HALO: Hazard-Aware Landing Optimization for Autonomous Systems

TL;DR

This paper introduces HALO, a hazard-aware landing optimization system for autonomous vehicles that combines perception and planning algorithms to improve landing safety and efficiency in uncertain environments.

Contribution

The paper presents two novel algorithms, HALSS and Adaptive-DDTO, for hazard detection and adaptive trajectory planning in autonomous landing scenarios.

Findings

Achieves higher landing success rates in simulated Martian environments.

Demonstrates improved fuel efficiency over nonadaptive planning methods.

Effectively identifies safe landing zones using point cloud data.

Abstract

With autonomous aerial vehicles enacting safety-critical missions, such as the Mars Science Laboratory Curiosity rover's landing on Mars, the tasks of automatically identifying and reasoning about potentially hazardous landing sites is paramount. This paper presents a coupled perception-planning solution which addresses the hazard detection, optimal landing trajectory generation, and contingency planning challenges encountered when landing in uncertain environments. Specifically, we develop and combine two novel algorithms, Hazard-Aware Landing Site Selection (HALSS) and Adaptive Deferred-Decision Trajectory Optimization (Adaptive-DDTO), to address the perception and planning challenges, respectively. The HALSS framework processes point cloud information to identify feasible safe landing zones, while Adaptive-DDTO is a multi-target contingency planner that adaptively replans as new perception information is received. We demonstrate the efficacy of our approach using a simulated Martian environment and show that our coupled perception-planning method achieves greater landing success whilst being more fuel efficient compared to a nonadaptive DDTO approach.