Yasuhiro Fukuoka

Adaptive Dynamic Walking of a Quadruped Robot on Irregular Terrain Based on Biological Concepts

We have been trying to induce a quadruped robot to walk with medium walking speed on irregular terrain based on biological concepts. We propose the necessary conditions for stable dynamic walking on irregular terrain in general, and we design the mechanical system and the neural system by comparing biological concepts with those necessary conditions described in physical terms. A PD controller at the joints can construct the virtual spring-damper system as the visco-elasticity model of a muscle. The neural system model consists of a central pattern generator (CPG) and reflexes. A CPG receives sensory input and changes the period of its own active phase. The desired angle and P-gain of each joint in the virtual spring-damper system is switched based on the phase signal of the CPG. CPGs, the motion of the virtual spring-damper system of each leg and the rolling motion of the body are mutually entrained through the rolling motion feedback to CPGs, and can generate adaptive walking. We report on our experimental results of dynamic walking on terrains of medium degrees of irregularity in order to verify the effectiveness of the designed neuro-mechanical system. We point out the trade-off problem between the stability and the energy consumption in determining the cyclic period of walking on irregular terrain, and we show one example to solve this problem. MPEG footage of these experiments can be seen at http://www.kimura.is.uec.ac.jp.

Adaptive Dynamic Walking of a Quadruped Robot on Natural Ground Based on Biological Concepts

The paper reports on a project to make a quadruped robot walk with medium forward speed on irregular terrain in an outdoor environment using a neural system model. The necessary conditions for stable dynamic walking on irregular terrain in general are proposed, and the neural system is designed by comparing biological concepts with those necessary conditions described in physical terms. A PD-controller is used at joints to construct a virtual spring—damper system as the visco-elasticity model of a muscle. The neural system model consists of a CPG (central pattern generator), responses and reflexes. A response directly and quickly modulates the CPG phase, and a reflex directly generates joint torque. The state of the virtual spring—damper system is switched, based on the CPG phase. In order to make a self-contained quadruped (called Tekken2) walk on natural ground, several new reflexes and responses are developed in addition to those developed in previous studies. A flexor reflex prevents a leg from stumbling on small bumps and pebbles. A sideways stepping reflex stabilizes rolling motion on a sideways inclined slope. A corrective stepping reflex/response prevents the robot from falling down in the case of loss of ground contact. A crossed flexor reflex helps a swinging leg keep enough clearance between the toe and the ground. The effectiveness of the proposed neural system model control and especially the newly developed reflexes and responses are validated by indoor and outdoor experiments using Tekken2. A CPG receives sensory feedback as a result of motions induced by reflexes, and changes the period of its own active phase. Since a CPG has the ability of mutual entrainment with pitching motion of legs and rolling motion of the body in addition, the consistency between motion of a leg temporally modified by a reflex and motions of the other legs is maintained autonomously. It is shown that CPGs can be the center of sensorimotor coordination, and that the neural system model simply defining the relationships between CPGs, sensory input, reflexes and mechanical system works very well even in complicated tasks such as adaptive dynamic walking on unstructured natural ground.

Adaptive dynamic walking of a quadruped robot using a neural system model

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In this paper, we integrate several reflexes, such as a stretch reflex, a vestibulospinal reflex and extensor/flexor reflexes, into a central pattern generator (CPG). We try to realize adaptive walking up and down a slope of 12°, walking over an obstacle 3 cm in height, and walking on terrain undulation consisting of bumps 3 cm in height with fixed parameters of CPGs and reflexes. The success in walking on such irregular terrain in spite of stumbling and landing on obstacles shows that the control method using a neural system model proposed in this study has the ability for autonomous adaptation to unknown irregular terrain. In order to clarify the role of a CPG, we investigate the relation between parameters of a CPG and the mechanical system by simulations and experiments. CPGs can generate stable walking suitable for the mechanical system by receiving inhibitory input as sensory feedback and generate adaptive walking on irregular terrain by receiving excitatory input as sensory feedback. MPEG footage of these experiments can be seen at: http://www.kimura.is.uec.ac.jp.

Biologically inspired adaptive walking of a quadruped robot.

We describe here the efforts to induce a quadruped robot to walk with medium-walking speed on irregular terrain based on biological concepts. We propose the necessary conditions for stable dynamic walking on irregular terrain in general, and we design the mechanical and the neural systems by comparing biological concepts with those necessary conditions described in physical terms. PD-controller at joints constructs the virtual spring–damper system as the viscoelasticity model of a muscle. The neural system model consists of a central pattern generator (CPG), reflexes and responses. We validate the effectiveness of the proposed neural system model control using the quadruped robots called ‘Tekken1&2’. MPEG footage of experiments can be seen at http://www.kimura.is.uec.ac.jp.

