A new nonparametric Bayesian-based motion planning algorithm for autonomous plume source term estimation (STE) and source seeking (SS) is presented in this paper. The algorithm is designed for mobile robots equipped with gas concentration sensors. Specifically, robots coordinate and utilize a Gaussian-plume likelihood model in a Bayesian-based STE process, then they simultaneously search for and navigate toward the source through model based, bioinspired SS methods such as biased-random-walk and surge-casting. Compared with the state-of-the-art Bayesian- and sensor-based STE/SS motion planners, the strategy described takes advantage of coordination between multiple robots and the estimated plume model for faster and more robust SS, rather than rely on direct or filtered sensor measurements. A set of Monte Carlo simulation studies are conducted to compare the performance between the uncoordinated and coordinated algorithms for different robot team sizes and starting conditions. Additionally, the algorithms are validated experimentally through a laboratory-safe, realistic humid-air plume that behaves similar to a gas plume, to test STE and SS using mobile ground robots equipped with humidity sensors. Simulation and experimental results show consistently that the algorithm involving coordination outperforms traditional bioinspired SS algorithms and it is approximately twice as fast as the uncoordinated case. Finally, the plume source is distorted to study the algorithm's limitations and impact on STE and SS, where results show that even for distorted plumes, useful source localization information can be obtained.

This paper presents the Treadport Active Wind Tunnel (TPAWT)-a full-body immersive virtual environment for the Treadport locomotion interface designed for generating wind on a user from any frontal direction at speeds up to 20 kph. The goal is to simulate the experience of realistic wind while walking in an outdoor virtual environment. A recirculating-type wind tunnel was created around the pre-existing Treadport installation by adding a large fan, ducting, and enclosure walls. Two sheets of air in a non-intrusive design flow along the side screens of the back-projection CAVE-like visual display, where they impinge and mix at the front screen to redirect towards the user in a full-body cross-section. By varying the flow conditions of the air sheets, the direction and speed of wind at the user are controlled. Design challenges to fit the wind tunnel in the pre-existing facility, and to manage turbulence to achieve stable and steerable flow, were overcome. The controller performance for wind speed and direction is demonstrated experimentally.

This research creates a steady headwind at a user position in the scaled Treadport Active Wind Tunnel (TPAWT). The TPAWT adds a wind display system to the previously developed Treadport virtual environment, and this research builds upon prior work to provide improved control of headwind angle at the user position. Key to this research is the addition of a negative pressure plenum at the rear of the treadport to improve nominal flow stability. The previous controller based upon the small gain theorem with a dynamic extension is then modified to provide wind angle feedback control. A conditional angular rate-switching controller is added to reduce wind angle oscillations at the user. A vorticity-meter is developed to assure that the wind flow is centered at the user position. As a result, this research reduces wind angle error by 75% compared to previous work.

This paper develops a static observer for estimating wind speed in order to control wind speed and angle control at a user position in a submersive virtual environment. Addition of wind display evolves the Treadport Virtual environment into a highly immersive virtual environment called Treadport Active Wind Tunnel (TPAWT). Experiments on a scaled model of the TPAWT show that headwind flow stream diverges at the user. Pitot tube sensors placed at a particular region of converged flow in the scaled TPAWT provide measurements with lower noise. Open loop experiments on a scaled model of TPAWT show that there exists a relationship between speed measured at this region of converged flow and the wind speed at the user position. Using this relationship, the wind speed at the user can be estimated. We use this relation and combine previously used speed and angle controllers based upon the small gain theorem with a dynamic extension and conditional angular rate-switching control. Finally, we simultaneously control wind speed and headwind angle.

This research focuses on development of a haptic system to create controlled air flow acting on a user in the Treadport virtual environment. The Treadport active wind tunnel (TPAWT) is thus created in order to produce air flow patterns that allow a variety of wind angles and speeds to be felt by the user. In order to control this system in real-time, the small gain theorem is used in conjunction with a dynamic extension to formulate an output feedback control law. Examples of controller formulations are derived and discrete time simulations in FLUENT demonstrate their effectiveness. The controller is then validated experimentally using a scale model of the TPAWT.