Narrow Results

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.