Hierarchical Motion Planning of Mobile Robot Based on Dynamic Corridor Inflation and Convex Optimization

doi:10.16182/j.issn1004731x.joss.25-0395

Abstract

Abstract:

Motion planning for robots with Ackermann chassis in dynamic complex environments faces nonholonomic constraints and kinematic-dynamic coupling challenges. However, traditional methods suffer from path redundancy, random fluctuations, and local optimality. A hierarchical motion planning method based on dynamic corridor inflation and convex optimization is proposed. Topologically sparse paths are generated by fusing the Ramer-Douglas-Peucker (RDP) path compression operator with the A* algorithm to reduce redundant path points' interference with backend optimization. Dynamic corridor inflation strategies are designed considering Ackermann steering characteristics, and safe corridors satisfying kinematic constraints are constructed via convex decomposition. Corridor constraints are then transformed into linear inequalities embedded in a convex optimization model. An objective function of minimizing acceleration's second derivative is adopted, and a curvature-adaptive time adjustment mechanism is introduced for global optimal trajectory generation under dynamic constraints. Simulation and physical verification platforms validate the algorithm's effectiveness. In scenarios with dense obstacles, it achieves a 97.3% success rate. Compared with the A* algorithm, it improves trajectory smoothness by 93.5% and reduces 81.6% of path points. In comparison with hierarchical motion planning method with A*-integrated ESDF-based conjugate gradient optimization (HPGO), the proposed method enhances trajectory smoothness by 91.6% and reduces 81.8% of path points. High-precision iGPS physical tests show centimeter-level tracking accuracy, confirming algorithm-control system adaptability. Experiments demonstrate improved planning quality and execution reliability in warehouse logistics, urban rescue, etc., offering a new solution for nonholonomic mobile platform motion planning.

Key words: Ackermann-steering robots, motion planning, safe corridors, trajectory optimization, convex optimization

CLC Number:

TP242

Zhang Dingkun, Liang Haizhao. Hierarchical Motion Planning of Mobile Robot Based on Dynamic Corridor Inflation and Convex Optimization[J]. Journal of System Simulation, 2026, 38(5): 1383-1407.

Figures/Tables 36

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Table 1

Algorithm performance quantitative comparison

量化指标	HPCC	HPGO	A*
曲线连续性误差 $/ (r a d / m 2)$	70.32	837.65	1 082.84
轨迹长度/m	29.74	30.59	30.64
重规划计算时间/ms	93.11	80.79	65.77
路径点数量	100	543	544

