Concepts of model-based control and trajectory planning for parallel robots

被引:0
|
作者
Belda, Kvetoslav [1 ]
Boehm, Josef [1 ]
Pisa, Pavel [1 ]
机构
[1] Acad Sci Czech Republic, Inst Informat Theory & Automat, Dept Adapt Syst, Pod Vodarenskou Vezi 4, CR-18208 Prague 8, Czech Republic
来源
PROCEEDINGS OF THE 13TH IASTED INTERNATIONAL CONFERENCE ON ROBOTICS AND APPLICATIONS/PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON TELEMATICS | 2007年
关键词
multi-level control; predictive control; trajectory planning;
D O I
暂无
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
The paper deals with the concepts of model-based control and trajectory planning intended for industrial parallel robots. These robots are characterized by very good dynamical properties arisen from small number of moving masses in comparison with conventional configurations. In the paper, multi-level (hierarchical) control will be investigated. It can be specified as a model-based control providing positional and speed loops with addition of fast low-level current-loop control. As a suitable representative of model-based control, predictive control is considered. Described concept can offer more possibilities to manage the control process than usual cascade control. Finally, the paper outlines two different concepts of trajectory planning. The first concept considers only pure geometrical features (curve-based planning) without relation to the real robots. The second concept conversely takes into account the dynamical features of the real robot with initial and final points (point-to-point planning).
引用
收藏
页码:15 / +
页数:2
相关论文
共 50 条
  • [21] Smooth Trajectory Planning Based on Direct Collocation Method for Cable-Driven Parallel Robots with Central Spine
    M. Badrikouhi
    M. Bamdad
    Mechanics of Solids, 2022, 57 : 652 - 670
  • [22] Smooth Trajectory Planning Based on Direct Collocation Method for Cable-Driven Parallel Robots with Central Spine
    Badrikouhi, M.
    Bamdad, M.
    MECHANICS OF SOLIDS, 2022, 57 (03) : 652 - 670
  • [23] Using First Principles for Deep Learning and Model-Based Control of Soft Robots
    Johnson, Curtis C.
    Quackenbush, Tyler
    Sorensen, Taylor
    Wingate, David
    Killpack, Marc D.
    FRONTIERS IN ROBOTICS AND AI, 2021, 8
  • [24] Rest-to-Rest Trajectory Planning for Underactuated Cable-Driven Parallel Robots
    Ida, Edoardo
    Bruckmann, Tobias
    Carricato, Marco
    IEEE TRANSACTIONS ON ROBOTICS, 2019, 35 (06) : 1338 - 1351
  • [25] Comparing model-based control methods for simultaneous stiffness and position control of inflatable soft robots
    Best, Charles M.
    Rupert, Levi
    Killpack, Marc D.
    INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2021, 40 (01): : 470 - 493
  • [26] A General Kinematics Model for Trajectory Planning of Upper Limb Exoskeleton Robots
    Meng, Qiaoling
    Xie, Qiaolian
    Deng, Zhimeng
    Yu, Hongliu
    INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PART VI, 2019, 11745 : 63 - 75
  • [27] Optimal trajectory planning for industrial robots
    Gasparetto, A.
    Zanotto, V.
    ADVANCES IN ENGINEERING SOFTWARE, 2010, 41 (04) : 548 - 556
  • [28] SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors
    Omar, Shafeef
    Amatucci, Lorenzo
    Barasuol, Victor
    Turrisi, Giulio
    Semini, Claudio
    2023 IEEE-RAS 22ND INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS, HUMANOIDS, 2023,
  • [29] Energy-optimal transport trajectory planning and online trajectory modification for holonomic robots
    Kim, Hongjun
    Kim, Byung Kook
    ASIAN JOURNAL OF CONTROL, 2021, 23 (05) : 2185 - 2200
  • [30] Tracking-error model-based predictive control for mobile robots in real time
    Klancar, Gregor
    Skrjanc, Igor
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2007, 55 (06) : 460 - 469