Model-Based Planning and Control for Terrestrial-Aerial Bimodal Vehicles with Passive Wheels

Abstract

Terrestrial and aerial bimodal vehicles have gained widespread attention due to their cross-domain maneuverability. Nevertheless, their bimodal dynamics significantly increase the complexity of motion planning and control, thus hindering robust and efficient autonomous navigation in unknown environments. To resolve this issue, we develop a model-based planning and control framework for terrestrial aerial bi-modal vehicles. This work begins by deriving a unified dynamic model and the corresponding differential flatness. Leveraging differential flatness, an optimization-based trajectory planner is proposed, which takes into account both solution quality and computational efficiency. Moreover, we design a tracking controller using nonlinear model predictive control based on the proposed unified dynamic model to achieve accurate trajectory tracking and smooth mode transition. We validate our framework through extensive benchmark comparisons and experiments, demonstrating its effectiveness in terms of planning quality and control performance.

Figures (8)

• Figure 1: The real-world experiments. a) An autonomous navigation test in an unknown dense environment. b) A Terrestrial-aerial hybrid trajectory tracking test in which the maximal commanded velocity and acceleration reach $3m/s$ and $2.5m/s^2$, respectively.
• Figure 2: A diagram of the proposed planning and control framework.
• Figure 3: Illustration of the reference frames. Three frames are introduced: inertial frame ($\boldsymbol{x}_I - \boldsymbol{y}_I-\boldsymbol{z}_I$) with $\boldsymbol{Z}_I$ pointing in the opposite direction of the gravity vector, body frame ($\boldsymbol{x}_B - \boldsymbol{y}_B-\boldsymbol{z}_B)$) with $\boldsymbol{z}_B$ aligned with the rotor thrust vector, and intermediate frame ($\boldsymbol{x}_C - \boldsymbol{y}_C-\boldsymbol{z}_C$) which is separated from the inertial frame by the yaw rotation $\psi$.
• Figure 4: Illustration of the customized TABV platform.
• Figure 5: The trajectory planning benchmark comparison. a) The experimental setting and the given goals. b) The planned and the actual trajectories with the proposed and the previous trajectory plannerszhang2022autonomous, respectively.