Predictive control of 5 DOF robot arm of autonomous mobile robotic system motion control employing mathematical model of the robot arm dynamics

Belda, Květoslav; Rovný, Oliver

doi:10.1109/pc.2017.7976237

Cited by 15 publications

(4 citation statements)

References 5 publications

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“…Lam et al [24], Arkadani et al [25] and Belda et al [26] carried out trajectory generation with MPC for a single robotic manipulator without collision avoidance. There are two possible approaches integrating collision avoidance into trajectory generation with MPC.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Collision and Deadlock Avoidance for Multiple Robotic Manipulators

et al. 2022

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A flexible operation of multiple robotic manipulators operating in a dynamic environment requires online trajectory planning to ensure collision-free trajectories. In this work, we propose a real-time capable motion control algorithm, based on nonlinear model predictive control, which accounts for static and dynamic obstacles. The proposed algorithm is realized in a distributed scheme, where each robot optimizes its own trajectory with respect to the related objective and constraints. We propose a novel approach for collision avoidance between multiple robotic manipulators, where each robot accounts for the predicted movement of the neighboring robots. Additionally, we propose a method to reliably detect and resolve deadlocks occurring in a setup of multiple robotic manipulators. We validate our approach on pick and place scenarios involving multiple robotic manipulators operating in a common workspace in a realistic simulation environment set up in Gazebo. The robots are controlled using the Robot Operating System. Our approach scales up to 4 manipulators and computes a path for each robot in a simultaneous pick and place operation in 94% of all investigated cases without deadlock detection and 100 % of cases with the proposed deadlock resolution algorithm. In contrast, the investigated conventional path planners, such as PRM, PRM*, CHOMP and RRT-Connect, successfully plan a trajectory in at most 54% of all investigated cases for a simultaneous operation of 4 robotic manipulators hindering their application in setups of multiple manipulators.INDEX TERMS Robotic manipulators, collision avoidance, distributed model predictive control, motion control, deadlock, ROS.

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Section: Related Workmentioning

confidence: 99%

Dynamic Collision and Deadlock Avoidance for Multiple Robotic Manipulators

et al. 2022

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“…The closed-loop control accounts for model uncertainties and disturbances, whereas the predictive nature of MPC gives an insight on the future trajectory. A selection of literature for point-to-point trajectory generation with MPC for a single robotic manipulator without collision avoidance was done by Lam et al [13], Arkadani et al [14] and Belda et al [15].…”

Section: Related Workmentioning

confidence: 99%

Dynamic collision avoidance for multiple robotic manipulators based on a non-cooperative multi-agent game

Gafur¹,

Kanagalingam²,

Ruskowski³

2021

Preprint

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A flexible operation of multiple robotic manipulators in a shared workspace requires an online trajectory planning with static and dynamic collision avoidance. In this work, we propose a real-time capable motion control algorithm, based on non-linear model predictive control, which accounts for static and dynamic collision avoidance. The proposed algorithm is formulated as a non-cooperative game, where each robot is considered as an agent. Each agent optimizes its own motion and accounts for the predicted movement of surrounding agents. We propose a novel approach to formulate the dynamic collision constraints. Additionally, we account for deadlocks that might occur in a setup of multiple robotic manipulators. We validate our algorithm on a pick and place scenario for four collaborative robots operating in a common workspace in the simulation environment Gazebo. The robots are controlled by the Robot Operating System (ROS). We demonstrate, that our approach is real-time capable and, due to the distributed nature of the approach, easily scales to an arbitrary number of robot manipulators in a shared workspace.

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“…In order to improve the effectiveness of training, many scholars put forward different strategies. Some of the practical applicationsused are still classical proportional-integral-derivative (PID) control [6][7][8], and Belda et al use model predictive control [9]. Baigzadehnoe et al proposed an adaptive fuzzy backstepping position tracking control scheme for multi-robot manipulator system by adopting the backstepping design strategy [10].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the practical applicationsused are still classical proportional‐integral‐derivative (PID) control [6–8], and Belda et al. use model predictive control [9]. Baigzadehnoe et al.…”

Section: Introductionmentioning

confidence: 99%

Control of upper limb rehabilitation robot based on active disturbance rejection control

Zhang

Zheng

et al. 2021

IET Cyber-Syst and Robotics

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The upper limb rehabilitation robot technology integrates rehabilitation medicine, human anatomy, mechanics, computer science, robotics, and many other disciplines. Its main function is to drive the affected limb to carry out rehabilitation training to restore the condition of patients with upper limb dyskinesia, which plays a great role in improving the quality of life. In this study, to resolve the problems of slow convergence speed and poor tracking accuracy due to the interference of patient spasms with the trajectorytracking control of the upper limb rehabilitation robot, a novel algorithm based on active disturbance rejection control (ADRC) is adopted, and the convergence of its main structure is proved by the time-domain analysis method. First, this ADRC algorithm can obtain better trajectory-tracking performance due to its non-linear extended observer and non-linear feedback mechanism, even if the model suffers a strong disturbance or receives inaccurate information. Second, the non-linear tracking differentiator can guarantee to gain quick convergence speed. To validate this algorithm, a model of three degrees of freedom upper limb rehabilitation robot is established using MATLAB R2019b and three situations including strong spasm and weak spasm are carried out to prove the effectiveness and reliability of the control algorithm designed.

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Predictive control of 5 DOF robot arm of autonomous mobile robotic system motion control employing mathematical model of the robot arm dynamics

Cited by 15 publications

References 5 publications

Dynamic Collision and Deadlock Avoidance for Multiple Robotic Manipulators

Dynamic Collision and Deadlock Avoidance for Multiple Robotic Manipulators

Dynamic collision avoidance for multiple robotic manipulators based on a non-cooperative multi-agent game

Control of upper limb rehabilitation robot based on active disturbance rejection control

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