Robots are finding new applications where physical interaction with a human is necessary, such as manufacturing, healthcare, and social tasks. Accordingly, the field of physical human–robot interaction (pHRI) has leveraged impedance control approaches, which support compliant interactions between human and robot. However, a limitation of traditional impedance control is that—despite provisions for the human to modify the robot's current trajectory—the human cannot affect the robot's future desired trajectory through pHRI. In this paper, we present an algorithm for physically interactive trajectory deformations which, when combined with impedance control, allows the human to modulate both the actual and desired trajectories of the robot. Unlike related works, our method explicitly deforms the future desired trajectory based on forces applied during pHRI, but does not require constant human guidance. We present our approach and verify that this method is compatible with traditional impedance control. Next, we use constrained optimization to derive the deformation shape. Finally, we describe an algorithm for real-time implementation, and perform simulations to test the arbitration parameters. Experimental results demonstrate reduction in the human's effort and improvement in the movement quality when compared to pHRI with impedance control alone.
This paper presents a novel approach for human-robot cooperation in tasks with dynamic uncertainties. The essential element of the proposed method is a multi-modal interface that provides the robot with the feedback about the human motor behaviour in real-time. The human muscle activity measurements and the arm force manipulability properties encode the information about the motion and impedance, and the intended configuration of the task frame, respectively. Through this human-in-the-loop framework, the developed hybrid controller of the robot can adapt its actions to provide the desired motion and impedance regulation in different phases of the cooperative task. We experimentally evaluate the proposed approach in a two-person sawing task that requires an appropriate complementary behaviour from the two agents. This work extends the framework of bilateral shared control of mobile robots with the aim of increasing the robot autonomy and decreasing the operator commitment. We consider persistent autonomous behaviors where a cyclic motion must be executed by the robot. The human operator is in charge of modifying online some geometric properties of the desired path. This is then autonomously processed by the robot in order to produce an actual path guaranteeing: i) tracking feasibility, ii) collision avoidance with obstacles, iii) closeness to the desired path set by the human operator, and iv) proximity to some points of interest. A force feedback is implemented to inform the human operator of the global deformation of the path rather than using the classical mismatch between desired and executed motion commands. Physically-based simulations, with human hardware-in-the-loop and a quadrotor UAV as robotic platform, demonstrate the feasibility of the method. 