Adaptive dynamic walking of a quadruped robot 'Tekken' on irregular terrain using a neural system model

We have induced a quadruped robot to walk with medium walking speed on irregular terrain based on biological concepts. The PD-controller at joints constructs the virtual spring-damper system as the visco-elasticity model of a muscle. The neural system model consists of a CPG (central pattern generator) and reflexes. A CPG receives sensory input and changes the period of its own active phase. The desired angle and P-gain of each joint in the virtual spring-damper system is switched based on the phase signal of the CPG. CPGs, the motion of the virtual spring-damper system of each leg and the rolling motion of the body are mutually entrained through the rolling motion feedback to CPGs, and can generate adaptive walking. We report our experimental results of dynamic walking on terrains of medium degrees of irregularity in order to verify the effectiveness of the designed neuro-mechanical system.

Three-dimensional adaptive dynamic walking of a quadruped - rolling motion feedback to CPGs controlling pitching motion

We attempt to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model consisting of a central pattern generator (CPG) and reflexes. In this paper, we report on a newly developed quadruped robot, which contains a mechanism designed to make adaptive 3D space walking (pitch, roll and yaw planes) on irregular terrain be performed more simply. In 3D walking, a rolling motion is naturally generated in most gaits. By having the rolling motion be the standard oscillation and making the CPGs of the legs be entrained with a rolling motion, we realized the stabilization of the gait at a constant walking speed, in spite of an unbalanced gravity load between the fore and hind legs. We also realized an autonomous gait transition in changing walking speeds, by utilizing such rolling motion feedback to CPGs, in order to reduce energy consumption. Rolling motion feedback to CPGs contributes to gait stabilization in walking on an irregular terrain as a tonic labyrinthine reflex for rolling. At present stage, the robot succeeded in walking over several 2D irregular terrains by using CPGs, a flexor reflex and tonic labyrinthine reflexes for pitching and rolling.

Towards 3D adaptive dynamic walking of a quadruped robot on irregular terrain by using neural system model

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In our previous study, we integrated several reflexes into a CPG (central pattern generator) and realized adaptive 2D walking on terrain of medium degree of irregularity. In this paper, in order to make the role of a CPG be clear, we investigate the relation between parameters of a CPG and the mechanical system by simulations and experiments. In addition, we show a newly developed quadruped robot, of which mechanism is designed to make adaptive 3D walking on irregular terrain be realized more simply. At this moment, 3D walking on flat terrain is realized by using a neural system model consisting of CPG and reflexes.

Adaptive dynamic walking of the quadruped on irregular terrain-autonomous adaptation using neural system model

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In this paper, we integrate several reflexes such as stretch reflex, vestibulospinal reflex, and extensor and flexor reflex into CPG (central pattern generator). The success in walking on terrain of medium degree of irregularity with fixed parameters of CPG and reflexes shows that the biologically inspired control proposed in this study has an ability for autonomous adaptation to unknown irregular terrain.

Autonomously generating efficient running of a quadruped robot using delayed feedback control

We report on the design and stability analysis of a simple quadruped running controller that can autonomously generate steady running of a quadruped with good energy efficiency and suppress such disturbances as irregularities of terrain. In this paper, we first consider the fixed point of quasi-passive running based on a sagittal plane model of a quadruped robot. Next, we regard friction and collision as disturbances around the fixed point of quasi-passive running, and propose an original control method to suppress these disturbances. Since it is difficult to accurately measure the total energy of the system in a practical application, we use a delayed feedback control (DFC) method based on the stance phase period measured by contact sensors on the robots feet with practical accuracy. The DFC method not only stabilizes running around a fixed point, but also results in the transition from standing to steady running and stabilization in running up a small step. The effectiveness of the proposed control method is validated by simulations. MPEG footage of these simulations can be viewed at: http://www.kimura.is.uec.ac.jp/running.

Adaptive dynamic walking of a quadruped robot on irregular terrain by using neural system model

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. We integrate several reflexes, such as a stretch reflex, a vestibulospinal reflex, and extensor/flexor reflexes, into a CPG (central pattern generator). The success in walking on terrain of medium degree of irregularity with fixed parameters of CPGs and reflexes shows that the biologically inspired control proposed in this study has an ability for autonomous adaptation to unknown irregular terrain.