A New Type of Industrial Robot Trajectory Generation Component Based on Motion Modularity Technology

Motion modularity is the main method of motion control for higher animals. That means the complex movements of the muscles are made up of basic motion primitives, and the brain or central nervous system does not care about the specific details of the movement. However, the industrial robot control system does not adopt the technical roadmap of motion modularity, it generates complex trajectories by providing a large number of sampling points. This approach is equivalent to using the brain to directly guide the specific movement of the muscle and has to rely on a faster Fieldbus system to obtain complex motion trajectories. This work constructs a modularized industrial robot trajectory generation component based on Dynamic Movement Primitives (DMP) theory. With this component, the robot controller can generate complex trajectories without increasing the sampling points and can obtain good trajectory accuracy. Finally, the rationality of this system is proved by simulations and experiments.

[1]  Jun Nakanishi,et al.  Learning Attractor Landscapes for Learning Motor Primitives , 2002, NIPS.

[3]  Hongyi Li,et al.  MDL-based control method for mobile robot with randomly varying time-delay , 2011, 2011 IEEE International Conference on Robotics and Automation.

[4]  Roger W. Brockett,et al.  Hybrid Models for Motion Control Systems , 1993 .

[5]  Tamar Flash,et al.  Kinematic decomposition and classification of octopus arm movements , 2013, Front. Comput. Neurosci..

[6]  Jun Nakanishi,et al.  Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors , 2013, Neural Computation.

[7]  Michelle Karg,et al.  Movement Primitive Segmentation for Human Motion Modeling: A Framework for Analysis , 2016, IEEE Transactions on Human-Machine Systems.

[8]  Andrea d'Avella,et al.  Modularity for Motor Control and Motor Learning. , 2016, Advances in experimental medicine and biology.

[9]  Emilio Bizzi,et al.  Muscle synergies evoked by microstimulation are preferentially encoded during behavior , 2014, Front. Comput. Neurosci..

[10]  Naveen Kuppuswamy,et al.  Do muscle synergies reduce the dimensionality of behavior? , 2014, Front. Comput. Neurosci..

[11]  Wei Qing,et al.  Motion description language for trajectory generation of a robot manipulator , 2017, 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[12]  Gargas,et al.  Generation and use of a discrete robotic controls alphabet for high-level tasks , 2012 .

[13]  Andrea d'Avella,et al.  Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems , 2013, Front. Comput. Neurosci..

[14]  Roger W. Brockett.  Language driven hybrid systems , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[15]  Olivier Sigaud,et al.  Many regression algorithms, one unified model: A review , 2015, Neural Networks.

[16]  Dario Farina,et al.  A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives , 2013, Front. Comput. Neurosci..

[17]  Surya P. N. Singh,et al.  V-REP: A versatile and scalable robot simulation framework , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.