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This paper is concerned with multi-user haptic simulation environments in which users can interact across an Ethernet-based Local Area Network (LAN) or a Metropolitan Area Network (MAN). Using network protocols such as the UDP and TCP/IP under normal network traffic conditions, the achievable real-time packet communication rate can be well below the 1 kHz update rate suggested in the literature for high fidelity haptic rendering. However by adopting a multi-rate control strategy, the local control loops can be executed at a much higher rate than that of the data packet transmission between the user workstations. Within such a framework, two control architectures, namely centralized and distributed are presented. Mathematical models of the controllers are developed and used in a comparative analysis of their stability and performance. The results of such analysis demonstrate that the distributed control architecture has greater stability margins and outperforms the centralized controller. It is also shown that the limited network packet transmission rate can degrade the haptic fidelity by introducing a viscous damping into the perceived impedance of the virtual object. Using the proposed models, this damping value is calculated and compensated by active control. Experiments conducted with a dual-user/dual-finger haptic platform confirm the analytical results.

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This paper addresses recursive markerless estimation of a robot's end-effector using visual observations from its cameras. The problem is formulated into the Bayesian framework and addressed using Sequential Monte Carlo (SMC) filtering. We use a 3D rendering engine and Computer Aided Design (CAD) schematics of the robot to virtually create images from the robot's camera viewpoints. These images are then used to extract information and estimate the pose of the end-effector. To this aim, we developed a particle filter for estimating the position and orientation of the robot's end-effector using the Histogram of Oriented Gradient (HOG) descriptors to capture robust characteristic features of shapes in both cameras and rendered images. We implemented the algorithm on the iCub humanoid robot and employed it in a closed-loop reaching scenario. We demonstrate that the tracking is robust to clutter, allows compensating for errors in the robot kinematics and servoing the arm in closed loop using vision.

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