Abstract
Human beings can achieve a high level of motor performance that is still unmatched in robotic systems. These capabilities can be ascribed to two main enabling factors: (i) the physical proprieties of human musculoskeletal system, and (ii) the effectiveness of the control operated by the central nervous system. Regarding point (i), the introduction of compliant elements in the robotic structure can be regarded as an attempt to bridge the gap between the animal body and the robot one. Soft articulated robots aim at replicating the musculoskeletal characteristics of vertebrates. Yet, substantial advancements are still needed under a control point of view, to fully exploit the new possibilities provided by soft robotic bodies. This paper introduces a control framework that ensures natural movements in articulated soft robots, implementing specific functionalities of the human central nervous system, i.e., learning by repetition, after-effect on known and unknown trajectories, anticipatory behavior, its reactive re-planning, and state covariation in precise task execution. The control architecture we propose has a hierarchical structure composed of two levels. The low level deals with dynamic inversion and focuses on trajectory tracking problems. The high level manages the degree of freedom redundancy, and it allows to control the system through a reduced set of variables. The building blocks of this novel control architecture are well-rooted in the control theory, which can furnish an established vocabulary to describe the functional mechanisms underlying the motor control system. The proposed control architecture is validated through simulations and experiments on a bio-mimetic articulated soft robot.
Highlights
Activities of human beings are a clear example of the exceptional versatility of their motor control system
This novel generation of systems, namely soft robots, can be categorized as invertebrate-inspired or vertebrate-inspired (Della Santina et al, 2020). The latter class includes articulated soft robots, which are systems with rigid links and elasticity lumped at the joints (Albu-Schaffer et al, 2008). We focus on the latter category, i.e., robots actuated by series elastic actuators (SEA) (Pratt and Williamson, 1995) or variable stiffness actuators (VSA) (Vanderborght et al, 2013)
The model of the actuators takes into account hardware parameters, such as measure noise, communication delays, saturations, motors dynamics2
Summary
Activities of human beings are a clear example of the exceptional versatility of their motor control system. Tasks that are still challenging for robots are executed by people. Responsible for such a high level of performance are the musculoskeletal system and the Central Nervous System (CNS). The musculoskeletal system allows to exert forces and to percept the external world through a multitude of receptors. One of the main characteristics of this system is its compliant nature. Body flexibility provided by muscles and tendons enables features like energy efficiency, power amplification and shock absorption (Roberts and Azizi, 2011)
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