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.
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We report on real-robot odor source localization experiments carried out in an environment with obstacles in the odor plume. The robot was equipped with an ethanol sensor and a wind direction sensor, and the experiments were carried out in a wind tunnel, i.e. in a controlled environment. An enhanced version of the surge-spiral algorithm was used, which was augmented with a dedicate behavior to manage obstacles (avoid them, or follow their contour). We compare the results in terms of distance overhead and success rate, and discuss the impact of obstacles on plume traversal.
Large numbers of collaborating robots are advantageous for solving distributed problems. In order to efficiently solve the task at hand, the robots often need accurate localization. In this work, we address the localization problem by developing a solution that has low computational and sensing requirements, and that is easily deployed on large robot teams composed of cheap robots. We build upon a real-time, particle-filter based localization algorithm that is completely decentralized and scalable, and accommodates realistic robot assumptions including noisy sensors, and asynchronous and lossy communication. In order to further reduce this algorithm’s overall complexity, we propose a low-cost particle clustering method, which is particularly well suited to the collaborative localization problem. Our approach is experimentally validated on a team of ten real robots.
Ultra-wideband (UWB) localization is a recent technology that promises to outperform many indoor localization methods currently available. Yet, non-line-of-sight (NLOS) positioning scenarios can create large biases in the time-difference-of-arrival (TDOA) measurements, and must be addressed with accurate measurement models in order to avoid significant localization errors. In this work, we first develop an efficient, closed-form TDOA error model and analyze its estimation characteristics by calculating the Cramer-Rao lower bound (CRLB). We subsequently detail how an online Expectation Maximization (EM) algorithm is adopted to find an elegant formalism for the maximum likelihood estimate of the model parameters. We perform real experiments on a mobile robot equipped with an UWB emitter, and show that the online estimation algorithm leads to excellent localization performance due to its ability to adapt to the varying NLOS path conditions over time.
This paper presents an affordable, fully automated and accurate mapping solutions based on ultra-light UAV imagery. Several datasets are analysed and their accuracy is estimated. We show that the accuracy highly depends on the ground resolution (flying height) of the input imagery. When chosen appropriately this mapping solution can compete with traditional mapping solutions that capture fewer high-resolution images from airplanes and that rely on highly accurate orientation and positioning sensors on board. Due to the careful integration with recent computer vision techniques, the post processing is robust and fully automatic and can deal with inaccurate position and orientation information which are typically problematic with traditional techniques.
We derive the theoretical performance of three bio-inspired odor source localization algorithms (casting, surge-spiral 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 introduce a novel bio-inspired odor source localization algorithm (surge- cast) for environments with a main wind ﬂow and compare it to two well-known 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.