arXiv subjects

Ruitao Song

Publications and source records attributed to Ruitao Song.

3 recordsLinked to original sources

arXiv2023

VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain

We present a visual and inertial-based terrain classification network (VINet) for robotic navigation over different traversable surfaces. We use a novel navigation-based labeling scheme for terrain classification and generalization on unknown surfaces. Our proposed perception method and adaptive scheduling control framework can make predictions according to terrain navigation properties and lead to better performance on both terrain classification and navigation control on known and unknown surfaces. Our VINet can achieve 98.37% in terms of accuracy under supervised setting on known terrains and improve the accuracy by 8.51% on unknown terrains compared to previous methods. We deploy VINet on a mobile tracked robot for trajectory following and navigation on different terrains, and we demonstrate an improvement of 10.3% compared to a baseline controller in terms of RMSE.

cs.RO↗

arXiv2022

TNS: Terrain Traversability Mapping and Navigation System for Autonomous Excavators

We present a terrain traversability mapping and navigation system (TNS) for autonomous excavator applications in an unstructured environment. We use an efficient approach to extract terrain features from RGB images and 3D point clouds and incorporate them into a global map for planning and navigation. Our system can adapt to changing environments and update the terrain information in real-time. Moreover, we present a novel dataset, the Complex Worksite Terrain (CWT) dataset, which consists of RGB images from construction sites with seven categories based on navigability. Our novel algorithms improve the mapping accuracy over previous SOTA methods by 4.17-30.48% and reduce MSE on the traversability map by 13.8-71.4%. We have combined our mapping approach with planning and control modules in an autonomous excavator navigation system and observe 49.3% improvement in the overall success rate. Based on TNS, we demonstrate the first autonomous excavator that can navigate through unstructured environments consisting of deep pits, steep hills, rock piles, and other complex terrain features.

cs.RO↗

arXiv2022

Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC

In this paper, we present a systematic approach for high-performance and efficient trajectory tracking control of autonomous wheel loaders. With the nonlinear dynamic model of a wheel loader, nonlinear model predictive control (MPC) is used in offline trajectory planning to obtain a high-performance state-control trajectory while satisfying the state and control constraints. In tracking control, the nonlinear model is embedded into a Linear Parameter Varying (LPV) model and the LPV-MPC strategy is used to achieve fast online computation and good tracking performance. To demonstrate the effectiveness and the advantages of the LPV-MPC, we test and compare three model predictive control strategies in the high-fidelity simulation environment. With the planned trajectory, three tracking control strategies LPV-MPC, nonlinear MPC, and LTI-MPC are simulated and compared in the perspectives of computational burden and tracking performance. The LPV-MPC can achieve better performance than conventional LTI-MPC because more accurate nominal system dynamics are captured in the LPV model. In addition, LPV-MPC achieves slightly worse tracking performance but tremendously improved computational efficiency than nonlinear MPC. A video with loading cycles completed by our autonomous wheel loader in the simulation environment can be found here: https://youtu.be/QbNfS_wZKKA.

eess.SY↗