When robots operate in unknown environments small errors in postions can lead to large variations in the contact forces, especially with typical high-impedance designs. This can potentially damage the surroundings and/or the robot. Series elastic actuators (SEAs) are a popular way to reduce the output impedance of a robotic arm to improve control authority over the force exerted on the environment. However this increased control over forces with lower impedance comes at the cost of lower positioning precision and bandwidth. This article examines the use of an iteratively-learned feedforward command to improve position tracking when using SEAs. Over each iteration, the output responses of the system to the quantized inputs are used to estimate a linearized local system models. These estimated models are obtained using a complex-valued Gaussian Process Regression (cGPR) technique and then, used to generate a new feedforward input command based on the previous iteration's error. This article illustrates this iterative machine learning (IML) technique for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful convergence of the IML approach to reduce the tracking error.

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Assembly fixtures that are much smaller than the structure being manufactured, such as an aircraft, must routinely be positioned and docked against the structure on which they act.Here, docking implies physical contact between the fixture and the structure. The major contribution of this thesis is to develop a variable-impedance-based human-machine control for the docking process. The major issues investigated are: (i) impedance control to facilitate intuitive human force input; (ii) utilizing variable damping to aid docking and safeguard both the fixture and structure; and (iii) apply a dock force while preserving human control for undocking. A single-degree-of-freedom experimental test bed is used to simulate docking using the proposed controller, and to explore how controller parameter choices impact overall performance.