Development of motion control algorithms for obstacle avoidance application on a 6x6 skid steered modular height adjustable land platform

Date

2024-9-04

Author

Ulusoy, Kaan Berke

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The advancement of sensor technology led the autonomous driving one of the growing interest in the literature starting from passenger cars to unmanned ground vehicles. The predefined operational design domains relatively simplifies the development process for passenger cars. However, the task is quite challenging for UGVs, especially on rough terrain applications because of uncharted territories. To address these challenges, several trajectory optimization and tracking algorithms have been developed in the literature. Main objective of this study is to develop and test advance motion control algorithms, which reduce power consumption while maintaining smooth road holding performance for obstacle avoidance applications on a 6x6 skid steering vehicle. In this study, a geometric path planner and a model predictive trajectory optimization algorithm are utilized to find the optimal trajectory for obstacle avoidance. To track the desired path, a pure pursuit path tracking algorithm used as a baseline for performance comparison is utilized for motion control. To further enhance performance, two separate receding horizon control approaches are employed as velocity and torque controllers, each covering the necessary boundaries within a single control framework. In order to adapt the torque controller to varying environmental conditions, such as different road surfaces and rough terrains, an adaptive weighting coefficient methodology is employed for the model predictive control. Thus, the traction stability and instantaneous load distribution effects were considered in a single control architecture. The performance of the developed algorithms is evaluated through testing in straight, obstacle avoidance, and circular path scenarios on the 6x6 skid-steering vehicle.

Subject Keywords

NMPC, Obstacle Avoidance, Trajectory Tracking, Skid Steering

URI

https://hdl.handle.net/11511/111413

Collections

Graduate School of Natural and Applied Sciences, Thesis