Table 1

Fig. 13

Fig. 14

Fig. 15

Fig. 16

Fig. 17

Fig. 18

Fig. 19

Fig. 20

Fig. 21

Fig. 22

Fig. 23

Fig. 24

Fig. 25

Fig. 26

Fig. 27

Fig. 28

Fig. 29

Table 2

Fig. 30

Fig. 31

Fig. 32

Fig. 33

Fig. 34

References 25

[1]	王雅清, 倪晓昌, 李静, 等. 移动机器人路径规划算法研究进展[J]. 智能计算机与应用, 2024, 14(11): 211-217.
	Wang Yaqing, Ni Xiaochang, Li Jing, et al. Research on Path Planning Algorithm Based on Mobile Robot[J]. Intelligent Computer and Applications, 2024, 14(11): 211-217.
[2]	Hart P E, Nilsson N J, Raphael B. A Formal Basis for the Heuristic Determination of Minimum Cost Paths[J]. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2): 100-107.
[3]	LaValle S. Rapidly-exploring Random Trees: A New Tool for Path Planning[J]. Research Report, 1998.
[4]	Zucker M, Ratliff N, Dragan A D, et al. CHOMP: Covariant Hamiltonian Optimization for Motion Planning[J]. The International Journal of Robotics Research, 2013, 32(9/10): 1164-1193.
[5]	Mukadam M, Dong Jing, Yan Xinyan, et al. Continuous-time Gaussian Process Motion Planning via Probabilistic Inference[J]. The International Journal of Robotics Research, 2018, 37(11): 1319-1340.
[6]	Dolgov D, Thrun S, Montemerlo M, et al. Practical Search Techniques in Path Planning for Autonomous Driving[J]. ann arbor, 2008, 1001(48105): 18-80.
[7]	Pivtoraiko M, Knepper R A, Kelly A. Differentially Constrained Mobile Robot Motion Planning in State Lattices[J]. Journal of Field Robotics, 2009, 26(3): 308-333.
[8]	Werling Moritz, Kammel Sören, Ziegler Julius, et al. Optimal Trajectories for Time-critical Street Scenarios Using Discretized Terminal Manifolds[J]. The International Journal of Robotics Research, 2012, 31(3): 346-359.
[9]	Deits R, Tedrake R. Computing Large Convex Regions of Obstacle-free Space Through Semidefinite Programming[M]//H Levent Akin, Amato N M, Isler V, et al. Algorithmic Foundations of Robotics XI: Selected Contributions of the Eleventh International Workshop on the Algorithmic Foundations of Robotics. Cham: Springer International Publishing, 2015: 109-124.
[10]	Cheng Zikang, Xue Jingbo, Wang Bo, et al. Path Planning Using Improved Hybrid A* Algorithm for Mobile Robots[C]//2024 IEEE 25th China Conference on System Simulation Technology and its Application (CCSSTA). Piscataway: IEEE, 2024: 352-357.
[11]	夏雨奇, 黄炎焱, 陈恰. 基于深度Q网络的无人车侦察路径规划[J]. 系统工程与电子技术, 2024, 46(9): 3070-3081.
	Xia Yuqi, Huang Yanyan, Chen Qia. Path Planning for Unmanned Vehicle Reconnaissance Based on Deep Q-network[J]. Systems Engineering and Electronics, 2024, 46(9): 3070-3081.
[12]	Tian Zhaofeng, Liu Zichuan, Zhou Xingyu, et al. Unguided Self-exploration in Narrow Spaces with Safety Region Enhanced Reinforcement Learning for Ackermann-steering Robots[C]//2024 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST). Piscataway: IEEE, 2024: 260-268.
[13]	Mellinger D, Kumar V. Minimum Snap Trajectory Generation and Control for Quadrotors[C]//2011 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2011: 2520-2525.
[14]	Gao Fei, Wu W, Lin Yi, et al. Online Safe Trajectory Generation for Quadrotors Using Fast Marching Method and Bernstein Basis Polynomial[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, 2018: 344-351.
[15]	Liu Sikang, Watterson M, Mohta K, et al. Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments[J]. IEEE Robotics and Automation Letters, 2017, 2(3): 1688-1695.
[16]	Douglas David H, Peucker Thomas K. Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or Its Caricature[J]. Cartographica, 1973, 10(2): 112-122.
[17]	Stellato B, Banjac Goran, Goulart P, et al. OSQP: An Operator Splitting Solver for Quadratic Programs[J]. Mathematical Programming Computation, 2020, 12(4): 637-672.
[18]	AI-WINTER. Ros_Motion_Planning[EB/OL]. [2025-04-10]. .
[19]	LaValle S M, Kuffner Jr J J. Randomized Kinodynamic Planning[J]. The International Journal of Robotics Research, 2001, 20(5): 378-400.
[20]	Karaman S, Frazzoli E. Sampling-based Algorithms for Optimal Motion Planning[J]. The International Journal of Robotics Research, 2011, 30(7): 846-894.
[21]	Gammell Jonathan D, Srinivasa S S, Barfoot Timothy 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. Piscataway: IEEE, 2014: 2997-3004.
[22]	Depenthal C. iGPS Used as Kinematic Measuring System[C]//FIG Congress. [S.l. : s.n.], 2010: 11-16.
[23]	Schmitt Robert, Nisch S, Schönberg A, et al. Performance Evaluation of iGPS for Industrial Applications[C]//2010 International Conference on Indoor Positioning and Indoor Navigation. Piscataway: IEEE, 2010: 1-8.
[24]	Wang Zheng, Mastrogiacomo Luca, Franceschini Fiorenzo, et al. Experimental Comparison of Dynamic Tracking Performance of iGPS and Laser Tracker[J]. The International Journal of Advanced Manufacturing Technology, 2011, 56(1): 205-213.
[25]	Norman A, Schönberg A, Gorlach I, et al. Cooperation of Industrial Robots with Indoor-GPS[C]//Proceedings of the International Conference on Competitive Manufacturing. [S.l. : s.n.], 2010: 215-224.

参数名称	数据
尺寸/mm	445×359×206
重量/kg	9.34
续航时间/h	8.5
核心控制器	STM32F407VET6 ARM架构Cortex-M4内核32位