Identification of Fully Physical Consistent Inertial Parameters using Optimization on Manifolds

This paper presents a new condition, the fully physical consistency for a set of inertial parameters to determine if they can be generated by a physical rigid body. The proposed condition ensure both the positive definiteness and the triangular inequality of 3D inertia matrices as opposed to existing techniques in which the triangular inequality constraint is ignored. This paper presents also a new parametrization that naturally ensures that the inertial parameters are fully physical consistency. The proposed parametrization is exploited to reformulate the inertial identification problem as a manifold optimization problem, that ensures that the identified parameters can always be generated by a physical body. The proposed optimization problem has been validated with a set of experiments on the iCub humanoid robot.Comment: 6 pages, published in Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference o

In situ calibration of six-axis force-torque sensors using accelerometer measurements

Abstract—This paper proposes techniques to calibrate six-axis force-torque sensors that can be performed in situ, i.e., without removing the sensor from the hosting system. We assume that the force-torque sensor is attached to a rigid body equipped with an accelerometer. Then, the proposed calibration technique uses the measurements of the accelerometer, but requires neither the knowledge of the inertial parameters nor the orientation of the rigid body. The proposed method exploits the geometry induced by the model between the raw measurements of the sensor and the corresponding force-torque. The validation of the approach is performed by calibrating two six-axis force-torque sensors of the iCub humanoid robot. I

Improving Dynamics Estimations and Low Level Torque Control Through Inertial Sensing

In 1996, professors J. Edward Colgate and Michael Peshkin invented the cobots as robotic equipment safe enough for interacting with human workers. Twenty years later, collaborative robots are highly demanded in the packaging industry, and have already been massively adopted by companies facing issues for meeting customer demands. Meantime, cobots are still making they way into environments where value-added tasks require more complex interactions between robots and human operators. For other applications like a rescue mission in a disaster scenario, robots have to deal with highly dynamic environments and uneven terrains. All these applications require robust, fine and fast control of the interaction forces, specially in the case of locomotion on uneven terrains in an environment where unexpected events can occur. Such interaction forces can only be modulated through the control of joint internal torques in the case of under-actuated systems which is typically the case of mobile robots. For that purpose, an efficient low level joint torque control is one of the critical requirements, and motivated the research presented here. This thesis addresses a thorough model analysis of a typical low level joint actuation sub-system, powered by a Brushless DC motor and suitable for torque control. It then proposes procedure improvements in the identification of model parameters, particularly challenging in the case of coupled joints, in view of improving their control. Along with these procedures, it proposes novel methods for the calibration of inertial sensors, as well as the use of such sensors in the estimation of joint torques