2017
DOI: 10.3389/fnbot.2017.00039
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Adaptive Control Strategies for Interlimb Coordination in Legged Robots: A Review

Abstract: Walking animals produce adaptive interlimb coordination during locomotion in accordance with their situation. Interlimb coordination is generated through the dynamic interactions of the neural system, the musculoskeletal system, and the environment, although the underlying mechanisms remain unclear. Recently, investigations of the adaptation mechanisms of living beings have attracted attention, and bio-inspired control systems based on neurophysiological findings regarding sensorimotor interactions are being d… Show more

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Cited by 91 publications
(64 citation statements)
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References 162 publications
(278 reference statements)
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“…Locomotion is a complex process that requires various components with real-time interaction between motor control functions and body dynamics through sensory feedback (embodied sensorimotor interaction) [ 13 ]. For example, it is known that stick insects do not generate coordinated motor outputs without sensory feedback [ 14 , 15 ], which indicates that sensory feedback plays a critical role in shaping these motor patterns.…”
Section: Introductionmentioning
confidence: 99%
“…Locomotion is a complex process that requires various components with real-time interaction between motor control functions and body dynamics through sensory feedback (embodied sensorimotor interaction) [ 13 ]. For example, it is known that stick insects do not generate coordinated motor outputs without sensory feedback [ 14 , 15 ], which indicates that sensory feedback plays a critical role in shaping these motor patterns.…”
Section: Introductionmentioning
confidence: 99%
“…ER has motivated many di erent control systems for legged robots [4]. Arti cial Neural Networks (ANNs) have been used extensively because of their ability to represent complex functions, given the right evolved structure and connection weights [5,14,34].…”
Section: Controllers In Evolutionary Roboticsmentioning
confidence: 99%
“…For instance, the differential oscillators 1 and 2 have N 1 = {2} and N 2 = {1, 3, 6, 8}, respectively, for the CPG network of the robot spider9. Subsequently, the output value of each differential oscillator can be calculated by Equation 3. To reduce the number of weights to be learned, we set w xioi = 1.0 in this work, i.e., the input of the out i -neuron equals the activation value x (i,t) of the x i -neuron.…”
Section: Robot Controllersmentioning
confidence: 99%