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 en- gineering 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.
More and more biological evidences have been found that neural networks in the spinal cord, referred to as "central pattern generators" (CPGs), govern locomotion. CPGs are capable of producing rhythmic movements, such as swimming, flying, and walking, even when isolated from the brain and sensory inputs. If we could build up any models that have similar functions as CPGs, it will be much easier to design better locomotion for robots. In this paper, a self-training environment is designed and through genetic algorithm (GA), walking trajectories for every foot of AIBO are generated at first. With this acquired walking pattern, AIBO gets its fastest locomotion speed. Then, this walking pattern is taken as a reference to build CPGs with Hopf oscillators. By changing corresponding parameters, the frequencies and the amplitudes of CPGs' outputs can be adjusted online. The limit cycle behavior of Hopf oscillators ensures the online adjustment and the walking stability against perturbation as well. This property suggests a strong adaptive capacity to real environments for robots. At last, simulations are carried on in Webots and verify the proposed method.
