Controlling the dynamics of a two-joined arm by central patterning and reflex-like processing (original) (raw)
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Robotic locomotion combining Central Pattern Generators and reflexes
2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG), 2015
All the work carried during the past year would not be possible without the help, understanding and support from many people. First and foremost, I would like to express my deepest gratitude to my adviser Prof. Cristina Santos, who allowed me the possibility to integrate the CAR (Control, Automation and Robotics) group of the ALGORITMI center. I am extremely grateful for her dedication, support and constant motivation, providing me an excellent atmosphere to complete my thesis. I am sincerely grateful for her excellent guidance and perseverance, encouraging me to tackle the different problems of this research. The help, constructive criticism and advice that I received were invaluable to the final quality of this work, making suggestions that contributed to overcoming the difficulties that emerged during this journey. I would like to thank Prof. Lino Costa for the opportunity to learn new topics related with fitness evaluation. I want to express my gratitude to Prof. Auke Ijspeert, who was always available to help with whatever was needed, provided insighful discussions about the research and presented suggestions that helped me develop this accomplishment. I am especially thankful to everybody in the lab, for all the friendship and companionship that provided me with an excellent daily environment. A special thanks to João Macedo, who was always available to help me solve computational issues. I express my warm thank you to my girlfriend Bárbara for her support, dedication and patience during the last year. She assisted me when I was writing this thesis, giving me some insight to stay on track and complete this work successfully. Finally, I would like to thank my family, my parents and my brother, for their constant support, love and encouragement.
CPG based self-adapting multi-DOF robotic arm control
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
Recently, biologically inspired control approaches for robotic systems that involve the use of central pattern generators (CPGs) have been attracting considerable attention owing to the fact that most humans or animals move and walk easily without explicitly controlling their movements. Furthermore, they exhibit natural adaptive motions against unexpected disturbances or environmental changes without considering their kinematic configurations. Inspired by such novel phenomena, this paper endeavors to achieve self-adapting robotic arm motion. For this, biologically inspired CPG based control is proposed. In particular, this approach deals with crucial problems such as motion generation and repeatability of the joints emerged remarkably in most of redundant DOF systems. These problems can be overcome by employing a control based on artificial neural oscillators, virtual force and virtual muscle damping instead of trajectories planning and inverse kinematics. Biologically inspired motions can be attained if the joints of a robotic arm are coupled to neural oscillators and virtual muscles. We experimentally demonstrate self-adaptation motions that that enables a 7-DOF robotic arm to make adaptive changes from the given motion to a compliant motion. In addition, it is verified with real a real robotic arm that human-like movements and motion repeatability are satisfied under kinematic redundancy of joints.
Improved control for an artificial arm
2005
Successful control of multi-degree of freedom upper limb prostheses generally uses some form of sequential instruction. This is because simultaneous control of multiple inputs requires a considerable concentration to be operated effectively. In contrast, the natural arm is controlled in a parallel manner with a high level of subconscious control. Such control uses feedback, the person is rarely conscious of the feedback information, and most of the control is automatic. Attempts to achieve similar control with a prosthesis would requires a wide bandwidth feedback channel to the controller. This is currently impractical if the controller of a multiple degree of freedom arm is the wearer, because only very low frequency feedback is achievable. The Southampton Arm control philosophy avoids this bottleneck by keeping the low level control within the prosthesis and leaves low bandwidth and strategic control to the operator [1].
Undoubtedly, movement is one of the essential characteristics of living beings. Despite the diversity of animal species and the apparent differences, standard features exist between their movement systems that follow a particular pattern. The movements are mainly divided into rhythmic and discrete categories controlled by the central nervous system. Scientists usually consider these two types of motion separately in the control system and use different methods and resources to produce and model them. Proposing a unified and comprehensive model for generating and controlling rhythmic and discrete movement with the same techniques is more valuable albeit challenging. The present study provides a single neuromechanical control model for producing and managing both rhythmic and discrete movements. This model consists of a neural oscillator, the central pattern generator (CPG), coupled with inhibitory and excitatory paths to drive the flexor and extensor muscles. The computational model ...
