Adaptive, fast walking in a biped robot under neuronal control and learning

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks

Adaptive, fast walking in a biped robot under neuronal control and learning

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (>3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks

Lower body design of the ‘iCub’ a human-baby like crawling robot

The development of robotic cognition and a greater understanding of human cognition form two of the current greatest challenges of science. Within the RobotCub project the goal is the development of an embodied robotic child (iCub) with the physical and ultimately cognitive abilities of a 2frac12 year old human baby. The ultimate goal of this project is to provide the cognition research community with an open human like platform for understanding of cognitive systems through the study of cognitive development. In this paper the design of the mechanisms adopted for lower body and particularly for the leg and the waist are outlined. This is accompanied by discussion on the actuator group realisation in order to meet the torque requirements while achieving the dimensional and weight specifications. Estimated performance measures of the iCub are presented

Obstacle Avoidance Design for Humanoid Robot Based on Four Infrared Sensors

[[abstract]]A behavior strategy of humanoid robot for obstacle avoidance based on four infrared sensors is proposed and implemented on an autonomous humanoid robot. A mechanical structure with 26 degrees of freedom is design so that an implemented small-size humanoid robot named TWNHR-Ⅲ is able to accomplish five walking motions. Three walking experiments are presented to illustrate that the proposed biped structure lets TWNHR-Ⅲ can move forward, turn, and slip. One electronic compass and four infrared sensors are mounted on TWNHR-Ⅲ to obtain the head direction of the robot and detect obstacles, respectively. Based on the obtained information from these sensors, a decision tree method is proposed to decide one behavior from five movements: walk forward, turn right and left, and slip right and left. Two MATLAB simulations and one real experiment are presented to illustrate that the robot can avoid obstacles autonomously and go to the destination effectively.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Simulation and Framework for the Humanoid Robot TigerBot

Walking humanoid robotics is a developing field. Different humanoid robots allow for different kinds of testing. TigerBot is a new full-scale humanoid robot with seven degrees-of-freedom legs and with its specifications, it can serve as a platform for humanoid robotics research. Currently TigerBot has encoders set up on each joint, allowing for position control, and its sensors and joints connect to Teensy microcontrollers and the ODroid XU4 single-board computer central control unit. The components’ communication system used the Robot Operating System (ROS). This allows the user to control TigerBot with ROS. It’s important to have a simulation setup so a user can test TigerBot’s capabilities on a model before using the real robot. A working walking gait in the simulation serves as a test of the simulator, proves TigerBot’s capability to walk, and opens further development on other walking gaits. A model of TigerBot was set up using the simulator Gazebo, which allowed testing different walking gaits with TigerBot. The gaits were generated by following the linear inverse pendulum model and the basic zero-moment point (ZMP) concept. The gaits consisted of center of mass trajectories converted to joint angles through inverse kinematics. In simulation while the robot follows the predetermined joint angles, a proportional-integral controller keeps the model upright by modifying the flex joint angle of the ankles. The real robot can also run the gaits while suspended in the air. The model has shown the walking gait based off the ZMP concept to be stable, if slow, and the actual robot has been shown to air walk following the gait. The simulation and the framework on the robot can be used to continue work with this walking gait or they can be expanded on for different methods and applications such as navigation, computer vision, and walking on uneven terrain with disturbances

Natural ZMP trajectories for biped robot reference generation

The control of a biped humanoid is a challenging task due to the hard-to-stabilize dynamics. Walking reference trajectory generation is a key problem. Linear Inverted Pendulum Model (LIPM) and Zero Moment Point (ZMP) Criterion based approaches in stable walking reference generation are reported. In these methods, generally, the ZMP reference during a stepping motion is kept fixed in the middle of the supporting foot sole. This kind of reference generation lacks naturalness, in that, the ZMP in the human walk does not stay fixed, but it moves forward under the supporting foot. This paper proposes a reference generation algorithm based on the LIPM and moving support foot ZMP references. The application of Fourier series approximation simplifies the solution and it generates a smooth ZMP reference. A simple inverse kinematics based joint space controller is used for the tests of the developed reference trajectory through full-dynamics 3D simulation. A 12 DOF biped robot model is used in the simulations. Simulation studies suggest that the moving ZMP references are more energy efficient than the ones with fixed ZMP under the supporting foot. The results are promising for implementations