Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles

TL;DR

This paper introduces a safe local exploration method for micro-aerial vehicles that guarantees safety in cluttered unknown environments by combining conservative trajectory optimization with strategic exploration, validated through real-time experiments.

Contribution

A novel conservative local planner with exploration strategy that improves safety and efficiency in MAV navigation within cluttered unknown environments.

Findings

Outperforms optimistic global planners in simulations.

Handles local minima effectively with exploration strategy.

Operates in real time onboard MAVs at 4 Hz.

Abstract

In order to enable Micro-Aerial Vehicles (MAVs) to assist in complex, unknown, unstructured environments, they must be able to navigate with guaranteed safety, even when faced with a cluttered environment they have no prior knowledge of. While trajectory optimization-based local planners have been shown to perform well in these cases, prior work either does not address how to deal with local minima in the optimization problem, or solves it by using an optimistic global planner. We present a conservative trajectory optimization-based local planner, coupled with a local exploration strategy that selects intermediate goals. We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner, and also outperforms doing a single exploration step when the local planner is stuck. The method is validated through experiments in a variety of highly cluttered environments including a dense forest. These experiments show the complete system running in real time fully onboard an MAV, mapping and replanning at 4 Hz.