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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 novel bioinspired neural network is proposed as a replacement for traditional data processing methods in the electronic nose (e-nose) that we designed. This neural network mimics the main structures of the mammalian olfactory system. It contains olfactory sensing neurons, mitral cells, and granule cells. The proposed method can directly use the raw data collected from sensors in the e-nose without any signal preprocessing, feature selection, or reduction. The output neurons of the network can change the sensors’ responses into two new time series, of which we only use the variances to perform classification. This significantly simplifies the data processing procedure in e-noses. In order to test the performance of the proposed bioinspired neural network in the e-nose, two sampling methods, three classification methods, and seven kinds of Chinese liquors were employed. The highest classification rates of the proposed method and traditional method are 100% and 93%, respectively.

Localization of the hazardous gas source plays an important role in the protection of public security, since it can save a lot of time for subsequent rescue works. For gas source localization (GSL), a large number of gas sensor nodes can be rapidly deployed to construct a wireless sensor network (WSN) and cover the whole concerned area. Although least-squares (LS) methods can solve the problem of GSL in WSNs regardless of the distribution of measurement noises, centralized LS methods are not power efficient and robust since they require the gathering and processing of large-scale measurements on a central node. In this paper, we propose a novel distributed method for GSL in WSNs, which is performed on a sequence of sensor nodes successively. Each sensor node in the sequence conducts an individual estimation and a convex combination. The individual estimation is inspired by the LS formulation of the problem of GSL in WSNs. The proposed method is fully distributed and computationally efficient, and it does not rely on the absolute location of the sensor nodes. Extensive simulation results and a set of experimental results demonstrate that the success rate and localization accuracy of the proposed method are generally higher than those of the trust-region-reflective method.

Maintaining contact between the robot and plume is significant in chemical plume tracing (CPT). In the time immediately following the loss of chemical detection during the process of CPT, Track-Out activities bias the robot heading relative to the upwind direction, expecting to rapidly re-contact the plume. To determine the bias angle used in the Track-Out activity, we propose an online instance-based reinforcement learning method, namely virtual trail following (VTF). In VTF, action-value is generalized from recently stored instances of successful Track-Out activities. We also propose a collaborative VTF (cVTF) method, in which multiple robots store their own instances, and learn from the stored instances, in the same database. The proposed VTF and cVTF methods are compared with biased upwind surge (BUS) method, in which all Track-Out activities utilize an offline optimized universal bias angle, in an indoor environment with three different airflow fields. With respect to our experimental conditions, VTF and cVTF show stronger adaptability to different airflow environments than BUS, and furthermore, cVTF yields higher success rates and time-efficiencies than VTF.

This paper investigates the problem of locating a continuous chemical source using the concentration measurements provided by a wireless sensor network (WSN). Such a problem exists in various applications: eliminating explosives or drugs, detecting the leakage of noxious chemicals, etc. The limited power and bandwidth of WSNs have motivated collaborative in-network processing which is the focus of this paper. We propose a novel distributed least-squares estimation (DLSE) method to solve the chemical source localization (CSL) problem using a WSN. The DLSE method is realized by iteratively conducting convex combination of the locally estimated chemical source locations in a distributed manner. Performance assessments of our method are conducted using both simulations and real experiments. In the experiments, we propose a fitting method to identify both the release rate and the eddy diffusivity. The results show that the proposed DLSE method can overcome the negative interference of local minima and saddle points of the objective function, which would hinder the convergence of local search methods, especially in the case of locating a remote chemical source.

Chemical source localization (CSL) is significant for the protection of public security. In typical CSL problems, a collaborative group of multiple robots are expected to perform better than a single robot. This paper proposes a behavior-based multi-robot chemical source localization (BbMR-CSL) architecture. It includes three group behaviors, i.e., the chemical finding, chemical source tracing (CST), and chemical source declaration behaviors, among which the CST behavior is further divided into two individual behaviors. A hierarchical behavior selection method is designed for coordinating the behaviors in the BbMR-CSL architecture. Then, a method framework is also proposed for combining evolutionary computation (EC) with the BbMR-CSL architecture. Based on the proposed framework, three typical EC algorithms (i.e., the genetic algorithm, the artificial bee colony algorithm (ABC), and the global-best-guided ABC) are implemented in the BbMR-CSL architecture. The resulting behavior-based CSL methods were tested and compared in a simulated filamentous plume environment.

We propose an effective algorithm to localize a continuous gas-leakage source using a wireless sensor network (WSN). We first derive the possible source locations by reverse derivation from the diffusion model using Lambert W function, and then the most possible source location is determined based on prior information. Finally, the most-possible source location is incorporated into the localization result, which is updated in a distributed sequential manner by adaptive weighted summation. Our algorithm is shown to achieve excellent performance, even in the case that the actual source is far away from the sensor-deployment area. The computational complexity is applicable for WSNs with relatively low calculating ability.