2016
DOI: 10.1109/tcst.2015.2429615
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Mixed Integer Programming-Based Semiautonomous Step Climbing of a Snake Robot Considering Sensing Strategy

Abstract: We propose a control method for semiautonomous step climbing by a snake robot. Our method is based on mixed integer quadratic programming to generate the reference trajectory of the head of the snake robot online. One of the features of the method is that it determines suitable positions and time duration in which to sense the surroundings before approaching the step. Furthermore, constraints on velocity and acceleration are taken into account, so that the snake robot can securely follow the generated trajecto… Show more

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Cited by 23 publications
(6 citation statements)
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“…An adaptive slope control using feedback CPG network by varying speed and winding angle is proposed in [57]. Another CPG model for effective terrain exploration is discussed on various slopes and terrain in [58]. The concept of climbing comes where the snake robot must lift its body to traverse some obstacles.…”
Section: B Dynamicsmentioning
confidence: 99%
“…An adaptive slope control using feedback CPG network by varying speed and winding angle is proposed in [57]. Another CPG model for effective terrain exploration is discussed on various slopes and terrain in [58]. The concept of climbing comes where the snake robot must lift its body to traverse some obstacles.…”
Section: B Dynamicsmentioning
confidence: 99%
“…Pettersen et al [2] applied integral line-of-sight guidance control for underwater snake robots to adapt to ocean currents of unknown direction and magnitude. Other nonlinear algorithms like sliding mode control [12] and mixed integer programming [13] have also been investigated to produce gaits.…”
Section: Related Workmentioning
confidence: 99%
“…Intelligent techniques, such as neural networks, fuzzy logic, and genetic algorithms, have powerful self-learning, adaptive, and fault-tolerant capabilities that have attracted many robotics researchers to apply them to gait planning. In 1992, each joint of robot SD-2 was represented by a joint neuron [67]. In the study, the neural network obtained the relationship between the foot force and the angle of the corresponding joint angle adjustment.…”
Section: • Gait Planning Based On Intelligent Algorithmmentioning
confidence: 99%