arXiv subjects

Yihui Mao

Publications and source records attributed to Yihui Mao.

3 recordsLinked to original sources

arXiv2026

Parallel OctoMapping: A Scalable Framework for Enhanced Path Planning in Autonomous Navigation

Mapping is essential in robotics and autonomous systems because it provides the spatial foundation for path planning. Efficient mapping enables planning algorithms to generate reliable paths while ensuring safety and adapting in real time to complex environments. Fixed-resolution mapping methods often produce overly conservative obstacle representations that lead to suboptimal paths or planning failures in cluttered scenes. To address this issue, we introduce Parallel OctoMapping (POMP), an efficient OctoMap-based mapping technique that maximizes available free space and supports multi-threaded computation. To the best of our knowledge, POMP is the first method that, at a fixed occupancy-grid resolution, refines the representation of free space while preserving map fidelity and compatibility with existing search-based planners. It can therefore be integrated into existing planning pipelines, yielding higher pathfinding success rates and shorter path lengths, especially in cluttered environments, while substantially improving computational efficiency.

cs.RO↗

arXiv2024

A-OctoMap: An Adaptive OctoMap for Online Path Planning

Downsampling and path planning are essential in robotics and autonomous systems, as they enhance computational efficiency and enable effective navigation in complex environments. However, current downsampling methods often fail to preserve crucial geometric information while maintaining computational efficiency, leading to challenges such as information loss during map reconstruction and the need to balance precision with computational demands. Similarly, current graph-based search algorithms for path planning struggle with fixed resolutions in complex environments, resulting in inaccurate obstacle detection and suboptimal or failed pathfinding. To address these issues, we introduce an adaptive OctoMap that utilizes a hierarchical data structure. This innovative approach preserves key geometric information during downsampling and offers a more flexible representation for pathfinding within fixed-resolution maps, all while maintaining high computational efficiency. Simulations validate our method, showing significant improvements in reducing information loss, enhancing precision, and boosting the computational efficiency of map reconstruction compared to state-of-the-art methods. For path planning, our approach enhances Jump Point Search (JPS) by increasing the success rate of pathfinding and reducing path lengths, enabling more reliable navigation in complex scenes.

cs.RO↗

arXiv2024

Safety-Critical Planning and Control for Dynamic Obstacle Avoidance Using Control Barrier Functions

Dynamic obstacle avoidance is a challenging topic for optimal control and optimization-based trajectory planning problems. Many existing works use Control Barrier Functions (CBFs) to enforce safety constraints for control systems. CBFs are typically formulated based on the distance to obstacles, or integrated with path planning algorithms as a safety enhancement tool. However, these approaches usually require knowledge of the obstacle boundary equations or have very slow computational efficiency. In this paper, we propose a framework based on model predictive control (MPC) with discrete-time high-order CBFs (DHOCBFs) to generate a collision-free trajectory. The DHOCBFs are first obtained from convex polytopes generated through grid mapping, without the need to know the boundary equations of obstacles. Additionally, a path planning algorithm is incorporated into this framework to ensure the global optimality of the generated trajectory. We demonstrate through numerical examples that our framework allows a unicycle robot to safely and efficiently navigate tight, dynamically changing environments with both convex and nonconvex obstacles. By comparing our method to established CBF-based benchmarks, we demonstrate superior computing efficiency, length optimality, and feasibility in trajectory generation and obstacle avoidance.

cs.RO↗