Speed limitation of a mobile robot and methodology of tracing odor plume in airflow environments

AbstractThe methodology of tracing odor plume via a mobile robot is considered. In this research, two typical plume-tracing methods, i.e., a zigzagging method and an upwind method, are tested in four airflow fields with different long-time average wind speeds when the robot is set to possessing four different maximum speeds. According to the simulation results, it can be deduced that the zigzagging algorithms would be efficient when the robot moves faster than the odor plume or airflow, and the upwind algorithms are preferred especially when the robot is slow

Novel wind sensor for robotic chemical plume tracking

Copyright © 2006 IEEEWind sensors are widely used to measure wind speed and direction for weather forecasting or for other purposes including measuring wind flow of wind tunnel in a laboratory. Due to its wide applications, many wind sensors are commercially available off-the-shelf. This study concerns wind direction and speed sensing for robotic chemical plume tracking studies and applications. Therefore, it is highly demanded to have a wind sensor that has a small size, light weight and less power consumption. In this paper, the design of the wind sensor down to the post-processing of the sensor reading data are presented. The result shows that the wind sensor performance is satisfactory and can be implemented for robotic chemical plume tracking study to provide proper wind speed and direction information.Tien-Fu Lu and Chee Liang N

Information-Driven Gas Source Localization Exploiting Gas and Wind Local Measurements for Autonomous Mobile Robots

Gas source localization (GSL) by an olfactory robot is a research field with a great potential for applications but also with numerous unsolved challenges, particularly when the search must take place in realistic, indoor environments that feature obstacles and turbulent airflows. In this work, we present a new probabilistic GSL method for a terrestrial mobile robot that revolves around the propagation of local estimations throughout the environment. By exploiting the geometry of the environment as the basis for this propagation, we avoid relying on analytical dispersion models, eliminating the need to assume controlled environmental conditions. Simulated and real experiments are presented in different indoor environments featuring multiple rooms and turbulent flows, demonstrating the suitability of our approach for locating the emitting gas source.Junta de Andalucía, Fondos FEDER. Proyectos P20_01302, TEP2012-53

Mobile Robots for Localizing Gas Emission Sources on Landfill Sites: Is Bio-Inspiration the Way to Go?

Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully “translated” into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms

Swarm Robotic Plume Tracking for Intermittent and Time-Variant Odor Dispersion

This paper presents a method for odor plume tracking by a swarm of robots in realistic conditions. In real world environments, the chemical concentration within an odor plume is patchy, intermittent and time-variant. This study shows that swarm robots can cooperatively track the odor plume towards its source by establishing a cohesive spatial sensor network to deal with the turbulences and patchy nature of odor plumes. The robots move together and maintain a distance margin between themselves in order to keep the cohesion of the constructed sensor network while the odor concentration and air-flow speed are considered in the equations of navigation of the robots in the network to more efficiently track the plume. The method is evaluated in simulation against various number of robots, the emission rate of the odor source, the number of obstacles in the environment and the size of the testing environment. The emergent behavior of the swarm proves the functionality, robustness and scalability of the system in different conditions

Adapting an Ant Colony Metaphor for Multi-Robot Chemical Plume Tracing

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments

Effective Exploration Behavior for Chemical-Sensing Robots

Mobile robots that can effectively detect chemical effluents could be useful in a variety of situations, such as disaster relief or drug sniffing. Such a robot might mimic biological systems that exhibit chemotaxis, which is movement towards or away from a chemical stimulant in the environment. Some existing robotic exploration algorithms that mimic chemotaxis suffer from the problems of getting stuck in local maxima and becoming “lost”, or unable to find the chemical if there is no initial detection. We introduce the use of the RapidCell algorithm for mobile robots exploring regions with potentially detectable chemical concentrations. The RapidCell algorithm mimics the biology behind the biased random walk of Escherichia coli (E. coli) bacteria more closely than traditional chemotaxis algorithms by simulating the chemical signaling pathways interior to the cell. For comparison, we implemented a classical chemotaxis controller and a controller based on RapidCell, then tested them in a variety of simulated and real environments (using phototaxis as a surrogate for chemotaxis). We also added simple obstacle avoidance behavior to explore how it affects the success of the algorithms. Both simulations and experiments showed that the RapidCell controller more fully explored the entire region of detectable chemical when compared with the classical controller. If there is no detectable chemical present, the RapidCell controller performs random walk in a much wider range, hence increasing the chance of encountering the chemical. We also simulated an environment with triple effluent to show that the RapidCell controller avoided being captured by the first encountered peak, which is a common issue for the classical controller. Our study demonstrates that mimicking the adapting sensory system of E. coli chemotaxis can help mobile robots to efficiently explore the environment while retaining their sensitivity to the chemical gradient

Information-based search for an atmospheric release using a mobile robot: algorithm and experiments

Finding the location and strength of an unknown hazardous release is of paramount importance in emergency response and environmental monitoring, thus it has been an active research area for several years known as source term estimation. This paper presents a joint Bayesian estimation and planning algorithm to guide a mobile robot to collect informative measurements, allowing the source parameters to be estimated quickly and accurately. The estimation is performed recursively using Bayes’ theorem, where uncertainties in the meteorological and dispersion parameters are considered and the intermittent readings from a low-cost gas sensor are addressed by a novel likelihood function. The planning strategy is designed to maximize the expected utility function based on the estimated information gain of the source parameters. Subsequently, this paper presents the first experimental result of such a system in turbulent, diffusive conditions, in which a ground robot equipped with a low-cost gas sensor responds to the hazardous source stimulated by incense sticks. The experimental results demonstrate the effectiveness of the proposed estimation and search algorithm for source term estimation based on a mobile robot and a low-cost sensor

Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile Robots

This paper addresses the collective odor source localization (OSL) problem in a time-varying airflow environment using mobile robots. A novel OSL methodology which combines odor-source probability estimation and multiple robots’ search is proposed. The estimation phase consists of two steps: firstly, the separate probability-distribution map of odor source is estimated via Bayesian rules and fuzzy inference based on a single robot’s detection events; secondly, the separate maps estimated by different robots at different times are fused into a combined map by way of distance based superposition. The multi-robot search behaviors are coordinated via a particle swarm optimization algorithm, where the estimated odor-source probability distribution is used to express the fitness functions. In the process of OSL, the estimation phase provides the prior knowledge for the searching while the searching verifies the estimation results, and both phases are implemented iteratively. The results of simulations for large-scale advection–diffusion plume environments and experiments using real robots in an indoor airflow environment validate the feasibility and robustness of the proposed OSL method