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The spine of mammals aids in the stability of locomotion. Central Pattern Generators (CPGs) located in spinal cord can rapidly provide a rhythmic output signal during loss of sensory feedback on the basis of a simulated quadruped agent. In this paper, active spine of quadruped robot is shown to be extremely effective in motion. An active spine model based on the Parallel Kinematic Mechanism (PKM) system and biological phenomena is described. The general principles involved in constructing a neural network coupled with limbs and spine to solve specific problems are discussed. A CPG mathematical model based on Hopf nonlinear oscillators produces rhythmic signal during locomotion is described, where many parameters to be solved must be formulated in terms of desired stability, often subject to vertical stability analysis. Our simulations demonstrate that active spine with setting reasonable CPG parameters can reduce unnecessary lateral displacement during trot gait, improving the stability of quadruped robot. In addition, we demonstrate that physical prototype mechanism provides a framework which shows correctness of simulation, and stability can thus be easily embodied within locomotion.

Recent advances in control of humanoid robots have resulted in bipedal gaits that are dynamically stable on moderately rough terrain but are still limited to a small range of slopes. Humanoid robots, like humans, can take advantage of quadruped gaits to greatly extend this range. Cleverly designed gaits can provide robustness to rough terrain without requiring extensive feedback. In this paper, we present a robust crab-walking framework that includes forward and backward crawling patterns, rotation patterns, and sit-down and recovery sequences. The latter are activated autonomously once the robot detects that it tipped over. The performance and robustness of each locomotion pattern are investigated over a wide range of slopes. Crab-walking is shown to be especially adept at crawling forward on steep downward slopes (up to -54°) and crawling backward on steep upward slopes (up to 18°). Finally, we demonstrate the framework's autonomous capabilities by crossing the rough terrain in DARPA's virtual robotics challenge.

Wearable robotic systems have been a mechanism which clearly drives the motive of bringing back paraplegics back on their feet as well as executing difficult task beyond human ability. The purpose of this research study is to design and investigate the efficacy of rehabilitative walking in patients with lower limb disorders using oscillators which may commonly be referred to as central pattern generators (CPGs). In order to achieve this, a rhythmic trajectory is designed using Van der Pol oscillators. This rhythmic trajectory commensurates with the movement pattern of the hips and knees for a normal walking gait of humans. The dynamical model of a five-link biped exoskeletal device having four actuated joints is computed with regard to the wearer using Lagrangian principles in the sagittal plane. A feedback linearization control technique is therefore utilized for tracking the rhythmic trajectory to achieve a proper following of the human walking gait. Matlab/Simulink is used to validate this proposed strategy in the presence of uncertainties with a view to implementing it practically in the laboratory with human in the loop. Results show that humans with the aid of the exoskeleton device will possess the ability to track this rhythmic trajectory representing the hip and knee joint movements. The controller proved robust enough against disturbance.

This paper proposes a novel central pattern generator (CPG) model with proprioceptive mechanism and the dynamic connectivity mechanism. It not only contains the sensory information of the environment but also contains the information of the actuators and automatically tunes the parameters of CPG corresponding to the actuators information and inner sensory information. The position of the joints linked directly with the output of CPG is introduced to the CPG to find its proprioceptive system, spontaneously making the robot realize the actuator working status, further changing the CPG output to fit the change and decrease the influence of the problematic joints or actuators on the robot being controlled. So the damage would be avoided and self-protection is implemented. Its application on the locomotion control of a quadruped robot demonstrates the effectiveness of the proposed approach.

This paper presents a structure of coupled neural oscillator which can be used as central pattern generator(CPG), for humanoid robot locomotion. Parameters tuning is always a big challenge for oscillators. In this article, some properties of neural oscillator, like limit cycle requirement, amplitude and frequency are studied. The objective is to develop a trajectory generator which has simple parameters' tuning. This architecture is suitable in various fields where rhythmic motion is required. We tested the oscillator structure on a simulated biped robot. The result demonstrates a stable walking behavior in both forward and backward straight walking and different walking speeds.

It was proven that it is possible to build a gait pattern generators, producing the human-like leg joints trajectories. For this purpose the van der Pol equations, as well as the neural networks can be applied. A robot described in this paper will serve as a platform for research on implementation of the mentioned gait pattern generators in the bipedal antropomorphic structures. The paper contains the detailed design process description, especially the dynamic analysis process. The mechanical design methods, as well as the designed preload mechanism and the foot compliance are also presented.

The plantar area of the human foot is larger than that of the quadruped foot, and it contains a large number of sensory organs. Thus, such a foot structure plays a crucial role in extracting “rich” sensory information for the generation of adaptive walking in humans. Here, we propose novel central pattern generator (CPG)-based control of a bipedal walking robot by exploiting plantar sensation. To effectively exploit plantar sensory information, we redesign the local sensory feedback to the CPG model that we previously proposed for quadruped robots. The simulation results indicate that the biped model exhibits a remarkably robust walking ability by exploiting the plantar sensation according to the current walking motion.

Smart intelligent robotic fish has shown promising advantage in underwater searching. This paper addresses the smart robotic shark design and control issues with multi-sensors. In particular, we propose a new design of a two-link mechanism robotic shark equipped with gyroscope, pressure sensor, infrared sensor, and light sensor. Then three-dimensional motion control, depth control, autonomous obstacle avoidance, and light navigation are developed. In particular, a bio-inspired Central Pattern Generator (CPG) based control method is adopted to smoothly control the robotic shark's locomotion in all the above realization. All motion control methods are implemented in real time with a hybrid control system based on embedded microprocessor (STMicroelectronics STM32F407). Latest aquatic experiments demonstrate a fairly good result in improving the robotic shark's intelligence. The developed scheme affords an alternative to smart robotic fish design in complex underwater environments.