An adaptive spinal-like controller: tunable biomimetic behavior for a robotic limb

BioMedical Engineering OnLine

Filip Stefanovic¹  Henrietta L Galiana¹ 

[1] Department of Biomedical Engineering, McGill University, 3775, rue University, Room 316, Montréal, QC H3A 2B4, Canada

关键词: Biomimetic;    Tunable regulator;    Goal-oriented reaching;    Arm motions;    Adaptive control;   

Others  :  1084225
DOI  :  10.1186/1475-925X-13-151

 received in 2014-04-14, accepted in 2014-11-03,  发布年份 2014

【 摘 要 】

Background

Spinal-like regulators have recently been shown to support complex behavioral patterns during volitional goal-oriented reaching paradigms. We use an interpretation of the adaptive spinal-like controller as inspiration for the development of a controller for a robotic limb. It will be demonstrated that a simulated robot arm with linear actuators can achieve biological-like limb movements. In addition, it will be shown that programmability in the regulator enables independent spatial and temporal changes to be defined for movement tasks, downstream of central commands using sensory stimuli. The adaptive spinal-like controller is the first to demonstrate such behavior for complex motor behaviors in multi-joint limb movements.

Methods

The controller is evaluated using a simulated robotic apparatus and three goal-oriented reaching paradigms: 1) shaping of trajectory profiles during reaching; 2) sensitivity of trajectories to sudden perturbations; 3) reaching to a moving target. The experiments were designed to highlight complex motor tasks that are omitted in earlier studies, and important for the development of improved artificial limb control.

Results

In all three cases the controller was able to reach the targets without a priori planning of end-point or segmental motor trajectories. Instead, trajectory spatio-temporal dynamics evolve from properties of the controller architecture using the spatial error (vector distance to goal). Results show that curvature amplitude in hand trajectory paths are reduced by as much as 98% using simple gain scaling techniques, while adaptive network behavior allows the regulator to successfully adapt to perturbations and track a moving target. An important observation for this study is that all motions resemble human-like movements with non-linear muscles and complex joint mechanics.

Conclusions

The controller shows that it can adapt to various behavioral contexts which are not included in previous biomimetic studies. The research supplements an earlier study by examining the tunability of the spinal-like controller for complex reaching tasks. This work is a step toward building more robust controllers for powered artificial limbs.

【 授权许可】

   
2014 Stefanovic and Galiana; licensee BioMed Central Ltd.

【 图 表 】

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