Hybrid Aerial-Ground Vehicle Autonomy in GPS-denied Environments
Tara Bartlett
TL;DR
The work addresses autonomous navigation in GPS-denied underground environments by developing a hybrid aerial-ground Rollocopter and a robust local planning stack that can operate without reliable localisation. The core approach combines instantaneous sensor data with a collision-avoidant local planner, wall-following capabilities in both ground and aerial modes, and a hybrid planning framework guided by a local map and A*-based global planning. Key contributions include a collision-avoidant local planner compatible with rolling and flying modes, a wall-following strategy that enables navigation without SLAM, a traversability analysis concept to inform mode transitions, and a low-fidelity unit-test simulator to validate autonomy in diverse tunnels. The results demonstrate robust navigation through dusty tunnels, horizontal mazes, and rough terrain, highlighting practical implications for subterranean exploration and future planetary cave missions. The integrated toolkit, including a unit-test simulator and safety framework, positions the Rollocopter as a versatile platform for long-duration autonomous missions in GPS-denied, harsh environments.
Abstract
The DARPA Subterranean Challenge is leading the development of robots capable of mapping underground mines and tunnels up to 8km in length and identify objects and people. Developing these autonomous abilities paves the way for future planetary cave and surface exploration missions. The Co-STAR team, competing in this challenge, is developing a hybrid aerial-ground vehicle, known as the Rollocopter. The current design of this vehicle is a drone with wheels attached. This allows for the vehicle to roll, actuated by the propellers, and fly only when necessary, hence benefiting from the reduced power consumption of the ground mode and the enhanced mobility of the aerial mode. This thesis focuses on the development and increased robustness of the local planning architecture for the Rollocopter. The first development of thesis is a local planner capable of collision avoidance. The local planning node provides the basic functionality required for the vehicle to navigate autonomously. The next stage was augmenting this with the ability to plan more reliably without localisation. This was then integrated with a hybrid mobility mode capable of rolling and flying to exploit power and mobility benefits of the respective configurations. A traversability analysis algorithm as well as determining the terrain that the vehicle is able to traverse is in the late stages of development for informing the decisions of the hybrid planner. A simulator was developed to test the planning algorithms and improve the robustness of the vehicle to different environments. The results presented in this thesis are related to the mobility of the rollocopter and the range of environments that the vehicle is capable of traversing. Videos are included in which the vehicle successfully navigates through dust-ridden tunnels, horizontal mazes, and areas with rough terrain.