Combining central pattern generators and reflexes
Neurocomputing, 2015
Locomotion of quadruped robots has not yet achieved the robustness, harmony, efficiency and flexibility of its biological counterparts. Biological evidences showed that there is a two-way interaction between the Central Pattern Generators (CPGs) and the body in the locomotion process of animals. Therefore, the development of bio-inspired controllers seems to be a good and robust way to obtain an efficient and robust robotic locomotion. This contribution presents an innovative hybrid controller that generates locomotion through the combination of CPGs and reflexes. The results show that the hybrid controller is capable of producing stable quadruped locomotion with a regular stepping pattern. Furthermore, it proved to be able to deal with slopes without changing the parameters and with small obstacles, overcoming them successfully.
The handbook of brain theory and neural networks,, 2002
The control of arm and hand movements in human and nonhuman primates has fascinated researchers in psychology, neuroscience, robotics, and numerous related areas. Movement appears effortless to the uninitiated observer���only when trying to duplicate such skills with artificial systems or when examining the underlying neural substrate, one discovers a surprising complexity that, so far, has prevented us from understanding the biological implementation, how to repair neural damage, and how to create human-like robots with a ...
Biologically inspired control for robotic arm using neural oscillator network
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009
It is known that biologically inspired neural systems could exhibit natural dynamics efficiently and robustly for motion control, especially for rhythmic motion tasks. In addition, humans or animals exhibit natural adaptive motions without considering their kinematic configurations against unexpected disturbances or environment changes. In this paper, we focus on rhythmic arm motions that can be achieved by using a controller based on neural oscillators and virtual force. In comparison with conventional researches, this work treats neither trajectories planning nor inverse kinematics. Instead of those, a few desired points in task-space and a control method with Jacobian transpose and joint velocity damping are merely adopted. In addition, if the joints of robotic arms are coupled to neural oscillators, they may be capable of achieving biologically inspired motions corresponding to environmental changes. To verify the proposed control scheme, we perform some simulations to trace a desired motion and show the potential features related with self-adaptation that enables a three-link planar arm to make adaptive changes from the given motion to a compliant motion. Specifically, we investigate that human-like movements and motion repeatability are satisfied under kinematic redundancy of joints.
Human-like reflex control for an artificial hand
Biosystems, 2004
We illustrate the low level reflex control used to govern an anthropomorphic artificial hand. The paper develops the position and force control strategy based on dynamic artificial neurons able to simulate the natural neurons found in the human reflex control. The controller has a hierarchical structure. At the lowest level there are the receptors able to convert the analogical signal into a neural impulsive signal appropriate to govern the reflex control neurons. Immediately upon it, the artificial motoneurons set the actuators inner pressure to control the finger joint position and moment. Other auxiliary neurons in combination with the motoneurons are able to set the finger stiffness and emulate the inverse miotatic reflex control. Stiffness modulation is important both to save energy during task execution, and to manage objects made of different materials. The inverse miotatic reflex is able to protect the hand from possible harmful external actions. The paper also presents the dynamic model of the joints and of the artificial muscles inserted in Blackfingers, our artificial hand. This new type of neural control has been simulated on the Blackfingers model; the results indicate that the developed control is very flexible and efficient for all kind of joints present in the humanoid hand.
PLOS ONE
In this study, we introduce a hierarchical and modular computational model to explain how the CNS (Central Nervous System) controls arm reaching movement (ARM) in the frontal plane and under different conditions. The proposed hierarchical organization was established at three levels: 1) motor planning, 2) command production, and 3) motor execution. Since in this work we are not discussing motion learning, no learning procedure was considered in the model. Previous models mainly assume that the motor planning level produces the desired trajectories of the joints and feeds it to the next level to be tracked. In the proposed model, the motion control is described based on a regulatory control policy, that is, the output of the motor planning level is a step function defining the initial and final desired position of the hand. For the command production level, a nonlinear predictive model was developed to explain how the time-invariant muscle synergies (MSs) are recruited. We used the same computational model to explain the arm reaching motion for a combined ARM task. The combined ARM is defined as two successive ARM such that it starts from point A and reaches to point C via point B. To develop the model, kinematic and kinetic data from six subjects were recorded and analyzed during ARM task performance. The subjects used a robotic manipulator while moving their hand in the frontal plane. The EMG data of 15 muscles were also recorded. The MSs used in the model were extracted from the recorded EMG data. The proposed model explains two aspects of the motor control system by a novel computational approach: 1) the CNS reduces the dimension of the control space using the notion of MSs and thereby, avoids immense computational loads; 2) at the level of motor planning, the CNS generates the desired position of the hand at the starting, via and the final points, and this amounts to a regulatory and non-tracking structure.