Haptic augmented reality merges real and virtual feedback, allowing a user to touch the real environment augmented with virtual haptic stimuli. A key functionality of this technology is altering the haptic properties of real objects by means of virtual haptic feedback. Previously, we have developed a haptic augmentation system in which object stiffness was modulated. In this paper, we extend our framework to cover further haptic properties: friction and weight. Simple but effective algorithms for estimating and altering these properties have been developed. The first approach allows us to change the inherent friction between a tool tip and a surface to a desired one identified in an offline process. The second technique enables a user to perceive an altered weight when lifting an object at two interaction points. The performance of the proposed algorithms has been evaluated on real objects. The errors remain within reasonable bounds and compare well to the relevant perceptual Weber fractions. Limitations of our technique are identified, and possible extensions are proposed.

Robot-assisted rehabilitation can complement conventional rehabilitation after stroke, by increasing the duration and intensity of therapy and providing precise and objective measurements of interaction dynamics and performance. Such information can be used to drive assist-as-needed control strategies or to complement clinical assessments by reconstructing the scores from robot data. This paper presents the ReHapticKnob, a new end-effector-based hand rehabilitation robot with unique sensing and actuation capabilities for therapy of grasping and forearm rotation tasks. A compact design with high stiffness and high-fidelity instrumentation is presented, allowing for precise assessment and dynamic interaction. The device has two degrees of freedom (DOF), allowing independent control of hand opening/closing and forearm rotation. Each degree of freedom is equipped with a brake to allow independent training of either DOF or to assess isometric force through the two six-axis force/torque sensors located beneath the exchangeable finger fixations. The design, safety features and performance evaluation of the device are discussed and preliminary results from a study on grasping performed with healthy subjects are presented.

This paper presents a solution to decentralized Voronoi coverage in non-convex polygonal environments. We show that complications arise when existing approaches to Voronoi coverage are applied for deploying a group of robots in non-convex environments. We present an algorithm that is guaranteed to converge to a local optimum. Our algorithm combines classical Voronoi coverage with the Lloyd algorithm and the local path planning algorithm TangentBug to compute the motion of the robots around obstacles and corners. We present the algorithm and prove convergence and optimality. We also discuss experimental results from an implementation with five robots.

This paper addresses the problem of deploying a group of networked robots on a non-planar surface embedded in 3D space. Two distributed coverage control algorithms are presented that both provide a solution to the problem by discrete coverage of a graph. The first method computes shortest paths and runs the Lloyd algorithm on the graph to obtain a centroidal Voronoi tessellation. The second method uses the Euclidean distance measure and locally exchanges mesh cells between approximated Voronoi regions to reach an optimal robot configuration. Both methods are compared and evaluated in simulations and in experiments with five robots on a curved surface.