6 accepted papers
The suspension system, through effective damping of vibrations and shocks, can enhance the stability of wheeled robots traversing challenging terrain. Because the suspension system decouples the rigid correspondence between terrain changes and robot vibrations, considering suspension modeling in tra
In wild environments, motion planning for mobile robots faces the challenge of local optimal path traps due to limited sensor perception range and lack of spatial awareness. Existing approaches that avoid local optimum by designing heuristic functions or high-quality global paths in wild environment…
Trajectory planning is important for ground robots to achieve safe and efficient autonomous navigation in unstructured off-road environments. Most existing methods treat each terrain as a single type. However, in the real world, a ground usually consists of hybrid terrains. In this work, we propose
Speed planning in rugged terrain poses challenges due to various constraints, such as traverse efficiency, dynamics, safety, and smoothness. This letter introduces a framework based on Constrained Reinforcement Learning (CRL) that considers all these constraints. In addition, extracting the terrain
We present AVS-HRL, a modular Active visual SLAM system based on hierarchical reinforcement learning. The reward function explicitly considers the efficiency of exploration and the accuracy of mapping by utilizing the internal variables of SLAM, such as feature points distribution and loop-closure s…
Model predictive control is a promising method in robot control tasks. How to design an effective model structure and efficient prediction framework for model predictive control is still an open challenge. To reduce the time consumption and avoid compounding-error of the multi-step prediction proces…