Robot odor source localization in indoor environments based on gradient adaptive extremum seeking search

The purpose of this paper is to provide a new method for solving the problem of the 3D source localization in indoor environments with weak airflow by the experimental study on the multi-robot method. For this purpose, we developed a 3D MRO (mobile robot olfaction) system consisting of three mobile robots. The gas sensor carried with each robot has a moving range of 0.5 m to 1.5 m on the Z-axis. A total of 60 experiments were conducted to validate and compare the standard whale optimization algorithm (SWOA) and the standard particle swarm optimization (SPSO) methods in a 7.65 m×4.1 m experimental area with two source heights (0.75 m and 1.05 m). For two source heights, the success rate and localization step of the SPSO method are 56.7% (17/30) and 31.4 steps, respectively. In addition, the success rate and localization step of the SWOA method are 80% (24/30) and 32.9 steps, respectively. These results show that the SWOA method has strong application potential in indoor environments with weak airflow.

Odor source localization is a topic that continues to attract research attention, and in the past decade, mobile robots have been used to localize odor source. A robot which is capable of odor plume tracing can be employed in many applications, such as the detection of toxic gas, fire at its initial stage and unexploded bombs. