Classical sensor-based control laws are based on the regulation of a set of features to a desired reference value. In this paper, we focus on the study of control laws whose feature set varies during the servo. In that case, we first show that the classical control laws that use an iterative least-square minimization are discontinuous. We then show that these discontinuities are due to the pseudo-inverse operator, which is not continuous at matrix rank change. To solve this problem, we propose a new inversion operator. This operator is equal to the classical pseudo-inverse operator in the continuous cases, and ensures the continuity everywhere. This operator is then used to build a new control law. This general control scheme is applied to visual servoing, in order to ensure the continuity of the control law when some visual features leave the camera field of view. The experiments prove the interest and the validity of our approach.

This article presents the current state of an ongoing work on Human-Robot interaction in which two partners collaborate during an object hand-over interaction. The manipulator control is based on the Dynamic Movement Primitives model, specialized for the object hand-over context. The proposed modifications enable finer control of the dynamic of the DMP to align it to human control strategies, where the contributions of the feedforward and feedback parts of the control are different to the original DMP formulation. Furthermore, the proposed scheme handles moving goals. With these two modifications, the model no longer requires an explicit estimation of the exchange position and it can generate motion purely reactively to the instantaneous position of the human hand. The quality of the control system is evaluated through an extensive comparison with ground truth data related to the object interaction between two humans acquired in the context of the European project CogLaboration which envisages an application in an industrial setting.

This article presents the implementation and experimental validation of a control system dedicated to human robot physical interaction during object handovers. Our control law defines the trajectory of the robotic arm towards a previously unknown handover location based on the Dynamic Movement Primitives formalism adapted for human robot object handover. The control law was deployed on a Kuka Light-Weight Arm equipped with the Azzurra anthropomorphic hand. We employed an industrial-like setting involving three different human postures for object handover in order to evaluate the performance and user experience of our control law and its generalizability. The evaluation was conducted over 1000 object handover trials between the human and robot partners, and the kinematic data and subjective experience were gathered for each trial. The outcomes of this evaluation validate the current implementation and guide the next steps towards more efficient and fluid human robot interaction.

In this paper, the performance of a topological-metric visual-path-following framework is investigated in different environments. The framework relies on a monocular camera as the only sensing modality. The path is represented as a series of reference images such that each neighboring pair contains a number of common landmarks. Local 3-D geometries are reconstructed between the neighboring reference images to achieve fast feature prediction. This condition allows recovery from tracking failures. During navigation, the robot is controlled using image-based visual servoing. The focus of this paper is on the results from a number of experiments that were conducted in different environments, lighting conditions, and seasons. The experiments with a robot car show that the framework is robust to moving objects and moderate illumination changes. It is also shown that the system is capable of online path learning.