Autonomous and Adaptive Navigation for Terrestrial-Aerial Bimodal Vehicles

TL;DR

This paper introduces an autonomous navigation framework for terrestrial-aerial bimodal vehicles, enabling safe, energy-efficient traversal in unknown environments through hierarchical planning and adaptive control, validated by real-world experiments.

Contribution

It presents a novel hierarchical motion planner and unified controller for bimodal vehicles, enhancing autonomy, safety, and energy efficiency in complex environments.

Findings

Achieved 7x energy savings in terrestrial locomotion.

Successfully navigated complex environments with bimodal mobility.

Validated robustness through extensive real-world experiments.

Abstract

Terrestrial-aerial bimodal vehicles bloom in both academia and industry because they incorporate both the high mobility of aerial vehicles and the long endurance of ground vehicles. In this work, we present an autonomous and adaptive navigation framework to bring complete autonomy to this class of vehicles. The framework mainly includes 1) a hierarchical motion planner that generates safe and low-power terrestrial-aerial trajectories in unknown environments and 2) a unified motion controller which dynamically adjusts energy consumption in terrestrial locomotion. Extensive real-world experiments and benchmark comparisons are conducted on a customized robot platform to validate the proposed framework's robustness and performance. During the tests, the robot safely traverses complex environments with terrestrial-aerial integrated mobility, and achieves $7\times$ energy savings in terrestrial locomotion. Finally, we will release our code and hardware configuration for the reference of the community.