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.

Summary. We introduce a novel bio-inspired odor source localization algorithm (surge-cast) for environments with a main wind flow and compare it to two wellknown algorithms. With all three algorithms, systematic experiments with real robots are carried out in a wind tunnel under laminar flow conditions. The algorithms are compared in terms of distance overhead when tracking the plume up to the source, but a variety of other experimental results and some theoretical considerations are provided as well. We conclude that the surge-cast algorithm yields significantly better performance than the casting algorithm, and slightly better performance than the surge-spiral algorithm.

Abstract-We compare two well-known algorithms for locating odor sources in environments with a main wind flow. Their plume tracking performance is tested through systematic experiments with real robots in a wind tunnel under laminar flow condition. We present the system setup and show the wind and odor profiles. The results are then compared in terms of time and distance to reach the source, as well as speed in upwind direction. We conclude that the spiral-surge algorithm yields significantly better results than the casting algorithm, and discuss possible rationales behind this performance difference.

Abstract-We derive the theoretical performance of three bio-inspired odor source localization algorithms (casting, surgespiral and surge-cast) in laminar wind flow. Based on the geometry of the trajectories and the wind direction sensor error, we calculate the distribution of the distance overhead and the mean success rate using Bayes inference. Our approach is related to particle filtering and produces smooth output distributions. The results are compared to existing real-robot and simulation results, and a good match is observed.

We compare three bio-inspired odor source localization algorithm (casting, surge-spiral and surge-cast)