非结构化地形环境下移动机器人导航

doi:10.3901/JME.260093

摘要/Abstract

摘要： 移动机器人在二维结构化地形下的自主导航已取得显著进展，但在三维非结构化环境中的导航应用仍面临诸多挑战，如难以准确反映地形特征以及路径规划难以穿越障碍等。为此，提出一种针对非结构化地形环境的移动机器人导航框架。首先，将三维栅格地图表面点进行局部平面拟合以捕捉地形细节特征，并对周围地形进行评判与可穿越性分析，为导航决策提供定量依据。其次，基于Informed RRT*采样规划方法，设计考虑地形可穿越性的代价函数，提出一种新的非结构化地形全局路径规划算法IRRT*-TFP，通过二维平面采样搜索、三维空间优化的方式，有效利用地形信息准确规划的同时减少计算复杂度。最后，采用改进的动态窗口方法DWA-KPS进行局部规划，通过对全局轨迹增密及添加轨迹相似度评价函数等方法，有效解决移动机器人偏离全局规划而陷入危险地形等问题，提高导航质量和安全性。通过对IRRT-TFP算法和DWA-KPS算法的实验验证了所提出算法在崎岖复杂的非结构化环境中自主导航的有效性。

关键词: 非结构化地形, 导航, 运动规划, 移动机器人

Abstract: While significant progress has been made in autonomous navigation of mobile robots in 2D structured terrains, many challenges remain in navigating 3D unstructured environments, such as accurately reflecting terrain features and planning paths that can traverse obstacles. Therefore, a navigation framework for wheeled robots in unstructured terrains is proposed. Firstly, local plane fitting is applied to the surface points of the 3D grid map to capture terrain detail features, enabling evaluation and passability analysis of the surrounding terrain to provide quantitative support for navigation decisions. Secondly, an informed RRT* sampling planning method is employed to design a cost function that considers terrain passability, proposing a 2D sampling and 3D optimization global planning method, IRRT*-TFP, which effectively utilizes terrain information to accurately plan while reducing computational complexity. Finally, an improved dynamic window approach (DWA) is used for local planning, DWA-KPS, which solves the problem of deviating from the global plan and entering dangerous terrains by densifying the global trajectory and adding a trajectory similarity evaluation function, thereby improving navigation quality and safety. Comparative experiments between IRRT-TFP and the A* algorithm, as well as DWA-KPS and the original DWA algorithm, verify the effectiveness of the proposed algorithms for autonomous navigation in rugged and complex unstructured environments.

Key words: unstructured terrain, navigation, motion planning, mobile robots

中图分类号:

TP242

赵浩宇, 侯鹏帅, 陈星全, 邵宇乐, 董奇, 陈杰. 非结构化地形环境下移动机器人导航[J]. 机械工程学报, 2026, 62(3): 396-406.

ZHAO Haoyu, HOU Pengshuai, CHEN Xingquan, SHAO Yule, DONG Qi, CHEN Jie. Navigation of Mobile Robot in Unstructured Terrain Environments[J]. Journal of Mechanical Engineering, 2026, 62(3): 396-406.

