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The estimation of the ten inertial parameters of rigid loads, which are attached to manipulators, may benefit several robotics applications, e.g.: force control, object recognition, and pose estimation. These applications require sufficiently accurate, robust, and fast estimation of the inertial parameters. Existing approaches, however, do not allow for robust on-line estimation, since they use standard batch leastsquares techniques, which ignore noise in the data matrix. The proposed approach, however, estimates the inertial parameters on-line and very fast (approx. 1.5s), while explicitly considering noise in the data matrix by a total least-squares approach. Apart from estimation equations and estimation approaches, the design of estimation trajectories is addressed in this paper. The performance of the proposed estimation approach is compared with the recursive ordinary least-squares (RLS) and the recursive instrumental variables (RIV) method. Experimental results clearly recommend the proposed recursive total leastsquares approach (RTLS).

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This paper focusses on sensor fusion in robotic manipulation: six-dimensional (6-D) force/torque signals and 6-D acceleration signals are used to extract forces and torques caused by inertia. As result, only forces and torques established by environmental contact(s) remain. Apart from an improvement of hybrid force/pose control behavior, an additional major benefit is that regular resetting/zeroing of force/torque sensors before free space/contact transitions can be omitted. All essential equations, transformations and calculations that are required for this 6-D fusion approach are derived. To highlight the meaning for practical implementations, numerous experiments with a six-joint Staeubli RX60 industrial manipulator are presented and the achieved results are discussed.