Navigating in 3D Uneven Environments Through Supervoxels and Nonlinear MPC

Navigating uneven and rough terrains presents difficulties, including stability, traversability, sensing, and robustness, making autonomous navigation in these terrains a challenging task. This study introduces a new approach for mobile robots to navigate uneven terrains. The method uses a compact graph of traversable regions on point cloud maps, created through the utilization of supervoxel representation of point clouds. By using this supervoxel graph, the method navigates the robot to any reachable goal pose by utilizing a navigation function and Nonlinear Model Predictive Controller (NMPC). The NMPC ensures kinodynamically feasible and collision-free motion plans, while the supervoxel-based geometric planning generates near-optimal plans by exploiting the terrain information. We conducted extensive navigation experiments in real and simulated 3D uneven terrains and found that the approach performs reliably. Additionally, we compared resulting motion plans to some state-of-the-art sampling-based motion planners in which our method outperformed them in terms of execution time and resulting path lengths. The method can also be adapted to meet specific behavior, like the shortest route or the path with the least slope route. The source code is available in a GitHub repository.
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https://github.com/NMBURobotics/vox_nav.