This paper addresses the problem of estimating a likelihood map for the location of the source of a chemical plume using an autonomous vehicle as a sensor probe in a fluid flow. The fluid flow is assumed to have a high Reynolds number. Therefore, the dispersion of the chemical is dominated by turbulence, resulting in an intermittent chemical signal. The vehicle is capable of detecting above-threshold chemical concentration and sensing the fluid flow velocity at the vehicle location. This paper reviews instances of biological plume tracing and reviews previous strategies for a vehicle-based plume tracing. The main contribution is a new source-likelihood mapping approach based on Bayesian inference methods. Using this Bayesian methodology, the source-likelihood map is propagated through time and updated in response to both detection and nondetection events. Examples are included that use data from in-water testing to compare the mapping approach derived herein with the map derived using a previously existing technique

This paper presents an olfactory-based navigation algorithm for using a mobile robot to locate an odor source in a turbulent flow environment. We analogize the odor source localization (OSL) as a reinforcement learning (RL) problem. During the odor plume tracing process, the belief state in a partially observable Markov decision process (POMDP) model is adapted to generate a source probability map that estimates possible odor source locations, and a hidden Markov model (HMM) is employed to produce a plume distribution map that premises plume propagation areas. Both source and plume estimations are fed to the robot, and a decision-making approach based on fuzzy inference is designed to dynamically fuse information from two maps and to balance the exploitation and exploration of the search. After assigning the fused information to reward functions, a value iteration based path planning algorithm is presented to solve for the optimal action policy. Comparing to other commonly used olfactory-based navigation algorithms, such as moth-inspired and Bayesian inference methods, simulation results show that the proposed method is more intelligent and efficient.

The design, control, and implementation of an autonomous underwater vehicle (AUV) for collecting hydrological information from plateau rivers and lakes are presented in this paper. The hardware and software structures of the control system were previously described. A novel sliding mode controller (SMC) with combinational reaching law of vertical hovering motion is proposed to improve the robustness and stability. The S-plane control, a nonlinear controller with little parameters, is used in the horizontal motion. Besides, the navigation strategy based on the dead-reckoning algorithm, a path tracking based on the light-of-sight (LOS) algorithm, and a control allocation strategy considering saturation are present. Finally, experiments were performed in a tank and in a river in the Qinghai–Tibet Plateau to prove the feasibility and reliability of the AUV system.

The three-dimensional (3D) path following problem of an underactuated autonomous underwater vehicle with ocean currents disturbances is addressed in this paper. Firstly, the motion equation under the ocean currents disturbance is established, and the dynamic model of 3D tracking error is constructed based on virtual guidance method. Then, a finite-time control scheme based on super-twisting observer and command filtered backstepping technology is proposed. We adopt super-twisting observer based on finite-time theory to observe the ocean currents disturbances for improving the system robust. A command filtered backstepping is proposed to replace the differential process in the conventional backstepping method for avoiding the differential expansion problem. The filter compensation loop is designed to ensure the accuracy of the filtered signal, and the anti-integration saturation link is designed considering the influence of integral saturation. Lyapunov stability theory is used to prove the stability of the underactuated AUV. Simulation studies are conducted to show the effectiveness and robustness of the controller.