Compared with wheeled mobile robots, legged robots can easily step over obstacles and walk through rugged ground. They have more flexible bodies and therefore, can deal with complex environment. Nevertheless, some other issues make the locomotion control of legged robots a much complicated task, such as the redundant degree of freedoms and balance keeping. From literatures, locomotion control has been solved mainly based on programming mechanism. To use this method, walking trajectories for each leg and the gaits have to be designed, and the adaptability to an unknown environment cannot be guaranteed. From another aspect, studying and simulating animals’ walking mechanism for engineering application is an efficient way to break the bottleneck of locomotion control for legged robots. This has attracted more and more attentions. Inspired by central pattern generator (CPG), a control method has been proved to be a successful attempt within this scope. In this paper, we will review the biological mechanism, the existence evidences, and the network properties of CPG. From the engineering perspective, we will introduce the engineering simulation of CPG, the property analysis, and the research progress of CPG inspired control method in locomotion control of legged robots. Then, in our research, we will further discuss on existing problems, hot issues, and future research directions in this field.

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Survey of locomotion control of legged robots inspired by biological concept

CPG Based Motion Control for an Underwater Thruster with Undulating Long-Fin

Biped robot locomotion is one of the most challenging fields in humanoid robots. Many gait generation models are introduced to have stable walking similar to human. One of the gait generation models is Central Pattern Generator (CPG) which can produce complex nonlinear oscillation as a pattern for walking. In this paper joints trajectories are calculated by using polynomial equations for the support leg's joints and Truncated Fourier Series (TFS) equation for the swing leg's joints in sagittal plane and also to keep robot stability TFS is used for both leg in frontal plan. PSO algorithm and Genetic Algorithm (GA) are used and compared as evolutionary algorithms to find the best optimization algorithm for TFS and polynomial equation parameters to achieve the best speed and performance in walking.

Evaluating GA and PSO evolutionary algorithms for humanoid walk pattern planning

Biped robot locomotion is one of the active research areas in robotics. In this area, real-time stable walking with proper speed is one of the main challenges that needs to be overcome. Central Pattern Generators (CPG) as one of the biological gait generation models, can produce complex nonlinear oscillation as a pattern for walking. In this paper, we propose a model for a biped robot joint trajectory in order to be able to walk straight, exploiting polynomial equations for the support leg’s joints and Truncated Fourier (TFS) Series equations for the swing leg’s joints in the sagittal plane and frontal plane. Four customized genetic algorithms (GA-1 to GA-4) with different implementations for the crossover steps are used as evolutionary algorithms to optimize equation parameters and achieve the best speed and performance in walking motion. These four GAs differ in crossover step and parent selection parts. After a primary evaluation to make sure the next generation is better off than before, we consider a clever comparison feature between the best of two generations (parent and child) in GA-4. The algorithms have been tested on the Darwin humanoid robot in the Webots simulator environment where the results show that the GA-4 model has the best performance and achieves the desired fitness value.

http://www.qjcr.ir/article_539093_4ea6167b02f1aa9a173751256c50f4e4.pdf

Optimized Joint Trajectory Model with Customized Genetic Algorithm for Biped Robot Walk

Neural circuitry within the spinal cord of the lamprey forms a central pattern generator (CPG) for locomotor control, which interacts with particular neurons in a reticulo-spino-reticular (RSR) loop. Some of the CPG neurons have pacemaker properties. We have used mathematical models to examine the role of N-methyl-D-aspartate (NMDA) and calcium dependent potassium (K/sub Ca/ channel currents in generating oscillatory outputs in: (1) a single neuron; (2) a hybrid neural network for the CPG with embedded pacemaker neurons; and (3) a reticular CPG network. Neurons were modeled as isopotential single compartment units with simplified biophysical properties. All three conditions showed a minimum and maximum NMDA and K/sub Ca/, conductance values for which oscillations occurred. The pacemaker neurons played an important role in the control of the locomotor rhythm by affecting the burst generation and termination mechanisms. The range over which oscillations can be obtained by altering the pacemaker properties of the neurons is affected by the architecture of the network. The pacemaker properties can provide a mechanism for frequency control by the brain with or without affecting the drive to the motor output units.

http://ieeexplore.ieee.org/iel3/4462/12647/00583308.pdf

B.G. Brewer

Papers

Sensitivity analysis of a hybrid neural network for locomotor control in the lamprey