Uploaded March 2023 | Updated September 2026, 1 hour ago
Video for the IROS 2023 submission.
The video presents the experimental results of the paper "Model-Based Planning and Control for Terrestrial-Aerial Bimodal Vehicles with Passive Wheels".
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 bimodal 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 a 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.
Video for the IROS 2023 submission.
The video presents the experimental results of the paper "Model-Based Planning and Control for Terrestrial-Aerial Bimodal Vehicles with Passive Wheels".
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 bimodal 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 a 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.
![Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios [S2]](https://i.ytimg.com/vi/fBwW93Zq9ss/mqdefault.jpg "Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios [S2]
Video for the paper Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios.
[S2] - Simulations and benchmarks.")
