Current sensing feedback for humanoid stability

For humanoid robots to function in changing environments, they must be able to maintain balance similar to human beings. At present, humanoids recover from pushes by the use of either the ankles or hips and a rigid body. This method has been proven to work, but causes excessive strain on the joints of the robot and does not maximize on the capabilities of a humanlike body. The focus of this paper is to enable advanced dynamic balancing through torque classification and balance improving positional changes. For the robot to be able to balance dynamically, external torques must be determined accurately. The proposed method of this paper uses current sensing feedback at the humanoids power source to classify external torques. Through understanding the current draw of each joint, an external torque can be modeled. After being modeled, the external torque can be nullified with balancing techniques. Current sensing has the advantage that it adds detailed feedback while requiring small adjustments to the robot. Also, current sensing minimizes additional sensors, cost, and weight to the robot. Current sensing technology lies between the power supply and drive motors, thus can be implement without altering the robot. After an external torque has been modeled, the robot will undertake balancing positions to reduce the instability. The specialized positions increase the robot\u27s balance while reducing the workload of each joint. The balancing positions incorporate the humanlike body of the robot and torque from each of the leg servos. The best balancing positions were generated with a genetic algorithm and simulated in Webots. The simulation environment provided an accurate physical model and physics engine. The genetic algorithm reduced the workload of searching the workspace of a robot with ten degrees of freedom below the waist. The current sensing theory was experimentally tested on the TigerBot, a humanoid produced by the Rochester Institute of Technology (RIT). The TigerBot has twenty three degrees of freedom that fully simulate human motion. The robot stands at thirty-one inches tall and weighs close to nine pounds. The legs of the robot have six degrees of freedom per leg, which fully mimics the human leg. The robot was awarded first place in the 2012 IEEE design competition for innovation in New York

Towards Body Schema Learning using Training Data Acquired by Continuous Self-touch

Li Q, Haschke R, Ritter H. Towards Body Schema Learning using Training Data Acquired by Continuous Self-touch. Presented at the Humanoids2015, Seoul,Korea.Striving for an autonomous self-exploration of robots to learn their own body schema, i.e. body shape and appearance, kinematic and dynamic parameters, association of tactile stimuli to specific body locations, etc., we developed a tactile-servoing feedback controller that allows a robot to continuously acquire self-touch information while sliding a fingertip across its own body. In this manner one can quickly acquire a large amount of training data representing the body shape. We compare three approaches to track the common contact point observed when one robot arm is touching the other in a bimanual setup: feedforward control, solely relying on a coarse CAD-based kinematics performs worst, a solely feedback-based controller typically lacks behind, and only the combination of both approaches yields satisfactory tracking results. As a first, preliminary application, we use this self-touch capability to calibrate the closed kinematic chain formed by both arms touching each other. The obtained homogeneous transform describing the relative mounting pose of both arms improves end-effector position estimations by a magnitude