Gholami Amir, Ramirez-Serrano Alejandro
Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
Sensors (Basel). 2025 Jan 13;25(2):439. doi: 10.3390/s25020439.
This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot's terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings). The results demonstrate that the proposed framework provides the robot with an enhanced capability to perceive and interpret its environment and adapt to dynamic environment changes. This paper contributes to the advancement of robotic navigation and path-planning systems by providing a scalable and efficient framework for environment analysis. The integration of various point cloud processing techniques into a single architecture not only improves computational efficiency but also enhances the robot's interaction with its environment, making it more capable of operating in complex, hazardous, unstructured settings.
本文提出了一种综合方法，用于评估多足机器人穿越狭窄且几何形状复杂的非结构化环境的能力。所提出的方法利用先进的点云处理技术，集成体素滤波云、边界和网格生成以及动态可穿越性分析，以增强机器人的地形感知和导航能力。通过对人形机器人进行严格的模拟和实验测试，验证了所提出的框架，展示了该方法在具有复杂环境特征（如在倒塌建筑物内导航）的应用/环境中的应用潜力。结果表明，所提出的框架为机器人提供了增强的感知和解释环境以及适应动态环境变化的能力。本文通过提供一个可扩展且高效的环境分析框架，为机器人导航和路径规划系统的发展做出了贡献。将各种点云处理技术集成到单一架构中，不仅提高了计算效率，还增强了机器人与环境的交互能力，使其更有能力在复杂、危险、非结构化的环境中运行。
