reachable set constraints

Effective use of NeRFs for volumetric obstacle representation

Abstract

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.