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We propose a method for collision-free motion coordination of a group of unicycle agents. Under constraints on control signals, this method guarantees asymptotic tracking of the reference trajectories of all individual agents. The motion coordination is established by mutual coupling of coordinates of the interacting agents. For stronger couplings, the robustness of motion coordination to perturbations is increased. A collision avoidance algorithm is formulated to gain additional robustness against perturbations. The proposed control method is successfully validated in experiments.Index Terms-Coordinated control, control of non-holonomic systems, stability of nonlinear systems, collision avoidance.

This paper describes the design of an optical see-through head-mounted display (HMD) system for Augmented Reality (AR). Our goals were to make virtual objects "perfectly" indistinguishable from real objects, wherever the user roams, and to find out to which extent imperfections are hindering applications in art and design. For AR, fast and accurate measuring of head motions is crucial. We made a head-pose tracker for the HMD that uses error-state Kalman filters to fuse data from an inertia tracker with data from a camera that tracks visual markers. This makes on-line head-pose based rendering of dynamic virtual content possible. We measured our system, and found that with an A4-sized marker viewed from > 20 • at 5 m distance with an SXGA camera (FOV 108 • ), the RMS error in the tracker angle was < 0.5 • when moving the head slowly. Our Kalman filters suppressed the pose error due to camera delay, which is proportional to the angular and linear velocities, and the dynamic misalignment was comparable to the static misalignment. Applications of artists and designers lead to observations on the profitable use of our AR system. Their exhibitions at world-class museums showed that AR is a powerful tool for disclosing cultural heritage.

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This paper describes a two-tiered approach to the self-localization problem for soccer playing robots using generic off-the-shelf color cameras. The solution consists of two layers; the top layer is a global search assuming zero knowledge, and the bottom layer is a local search, assuming a relatively good estimation of the position and orientation of the robot. The global search generally yields multiple candidate positions and orientations, which can be tracked, and assigned a confidence level using the local search and/or historic information.