参考文献

[1] TU Haiyan，DENG Yizhao，LI Qiyang，et al. Improved RRT global path planning algorithm based on bridge test[J]. Robotics and Autonomous Systems，2024，171：104570.
[2] TAO Yong，DUAN Lian，GAO He，et al. Heuristic expanding disconnected graph：A rapid path planning method for mobile robots[J]. Chinese Journal of Mechanical Engineering，2024，37(1)：32.
[3] SHENTU Shuzhan，GONG Zhao，LIU Xinjun，et al. Hybrid navigation system based autonomous positioning and path planning for mobile robots[J]. Chinese Journal of Mechanical Engineering，2022，35(1)：109.
[4] 段书用，王启帆，韩旭，等. 具有确保安全距离的A* 路径优化方法[J]. 机械工程学报，2020，56(18)：205-215. DUAN Shuyong，WANG Qifan，HAN Xu，et al. A* Path optimization method with safety distance assurance[J]. Journal of Mechanical Engineering，2020，56(18)：205-215.
[5] WANG Shuxi. The improved dijkstra’s shortest path algorithm and its application[J]. Procedia Engineering，2012，29：1186-1190.
[6] 赵锦涛，李亮，薛仲瑾，等. 基于混合A星的停车场内巡航分层运动规划方法[J]. 机械工程学报，2023，59(24)：290-298. ZHAO Jintao，LI Liang，XUE Zhongjin，et al. Hierarchical motion planning method for parking lot cruising based on hybrid A*[J]. Journal of Mechanical Engineering，2023，59(24)：290-298.
[7] KARAMAN S. Anytime motion planning using the RRT[C]// 2011 IEEE International Conference on Robotics and Automation. Shanghai：IEEE，2011：1478-1483.
[8] HORNUNG A，WURM K M，BENNEWITZ M，et al. OctoMap：An efficient probabilistic 3D mapping framework based on octrees[J]. Autonomous robots，2013，34：189-206.
[9] WANG Chaoqun，WANG Jiankun，LI Chenming，et al. Safe and robust mobile robot navigation in uneven indoor environments[J]. Sensors，2019，19(13)：2993.
[10] KIM P. SLAM-driven intelligent autonomous mobile robot navigation for construction applications[C]// 25th EG-ICE International Workshop on Advanced Computing Strategies for Engineering. Lausanne：Springer International Publishing，2018：254-269.
[11] JEONG I，JANG Youjin，PARK J，et al. Motion planning of mobile robots for autonomous navigation on uneven ground surfaces[J]. Journal of Computing in Civil Engineering，2021，35(3)：04021001.
[12] XIAO Xuesu，BISWAS J，STONE P. Learning inverse kinodynamics for accurate high-speed off-road navigation on unstructured terrain[J]. IEEE Robotics and Automation Letters，2021，6(3)：6054-6060.
[13] 刘梓林，黎予生，郑玲. 基于非结构化环境点云稀疏表示的无人驾驶汽车局部路径规划方法[J]. 机械工程学报，2020，56(2)：163-173. LIU Zilin，LI Yusheng，ZHENG Ling. Local path planning method for autonomous vehicles based on sparse representation of point clouds in unstructured environments[J]. Journal of Mechanical Engineering，2020，56(2)：163-173.
[14] DIXIT A，FAN D，OTSU K，et al. STEP：Stochastic traversability evaluation and planning for risk-aware navigation; results from the DARPA subterranean challenge[J]. IEEE Transactions on Field Robotics，2025，2：81-99.
[15] SHAN Tixiao. Bayesian generalized kernel inference for terrain traversability mapping[C]// Conference on Robot Learning. Zurich：PMLR，2018：829-838
[16] YU Xiangyu. A map accessibility analysis algorithm for mobile robot navigation in outdoor environment[C]// 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR). Irkutsk：IEEE，2019：475-480.
[17] 黄志清，李鼎鑫，王庆文. 非平坦地形下移动机器人安全路径规划[J]. 控制与决策，2022，37(2)：323-330. HUANG Zhiqing，LI Dingxin，WANG Qingwen. Safe path planning for mobile robots on uneven terrain [J]. Control and Decision，2022，37(2)：323-330.
[18] REINA G. Traversability analysis for off-road vehicles using stereo and radar data[C]// 2015 IEEE International Conference on Industrial Technology (ICIT). Seville：IEEE，2015：540-546.
[19] LARSON J. Off-road terrain traversability analysis and hazard avoidance for UGVs[C]// 2011 IEEE Intelligent Vehicles Symposium. Baden-Baden：IEEE，2011：1-7.
[20] GAMMELL J D. Informed RRT*：Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic[C]// 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago：IEEE，2014：2997-3004.
[21] FOX D，BURGARD W，THRUN S. The dynamic window approach to collision avoidance[J]. IEEE Robotics & Automation Magazine，1997，4(1)：23-33.
[22] AGARWAL P K，AVRAHAM R B，KAPLAN H，et al. Computing the discrete Fréchet distance in subquadratic time[J]. SIAM Journal on Computing，2014，43(2)：429-449.