Chen Chen, Lin Junbo, You Bo, Li Jiayu, Gao Biao
The Key Laboratory of Intelligent Technology for Cutting and Manufacturing Ministry of Education, Harbin University of Science and Technology, Harbin, China.
The Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin, China.
Front Neurorobot. 2025 May 21;19:1605938. doi: 10.3389/fnbot.2025.1605938. eCollection 2025.
To address the challenges arising from the coupled interactions between multi-dimensional terrain features-encompassing both geometric and physical properties of complex field environments-and the locomotion stability of hexapod robots, this paper presents a comprehensive motion planning framework incorporating multi-dimensional terrain information. The proposed methodology systematically extracts multi-dimensional geometric and physical terrain features from a multi-layered environmental map. Based on these features, a traversal cost map is synthesized, and an enhanced A* algorithm is developed that incorporates terrain traversal metrics to optimize path planning safety across complex field environments. Furthermore, the framework introduces a foothold cost map derived from multi-dimensional terrain data, coupled with a fault-tolerant free gait planning algorithm based on foothold cost evaluation. This approach enables dynamic gait modulation to enhance overall locomotion stability while maintaining safe trajectory planning. The efficacy of the proposed framework is validated through both simulation studies and physical experiments on a hexapod robotic platform. Experimental results demonstrate that, compared to conventional hexapod motion planning approaches, the proposed multi-dimensional terrain-aware planning framework significantly enhances both locomotion safety and stability across complex field environments.
为应对多维地形特征（包括复杂野外环境的几何和物理特性）与六足机器人运动稳定性之间的耦合相互作用所带来的挑战，本文提出了一个包含多维地形信息的综合运动规划框架。所提出的方法从多层环境地图中系统地提取多维几何和物理地形特征。基于这些特征，合成了一个遍历成本地图，并开发了一种增强型A*算法，该算法结合了地形遍历指标，以优化复杂野外环境中的路径规划安全性。此外，该框架引入了一个从多维地形数据导出的立足点成本地图，并结合了基于立足点成本评估的容错自由步态规划算法。这种方法能够进行动态步态调制，以增强整体运动稳定性，同时保持安全的轨迹规划。通过在六足机器人平台上进行的仿真研究和物理实验，验证了所提出框架的有效性。实验结果表明，与传统的六足运动规划方法相比，所提出的多维地形感知规划框架显著提高了复杂野外环境中的运动安全性和稳定性。
