This paper presents a scheme to enhance payload manipulation using a robot collaborating with an overhead crane. In the current industrial practice, when the crane's payload has to be accurately manipulated and located in a desired position, the task becomes laborious and risky since the operators have to guide the fine motions of the payload by hand. In the proposed collaborative scheme, the crane lifts the payload while the robot's end-effector guides it toward the desired position. The only link between the robot and the crane is the interaction force produced during the guiding of the payload. Two admittance transfer functions are considered to accomplish harmless and smooth contact with the payload. The first is used in a position-based admittance control integrated with the robot. The second one adds compliance to the crane by processing the interaction force through the admittance transfer function to generate a crane's velocity command that makes the crane follow the payload. Then the robot's end-effector and the crane move collaboratively to guide the payload to the desired location. A method is presented to design the admittance controllers that accomplish a fluent robot-crane collaboration. Simulations and experiments validating the scheme potential are shown.

The interest in Physical Human-Robot Interaction (pHRI) has significantly increased over the last two decades thanks to the availability of collaborative robots that guarantee user safety during force exchanges. For this reason, stability concerns have been addressed extensively in the literature while proposing new control schemes for pHRI applications. Because of the nonlinear nature of robots, stability analyses generally leverage passivity concepts. On the other hand, the proposed algorithms generally consider ideal models of robot manipulators. For this reason, the primary objective of this paper is to conduct a detailed analysis of the sources of instability for a class of pHRI control schemes, namely proxy-based constrained admittance controllers, by considering parasitic effects such as transmission elasticity, motor velocity saturation, and actuation delay. Next, a sensitivity analysis supported by experimental results is carried out, in order to identify how the control parameters affect the stability of the overall system. Finally, an adaptation technique for the proxy parameters is proposed with the goal of maximizing transparency in pHRI. The proposed adaptation method is validated through both simulations and experimental tests.

Recent advancements have enabled human-robot collaboration through physical assistance and verbal guidance. However, limitations persist in coordinating robots' physical motions and speech in response to real-time changes in human behavior during collaborative contact tasks. We first derive principles from analyzing physical therapists' movements and speech during patient exercises. These principles are translated into control objectives to: 1) guide users through trajectories, 2) control motion and speech pace to align completion times with varying user cooperation, and 3) dynamically paraphrase speech along the trajectory. We then propose a Language Controller that synchronizes motion and speech, modulating both based on user cooperation. Experiments with 12 users show the Language Controller successfully aligns motion and speech compared to baselines. This provides a framework for fluent human-robot collaboration.

Physical Human-Robot Interaction (pHRI) task involves tight coupling between safety constraints and compliance with human intentions. In this paper, a novel switched model reference admittance controller is developed to maintain compliance with the external force while upholding safety constraints in the workspace for an n-link manipulator involved in pHRI. A switched reference model is designed for the admittance controller to generate the reference trajectory within the safe workspace. The stability analysis of the switched reference model is carried out by an appropriate selection of the Common Quadratic Lyapunov Function (CQLF) so that asymptotic convergence of the trajectory tracking error is ensured. The efficacy of the proposed controller is validated in simulation on a two-link robot manipulator.