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

Główne pojęcia

The author presents a unified model-based planning and control framework for terrestrial-aerial bimodal vehicles, focusing on differential flatness to ensure dynamic feasibility and smooth mode transitions.

Streszczenie

The content discusses the challenges of trajectory planning and motion control for terrestrial-aerial bimodal vehicles. It introduces a model-based planning and control framework that leverages differential flatness for accurate trajectory tracking and seamless mode transition. Extensive experiments validate the effectiveness of the proposed methods in real-world environments.
The introduction highlights the significance of mobile robots, leading to the development of terrestrial-aerial bimodal vehicles (TABVs) to combine advantages from both unmanned aerial and ground vehicles. Challenges in trajectory planning due to bimodal dynamics are addressed through a model-based approach.
The paper details the derivation of a unified dynamic model for passive-wheeled TABVs, emphasizing differential flatness benefits in trajectory planning and tracking control. An optimization-based trajectory planner is proposed, ensuring dynamical feasibility, continuity, smoothness, and real-time performance.
Extensive benchmark comparisons validate the proposed framework's effectiveness in planning quality and control performance. The study concludes by outlining future directions to enhance planning frameworks for TABVs on rough terrains.

Statystyki

"maximal commanded velocity reach 3m/s"
"acceleration reach 2.5m/s2"
"average computation times are 14.6ms"
"average computation time is 75.4ms"

Cytaty

"The results demonstrate that our method outperforms our previous work in planning quality and control performance."
"NMPC enables accurate trajectory tracking and smooth locomotion mode transition."
"The proposed planner prevents lateral movement, ensuring dynamic feasibility."

Kluczowe wnioski z

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

by Ruibin Zhang... o arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00322.pdf

Głębsze pytania

How can the proposed framework be adapted for different types of terrain

The proposed framework can be adapted for different types of terrain by incorporating terrain-specific constraints and dynamics into the trajectory planning and control algorithms. For example, when dealing with rough or uneven terrain, the planner could include additional constraints to account for variations in ground elevation and surface conditions. This adaptation may involve adjusting the cost functions in the optimization problem to prioritize paths that are more suitable for traversing challenging terrains. Additionally, sensor fusion techniques could be employed to enhance perception capabilities in environments with limited visibility or complex obstacles.

What are the potential limitations or drawbacks of relying heavily on differential flatness for trajectory planning

While differential flatness offers significant advantages in trajectory planning by simplifying the system's dynamics and enabling efficient optimization, there are potential limitations to relying heavily on this approach. One drawback is that differential flatness assumes a perfect model of the system dynamics, which may not always hold true in real-world scenarios where uncertainties and disturbances exist. In such cases, deviations between the actual system behavior and the modeled differential flatness could lead to suboptimal trajectories or even instability during execution.
Another limitation is related to computational complexity. The process of deriving a unified dynamic model and ensuring differential flatness for bimodal vehicles can be computationally intensive, especially when considering complex terrains or high-dimensional state spaces. This increased computational burden may impact real-time performance requirements for autonomous navigation tasks.
Furthermore, differential flatness-based approaches may struggle with handling discontinuities or abrupt changes in motion profiles due to their smooth trajectory generation nature. In scenarios requiring rapid maneuvers or quick responses to unforeseen events, traditional methods based on differential flatness alone might not provide sufficient agility or robustness.

How might advancements in autonomous navigation impact other industries beyond robotics

Advancements in autonomous navigation have far-reaching implications beyond robotics and can significantly impact various industries:

Transportation: Autonomous navigation technologies developed for robots can be applied to self-driving cars, drones used for delivery services, and unmanned aerial vehicles (UAVs) for surveillance purposes. Improved path planning algorithms can enhance traffic flow efficiency while ensuring safety on roads and airspace.

Logistics: Autonomous navigation systems enable optimized route planning for warehouses, distribution centers, and supply chain operations. By automating material handling processes using intelligent robots equipped with advanced navigation capabilities, companies can streamline operations and reduce costs.

Agriculture: Autonomous agricultural robots equipped with precise positioning systems can navigate fields autonomously while performing tasks like planting seeds, applying fertilizers/pesticides efficiently based on soil conditions mapping data collected during operation.

4Environmental Monitoring: Drones equipped with sophisticated autonomous navigation systems play a crucial role in environmental monitoring applications such as wildlife tracking surveys,
forest fire detection/prevention efforts through aerial surveillance.
5Search & Rescue Operations: Advanced autonomous UAVs capable of navigating complex terrains swiftly aid search & rescue missions by covering large areas quickly identifying survivors/trapped individuals accurately.