4 research outputs found
Distributed Bio-inspired Humanoid Posture Control
This paper presents an innovative distributed bio-inspired posture control
strategy for a humanoid, employing a balance control system DEC (Disturbance
Estimation and Compensation). Its inherently modular structure could
potentially lead to conflicts among modules, as already shown in literature. A
distributed control strategy is presented here, whose underlying idea is to let
only one module at a time perform balancing, whilst the other joints are
controlled to be at a fixed position. Modules agree, in a distributed fashion,
on which module to enable, by iterating a max-consensus protocol. Simulations
performed with a triple inverted pendulum model show that this approach limits
the conflicts among modules while achieving the desired posture and allows for
saving energy while performing the task. This comes at the cost of a higher
rise time.Comment: 2019 41st Annual International Conference of the IEEE Engineering in
Medicine & Biology Society (EMBC
Human-Likeness Indicator for Robot Posture Control and Balance
Similarly to humans, humanoid robots require posture control and balance to
walk and interact with the environment. In this work posture control in
perturbed conditions is evaluated as a performance test for humanoid control. A
specific performance indicator is proposed: the score is based on the
comparison between the body sway of the tested humanoid standing on a moving
surface and the sway produced by healthy subjects performing the same
experiment. This approach is here oriented to the evaluation of a
human-likeness. The measure is tested using a humanoid robot in order to
demonstrate a typical usage of the proposed evaluation scheme and an example of
how to improve robot control on the basis of such a performance indicator scoreComment: 16 pages, 5 Figures. arXiv admin note: substantial text overlap with
arXiv:2110.1439
Human-Derived Disturbance Estimation and Compensation (DEC) Method Lends Itself to a Modular Sensorimotor Control in a Humanoid Robot
The high complexity of the human posture and movement control system represents challenges for diagnosis, therapy, and rehabilitation of neurological patients. We envisage that engineering-inspired, model-based approaches will help to deal with the high complexity of the human posture control system. Since the methods of system identification and parameter estimation are limited to systems with only a few DoF, our laboratory proposes a heuristic approach that step-by-step increases complexity when creating a hypothetical human-derived control systems in humanoid robots. This system is then compared with the human control in the same test bed, a posture control laboratory. The human-derived control builds upon the identified disturbance estimation and compensation (DEC) mechanism, whose main principle is to support execution of commanded poses or movements by compensating for external or self-produced disturbances such as gravity effects. In previous robotic implementation, up to 3 interconnected DEC control modules were used in modular control architectures separately for the sagittal plane or the frontal body plane and successfully passed balancing and movement tests. In this study we hypothesized that conflict-free movement coordination between the robot's sagittal and frontal body planes emerges simply from the physical embodiment, not necessarily requiring a full body control. Experiments were performed in the 14 DoF robot Lucy Posturob (i) demonstrating that the mechanical coupling from the robot's body suffices to coordinate the controls in the two planes when the robot produces movements and balancing responses in the intermediate plane, (ii) providing quantitative characterization of the interaction dynamics between body planes including frequency response functions (FRFs), as they are used in human postural control analysis, and (iii) witnessing postural and control stability when all DoFs are challenged together with the emergence of inter-segmental coordination in squatting movements. These findings represent an important step toward controlling in the robot in future more complex sensorimotor functions such as walking