Bio-inspired central pattern generator for development of Cartesian motor skills for a Quadruped Robot

Central Pattern Generator (CPG) plays a significant role in the generation of diverse and stable gaits patterns for animals as well as controlling their locomotion. The main contributions of this paper are the ability to develop the Cartesian motor skills and coordinating legs of the quadruped robot for gait adaption and its nominal characteristics with CPG approach. Primary, a predefined relationship between an excitation signal and essential parameters of the CPG design is programmed. Next, the coordinated oscillator's rhythmic patterns by CPG and accordingly output gait diagrams for each foot of the robot are attained. Then, these desirable features such as predictive modulation and programming the gait event sequences including leg-lifting sequences and step length, duration of the time of each footstep within a gait, coordination of swing and stance phases of all legs are calculated in terms of different spatio_temporal vectors. Furthermore, a novel Cartesian footstep basis function is designed based on the robot characteristics and consequently, the associated spatio-temporal vectors can be inserted to it, which caused to spanning the space of possible gait timing in Cartesian space. Next, Cartesian footstep planner can be computed the swing foot trajectories in workspace along movement axes and then according to these footholds and feet placement, ZMP (Zero Moment Point) reference trajectory will be calculated and obtained. Therefore, COG (Center of Gravity) trajectory can be computed by designing a preview controller on the basis of the desired ZMP trajectory. Finally, to demonstrate the effectiveness of the proposed algorithm, it is implemented on a quadruped robot on both simulation or experimental implementations and the results are compared and discussed with other references.

Motion Control of a Gecko-like Robot Based on a Central Pattern Generator

To solve the problem of the motion control of gecko-like robots in complex environments, a central pattern generator (CPG) network model of motion control was designed. The CPG oscillation model was first constructed using a sinusoidal function, resulting in stable rhythm control signals for each joint of the gecko-like robot. Subsequently, the gecko-like robot successfully walked, crossed obstacles and climbed steps in the vertical plane, based on stable rhythm control signals. Both simulations and experiments validating the feasibility of the proposed CPG motion control model are presented.

Autonomous speed adaptation by a muscle-driven hind leg robot modeled on a cat without intervention from brain

This study aims to design a nervous system model to drive the realistic muscle-driven legs for the locomotion of a quadruped robot. We evaluate our proposed nervous system model with a hind leg simulated model and robot. We apply a two-level central pattern generator for each leg, which generates locomotion rhythms and reproduces cat-like leg trajectories by driving different sets of the muscles at any timing during one cycle of moving the leg. The central pattern generator receives sensory feedback from leg loading. A cat simulated model and a robot with two hind legs, each with three joints driven by six muscle models, are controlled by our nervous system model. Even though their hind legs are forced backward at a wide range of speeds, they can adapt to the speed variation by autonomously adjusting its stride and cyclic duration without changing any parameters or receiving any descending inputs. In addition to the autonomous speed adaptation, the cat hind leg robot switched from a trot-like gait to a gallop-like gait while speeding up. These features can be observed in existing animal locomotion tests. These results demonstrate that our nervous system is useful as a valid and practical legged locomotion controller.

Inhibition and Facilitation of the Spinal Locomotor Central Pattern Generator and Reflex Circuits by Somatosensory Feedback From the Lumbar and Perineal Regions After Spinal Cord Injury

Somatosensory feedback from peripheral receptors dynamically interacts with networks located in the spinal cord and brain to control mammalian locomotion. Although somatosensory feedback from the limbs plays a major role in regulating locomotor output, those from other regions, such as lumbar and perineal areas also shape locomotor activity. In mammals with a complete spinal cord injury, inputs from the lumbar region powerfully inhibit hindlimb locomotion, while those from the perineal region facilitate it. Our recent work in cats with a complete spinal cord injury shows that they also have opposite effects on cutaneous reflexes from the foot. Lumbar inputs increase the gain of reflexes while those from the perineal region decrease it. The purpose of this review is to discuss how somatosensory feedback from the lumbar and perineal regions modulate the spinal locomotor central pattern generator and reflex circuits after spinal cord injury and the possible mechanisms involved. We also discuss how spinal cord injury can lead to a loss of functional specificity through the abnormal activation of functions by somatosensory feedback, such as the concurrent activation of locomotion and micturition. Lastly, we discuss the potential functions of somatosensory feedback from the lumbar and perineal regions and their potential for promoting motor recovery after spinal cord injury.