Motion Trajectory Generation Using Updating Final-State Control

被引:0
作者
Hara, Susumu [1 ]
Tsukamoto, Masaki [2 ]
Maeda, Takao [1 ]
机构
[1] Nagoya Univ, Grad Sch Engn, Dept Aerosp Engn, Nagoya, Aichi, Japan
[2] Nagoya Univ, Sch Engn, Nagoya, Aichi, Japan
来源
2016 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING (ICITE) | 2016年
关键词
motion control; trajectory generation; collision avoidance; final-state control; model predictive control;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Manual motion control (MMC) problems are seen in the conveyance of a large amount of products in factories and stores. One of the most successful examples of MMC is power assist. The power-assisted systems have been introduced to reduce workers' loads in industrial production. In near future, in order to improve its efficiency, the power-assisted systems should include automatic operational modes. This paper discusses an obstacle collision avoidance control system design method for such an automatic operation. Concretely, an existing cart is applied as a controlled object example and it is assumed that the cart moves automatically using the cart's actuator and stops by itself in front of obstacles without any collision. Then, this study applies an improvement of the final-state control (FSC), the updating final-state control (UFSC) to the automatic operation for the obstacle collision avoidance. By using UFSC, the automatic operated cart can decelerate gradually. The responses of the proposed control system are verified by comparing with a model predictive control (MPC) by simulations and an experimental example.
引用
收藏
页码:35 / 39
页数:5
相关论文
共 50 条
[21]   Ti ji, a Generic Trajectory Generation Tool for Motion Planning and Control [J].
Delsart, Vivien ;
Fraichard, Thierry .
IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, :1439-1444
[22]   Final-state control for a system with load displacement-dependent stiffness in a two-inertia system [J].
Hirata, Mitsuo ;
Nomura, Rikuto ;
Suzuki, Masayasu ;
Araya, Hiroshi ;
Igarashi, Yoichi .
MECHANICAL ENGINEERING JOURNAL, 2025, 12 (03)
[23]   Trajectory Generation Using Model Predictive Control for Automated Vehicles [J].
Irie Y. ;
Akasaka D. .
International Journal of Automotive Engineering, 2021, 12 (01) :24-31
[24]   Motion Description Language for Trajectory Generation of a Robot Manipulator [J].
Liu Zhaoming ;
Liu Nailong ;
Wei Qing ;
Cui Long .
2017 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (IEEE ROBIO 2017), 2017, :1950-1955
[25]   A Computationally Efficient Motion Primitive for Quadrocopter Trajectory Generation [J].
Mueller, Mark W. ;
Hehn, Markus ;
D'Andrea, Raffaello .
IEEE TRANSACTIONS ON ROBOTICS, 2015, 31 (06) :1294-1310
[26]   Remote low frequency state feedback kinematic motion control for mobile robot trajectory tracking [J].
Flickinger, Daniel Montrallo ;
Minor, Mark A. .
PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-10, 2007, :3502-+
[27]   HIGH ACCURACY HUMAN MOTION TRAJECTORY GENERATION FOR EXOSKELETON ROBOT USING CURVE FITTING TECHNIQUE [J].
Jalil, Muhammad Abdul ;
Miskon, Muhammad Fahmi ;
Bin Bahar, Bazli .
IIUM ENGINEERING JOURNAL, 2023, 24 (02) :301-314
[28]   Trajectory Generation for a Hydraulic Mini Excavator using Nonlinear Model Predictive Control [J].
Wind, Hannes ;
Renner, Anton ;
Bender, Frank A. ;
Sawodny, Oliver .
2020 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 2020, :107-112
[29]   Constant Velocity Control of a Miniature Pantograph with Image Based Trajectory Generation [J].
Baran, Eray A. ;
Golubovic, Edin ;
Kurt, Tarik E. ;
Sabanovic, Asif .
2013 9TH ASIAN CONTROL CONFERENCE (ASCC), 2013,
[30]   Optimization of Trajectory Generation and Tracking Control Method for Autonomous Underwater Docking [J].
Ni, Tian ;
Sima, Can ;
Li, Shaobin ;
Zhang, Lindan ;
Wu, Haibo ;
Guo, Jia .
JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2024, 12 (08)