Paper Info
Title
Evolving joint-level control with digital muscles
Abstract
The neuromuscular systems of animals are governed by extremely complex networks of control signals, sensory feedback loops, and mechanical interactions. Morphology and control are inherently intertwined. In the case of animal joints, groups of muscles work together to provide power and stability to move limbs in a coordinated manner. In contrast, many robot controllers handle both high-level planning and low-level control of individual joints. In this paper, we propose a joint-level control method, called digital muscles, that operates in a manner analogous to biological muscles, yet is abstract enough to apply to conventional robotic joints. An individual joint is controlled by multiple muscle nodes, each of which responds to a control signal according to a node-specific activation function. Evolving the physical orientation of muscle nodes and their respective activation functions enables relatively complex and coordinated gaits to be realized with simple high-level control. Even using a sinusoid as the high-level control signal, we demonstrate the evolution of effective gaits for a simulated quadruped. The proposed model realizes a control strategy for governing the behavior of individual joints, and can be coupled with a high-level controller that focuses on decision making and planning.
Year
DOI
Venue
2014
10.1145/2576768.2598373
GECCO
Keywords
Field
DocType
digital muscles,bio-inspired design,joint-level control,autonomous vehicles,simulation,evolutionary robotics
Control theory,Evolutionary robotics,Gait,Computer science,Activation function,Control theory,Complex network,Robot,Sensory system
Conference
Citations 
PageRank 
References 
1
0.36
15
Authors
2
Name
Order
Citations
PageRank
Jared M. Moore1286.82
P. K. McKinley21397121.87