Acceleration Optimization-Based Speed Planning Method for High-Precision Longitudinal Control of Wheeled Robots

doi:10.1007/s12204-025-2836-2

Abstract

Abstract: In recent years, wheeled robots have been widely used in the field of logistics automation. In realworld application, the inertia of wheeled robots is not fully considered in traditional speed planning methods, and the longitudinal error of wheeled robots reaching the target area is too large to accurately complete subsequent operations, especially for large-loaded wheeled robots like autonomous forklifts. In order to deal with the above problem, this paper proposes an acceleration-awarded speed planning method based on acceleration optimization aimed at making wheeled robots reach the target area smoothly and accurately. This method first introduces acceleration information into speed planning based on dynamic constraints, and then models speed planning as an optimization problem to smooth speed changes. Experimental verification shows that the longitudinal error of wheeled robots using this method is significantly reduced, and the smoothness of speed is improved.

Key words: speed planning, wheeled robots, autonomous forklift

摘要： 近年来，轮式机器人在物流自动化领域得到了广泛的应用。在实际应用中，传统的速度规划方法没有充分考虑轮式机器人的惯性，轮式机器人到达目标区域的纵向误差太大，无法准确完成后续操作，特别是对于自动叉车等大型装载轮式机器人。为了解决上述问题，本文提出了一种基于优化的加速度奖励速度规划方法，旨在使轮式机器人平稳准确地到达目标区域。该方法首先将加速度信息引入基于动态约束的速度规划中，然后将速度规划建模为优化问题，以平滑速度变化。实验验证表明：采用该方法的轮式机器人纵向误差显著减小，且速度平稳性得到提高。

关键词: 速度规划，轮式机器人，自动叉车

CLC Number:

TP242.6

Wang Longsheng, Yuan Wei, Zhuang Hanyang, Wang Chunxiang, Yang Ming. Acceleration Optimization-Based Speed Planning Method for High-Precision Longitudinal Control of Wheeled Robots[J]. J Shanghai Jiaotong Univ Sci, 2026, 31(1): 48-58.

References

[1] WANG C J, DU D D. Research on logistics autonomous mobile robot system [C]//2016 IEEE International Conference on Mechatronics and Automation. Harbin: IEEE, 2016: 275-280.
[2] SHEN Q Y, WANG B, WANG C X. Real-time trajectory planning for on-road autonomous tractor-trailer vehicles [J]. Journal of Shanghai Jiao Tong University (Science), 2021, 26(5): 722-730.
[3] ESAN O, DU S Z, LODEWYK B. Review on autonomous indoor wheel mobile robot navigation systems [C]//2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems. Durban: IEEE, 2020: 1-6.
[4] LI Y, CHEN S M, BAI K, et al. Path planning of patrol robot based on improved discrete electrostatic discharge algorithm [J]. Journal of Intelligent & Fuzzy Systems, 2022, 42(6): 5919-5930.
[5] WERLING M, ZIEGLER J, KAMMEL S, et al. Optimal trajectory generation for dynamic street scenarios in a Frenét Frame [C]//2010 IEEE International Conference on Robotics and Automation. Anchorage: IEEE, 2010: 987-993.
[6] KUWATA Y, TEO J, KARAMAN S, et al. Motion planning in complex environments using closed-loop prediction [C]//AIAA Guidance, Navigation and Control Conference and Exhibit. Honolulu: AIAA, 2008: 7166.
[7] HADDAD M, KHALIL W, LEHTIHET H E. Trajectory planning of unicycle mobile robots with a trapezoidal-velocity constraint [J]. IEEE Transactions on Robotics, 2010, 26(5): 954-962.
[8] HUBMANN C, AEBERHARD M, STILLER C. A generic driving strategy for urban environments [C]//2016 IEEE 19th International Conference on Intelligent Transportation Systems. Rio de Janeiro: IEEE, 2016: 1010-1016.
[9] ZHOU M F, QU X B, JIN S. On the impact of cooperative autonomous vehicles in improving freeway merging: A modified intelligent driver model-based approach [J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(6): 1422-1428.
[10] ARTUÑEDO A, VILLAGRA J, GODOY J. Jerk-limited time-optimal speed planning for arbitrary paths [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 8194-8208.
[11] KUDERER M, GULATI S, BURGARD W. Learning driving styles for autonomous vehicles from demonstration [C]//2015 IEEE International Conference on Robotics and Automation. Seattle: IEEE, 2015: 2641-2646.
[12] LI S Y, LI M Z, JING Z L. Multi-agent path planning method based on improved deep Q-network in dynamic environments [J]. Journal of Shanghai Jiao Tong University (Science), 2024, 29(4): 601-612.
[13] CHAI R Q, LIU D R, LIU T H, et al. Deep learning-based trajectory planning and control for autonomous ground vehicle parking maneuver [J]. IEEE Transactions on Automation Science and Engineering, 2023, 20(3): 1633-1647.
[14] BAI Z W, CAI B G, SHANGGUAN W, et al. Deep learning based motion planning for autonomous vehicle using spatiotemporal LSTM network [C]//2018 Chinese Automation Congress. Xi’an: IEEE, 2018: 1610-1614.
[15] WANG L, WANG B, WANG C X. Collision-free path planning with kinematic constraints in urban scenarios [J]. Journal of Shanghai Jiao Tong University (Science), 2021, 26(5): 731-738.
[16] ZHANG Y J, SUN H Y, ZHOU J Y, et al. Optimal trajectory generation for autonomous vehicles under centripetal acceleration constraints for in-lane driving scenarios [C]//2019 IEEE Intelligent Transportation Systems Conference. Auckland: IEEE, 2019: 3619-3626.
[17] WEI Y, XU H Q. Path planning of autonomous driving based on quadratic optimization [C]//2023 9th International Conference on Control, Automation and Robotics. Beijing: IEEE, 2023: 308-312.
[18] VÁZQUEZ J L, BRÜHLMEIER M, LINIGER A, et al. Optimization-based hierarchical motion planning for autonomous racing [C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas: IEEE, 2020: 2397-2403.
[19] EIRAS F, HAWASLY M, ALBRECHT S V, et al. A two-stage optimization-based motion planner for safe urban driving [J]. IEEE Transactions on Robotics, 2022, 38(2): 822-834.
[20] LI B, ZHANG Y M, FENG Y H, et al. Balancing computation speed and quality: A decentralized motion planning method for cooperative lane changes of connected and automated vehicles [J]. IEEE Transactions on Intelligent Vehicles, 2018, 3(3): 340-350.
[21] JOHANSSON I, JIN J C, MA X L, et al. Look-ahead speed planning for heavy-duty vehicle platoons using traffic information [J]. Transportation Research Procedia, 2017, 22: 561-569.
[22] SUN C, ZHANG C T, ZHOU X Y, et al. Coordinated hierarchical co-optimization of speed planning and energy management for electric vehicles driving in stochastic environment [J]. IEEE Transactions on Vehicular Technology, 2023, 72(10): 12628-12638.
[23] PENG J H, ZHANG F Q, COSKUN S, et al. Hierarchical optimization of speed planning and energy management for connected hybrid electric vehicles under multi-lane and signal lights aware scenarios [J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(12): 14174-14188.
[24] ZHANG Y, CHEN H Y, WASLANDER S L, et al. Speed planning for autonomous driving via convex optimization [C]//2018 21st International Conference on Intelligent Transportation Systems. Maui: IEEE, 2018: 1089-1094.
[25] WACHTER A. An interior point algorithm for large-scale nonlinear optimization with applications in process engineering [D]. Pittsburgh: Carnegie Mellon University, 2002.