Abstract-Vision-based tracking is used in nearly all robotic laboratories for monitoring and extracting of agent positions, orientations, and trajectories. However, there is currently no accepted standard software solution available, so many research groups resort to developing and using their own custom software. In this paper, we present Version 4 of SwisTrack, an open source project for simultaneous tracking of multiple agents. While its broad range of pre-implemented algorithmic components allows it to be used in a variety of experimental applications, its novelty stands in its highly modular architecture. Advanced users can therefore also implement additional customized modules which extend the functionality of the existing components within the provided interface. This paper introduces SwisTrack and shows experiments with both marked and marker-less agents.

Abstract-In the field of mobile robotics, trajectory details are seldom taken into account to qualify robot performance. Most metrics rely mainly on global results such as the total time needed or distance traveled to accomplish a given navigational task. Indeed, usually mobile roboticists assume that, by using appropriate navigation techniques, they can design controllers so that the error between the actual and the ideal trajectory can be maintained within prescribed bounds. This assumption indirectly implies that there is no interesting information to be extracted by comparing trajectories if their variation is essentially resulting from uncontrolled noisy factors. In this paper, we will instead show that analyzing and comparing resulting trajectories is useful for a number of reasons, including model design, system optimization, system performance, and repeatability. In particular, we will describe a trajectory analysis method based on Point Distribution Models (PDMs). The applicability of this method is demonstrated on the trajectories of a real differentialdrive robot, endowed with two different controllers leading to different patterns of motion. Results demonstrate that in the space of the PDM, the difference between the two controllers is easily quantifiable. This method appears also to be extremely useful for comparing real trajectories with simulated ones for the same set-up since it affords an assessment of the simulation faithfulness before and after appropriate tuning of simulation features.

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In recent years, the advent of robust tracking systems has enabled behavioral analysis of individuals based on their trajectories. An analysis method based on a Point Distribution Model (PDM) is presented here. It is an unsupervised modeling of the trajectories in order to extract behavioral features. The applicability of this method has been demonstrated on trajectories of a realistically simulated mobile robot endowed with various controllers that lead to different patterns of motion. Results show that this analysis method is able to clearly classify controllers in the PDM-transformed space, an operation extremely difficult in the original space. The analysis also provides a link between the behaviors and trajectory differences.

Controlled delivery of intravenous (IV) anesthetics aims at fast and safe achievement and maintenance of a suitable depth of hypnosis (DOH), by ensuring appropriate effect site (i.e. brain) exposure to the drug. Today, such drugs are regularly injected by Target Controlled Infusion (TCI) systems, piloted by an open-loop algorithm based on Pharmacokinetic (PK) models. Yet the inaccuracy of concentration prediction of current TCI can reach up to 100%. The situation could be improved by closing the loop with sensors providing regular real measurements of the anesthetic concentration in body fluids. In this paper we present a closed-loop algorithm based on the classic open-loop algorithm combined with a Kalman filter. The latter estimates plasma drug concentration based on PK model and sensor measurements. The estimates are then used in the open-loop algorithm. To validate our approach measurements are generated by means of modulating the population-based plasma concentration values with the maximum inter-and intrapatient variability of the statistical Eleveld‫׳‬s (Eleveld et al., 2014) PK model. This allows us to stress the system to a maximum level prior to testing it on patients. We also perform robustness analysis of this algorithm by accounting for realistic measurement periods and delays.