Humans have an extremely redundant system for locomotion. To handle the redundancy problem, humans use coordinative structures using conditions of constraint in their joint movements to reduce the number of degrees of freedom, which is called kinematic synergy. This chapter shows some characteristics in the kinematic synergy in human locomotion and shows a locomotion control system for a biped robot, which is inspired by the physiological concept of Central Pattern Generator (CPG) and phase resetting to produce gaits (quadrupedal and bipedal locomotion) and change them based on the kinematic synergy to tackle the redundancy problem in the motion planning of the robot.
Top1. Introduction
Robotics and mechatronics have evolved through interdisciplinary studies (Maki, 2007). An important object in robotics and mechatronics is to produce adaptive locomotor behaviors of legged robots, as observed in humans and animals. For that purpose, robot mechanical and control systems have been developed based on biologically inspired approaches by integrating the knowledge from biomechanics and neurophysiology. This chapter shows the knowledge about locomotion control mechanisms in humans and a design of locomotion control system for a biped robot from the knowledge.
Humans produce adaptive locomotion in diverse environments by skillfully manipulating their complicated musculoskeletal systems. To create such locomotion, motor commands from the nervous system produce muscle activity, and muscle tensions around joints generate joint movements. Locomotion involves moving the center of mass (COM) of the body using the legs. Humans have more degrees of freedom (DOFs) in their joints than necessary for producing such a movement and, in addition, have more DOFs in their muscles than in their joints. Furthermore, various parts of the nervous system, such as the cerebral cortex, the basal ganglia, the cerebellum, the brainstem, and the spinal cord, contribute to generating motor commands for locomotion by integrating such sensory information as visual, vestibular, and somatosensory information. That is, humans use many DOFs and much information to achieve locomotion.
As mentioned above, humans have an extremely redundant system for locomotion. It is obvious that such redundancy plays an important role in achieving adaptive locomotion. However, humans have to solve the redundancy problem in some way. To overcome the redundancy problem, it has been suggested that individual DOFs are not manipulated independently, but some DOFs are functionally connected by object tasks. In other words, humans use conditions of constraint to decrease the number of DOFs to solve the redundancy problem. This functional coordinative structure is associated with simultaneous movements in several joints and co-variations of muscle activities.
These relationships among DOFs appear as low-dimensional structures by analyzing measured data during human locomotion (Bianchi et al., 1998; Funato et al., 2010; Ivanenko et al., 2004, 2005, 2006, 2007, 2008; Lacquaniti et al., 1999; Poppele & Bosco, 2003). In particular, low-dimensional structures in joint movements and muscle activities are called kinematic and muscle synergies, respectively. In addition to locomotion, low-dimensional structures are observed in various tasks from simple to complex (e.g., a single arm reaching task and whole body standing quietly and sit-to-stand tasks) and also observed in many animals (Alexandrov et al., 1998, 2001; Danna-dos-Santos et al., 2007; d'Avella et al., 2003; d'Avella & Bizzi, 2005; Dominici et al., 2011; Drew et al., 2008; Freitas et al., 2006; Latash, 2008; Ting & Macpherson, 2005; Todorov & Jordan, 2002). These kinematic and muscle synergies are viewed as one solution to handle the redundancy problem in biological systems.
This chapter focuses on the kinematic synergy and shows some of its characteristics observed in human locomotion. Furthermore, it shows a locomotion control system for biped robots to produce gaits (quadurupedal and bipedal locomotion) and change them inspired by the kinematic synergy (Aoi et al., 2012).