학술논문

Traversability-Aware Adaptive Optimization for Path Planning and Control in Mountainous Terrain
Document Type
Periodical
Author
Source
IEEE Robotics and Automation Letters IEEE Robot. Autom. Lett. Robotics and Automation Letters, IEEE. 9(6):5078-5085 Jun, 2024
Subject
Robotics and Control Systems
Computing and Processing
Components, Circuits, Devices and Systems
Robots
Costs
Optimization
Navigation
Kernel
Adaptation models
Propioception
Field robots
integrated planning and learning
robust/adaptive control
Language
ISSN
2377-3766
2377-3774
Abstract
Autonomous navigation in extreme mountainous terrains poses challenges due to the presence of mobility-stressing elements and undulating surfaces, making it particularly difficult compared to conventional off-road driving scenarios. In such environments, estimating traversability solely based on exteroceptive sensors often leads to the inability to reach the goal due to a high prevalence of non-traversable areas. In this letter, we consider traversability as a relative value that integrates the robot's internal state, such as speed and torque to exhibit resilient behavior to reach its goal successfully. We separate traversability into apparent traversability and relative traversability, then incorporate these distinctions in the optimization process of sampling-based planning and motion predictive control. Our method enables the robots to execute the desired behaviors more accurately while avoiding hazardous regions and getting stuck. Experiments conducted on simulation with 27 diverse types of mountainous terrain and real-world demonstrate the robustness of the proposed framework, with increasingly better performance observed in more complex environments.