Learning to rapidly re-contact the lost plume in chemical plume tracing

Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network

Marine pollution, which can cause damage to marine ecosystems, cut fishery production, and even harm human health, has aroused worldwide interest in recent years. Marine pollution reduction operations can stagnate in the case that the source of the pollution is unknown or hidden. In this paper, we present a novel method for marine pollution source localization using a network of mobile sensor nodes, such as autonomous underwater vehicles equipped with chemical sensors. Traditional reactive control methods can respond quickly to the shape dynamics of a chemical plume; however, they can hardly achieve intelligent cooperation unlike deliberative methods. In this study, we present a behavior composition method that attempts to combine the advantages of reactive and deliberative methods. An upwind-customized crossover operation based on the genetic algorithm was formulated as one of the elementary behaviors. The upwind sprint and movement away from the centroid of the sensor nodes were also modeled as another two elementary behaviors. Different sensor nodes are capable of different simultaneous elementary behaviors, enabling behavior composition in the mobile sensor network during plume source localization. The proposed method was evaluated using a widely used filamentous plume simulation platform, which has been used to facilitate field experiments in real marine environments. Simulation results indicate that the proposed method achieved high time-efficiency and localization accuracy during plume source localization in marine environments.

Robust pollution source parameter identification based on the artificial bee colony algorithm using a wireless sensor network

Pollution source parameter identification (PSPI) is significant for pollution control, since it can provide important information and save a lot of time for subsequent pollution elimination works. For solving the PSPI problem, a large number of pollution sensor nodes can be rapidly deployed to cover a large area and form a wireless sensor network (WSN). Based on the measurements of WSN, least-squares estimation methods can solve the PSPI problem by searching for the solution that minimize the sum of squared measurement noises. They are independent of the measurement noise distribution, i.e., robust to the noise distribution. To search for the least-squares solution, population-based parallel search techniques usually can overcome the premature convergence problem, which can stagnate the single-point search algorithm. In this paper, we adapt the relatively newly presented artificial bee colony (ABC) algorithm to solve the WSN-based PSPI problem and verifies its feasibility and robustness. Extensive simulation results show that the ABC and the particle swarm optimization (PSO) algorithm obtained similar identification results in the same simulation scenario. Moreover, the ABC and the PSO achieved much better performance than a traditionally used single-point search algorithm, i.e., the trust-region reflective algorithm.

Chemical Source Searching by Controlling a Wheeled Mobile Robot to Follow an Online Planned Route in Outdoor Field Environments

In this paper, we present an estimation-based route planning (ERP) method for chemical source searching using a wheeled mobile robot and validate its effectiveness with outdoor field experiments. The ERP method plans a dynamic route for the robot to follow to search for a chemical source according to time-varying wind and an estimated chemical-patch path (C-PP), where C-PP is the historical trajectory of a chemical patch detected by the robot, and normally different from the chemical plume formed by the spatial distribution of all chemical patches previously released from the source. Owing to the limitations of normal gas sensors and actuation capability of ground mobile robots, it is quite hard for a single robot to directly trace the intermittent and rapidly swinging chemical plume resulting from the frequent and random changes of wind speed and direction in outdoor field environments. In these circumstances, tracking the C-PP originating from the chemical source back could help the robot approach the source. The proposed ERP method was tested in two different outdoor fields using a wheeled mobile robot. Experimental results indicate that the robot adapts to the time-varying airflow condition, arriving at the chemical source with an average success rate and approaching effectiveness of about 90% and 0.4~0.6, respectively.

A Robot Equipped with a High-Speed LSPR Gas Sensor Module for Collecting Spatial Odor Information from On-Ground Invisible Odor Sources

Improving the efficiency of detecting the spatial distribution of gas information with a mobile robot is a great challenge that requires rapid sample collection, which is basically determined by the speed of operation of gas sensors. The present work developed a robot equipped with a high-speed gas sensor module based on localized surface plasmon resonance. The sensor module is designed to sample gases from an on-ground odor source, such as a footprint material or artificial odor marker, via a fine sampling tubing. The tip of the sampling tubing was placed close to the ground to reduce the sampling time and the effect of natural gas diffusion. On-ground ethanol odor sources were detected by the robot at high resolution (i.e., 2.5 cm when the robot moved at 10 cm/s), and the reading of gas information was demonstrated experimentally. This work may help in the development of environmental sensing robots, such as the development of odor source mapping and multirobot systems with pheromone tracing.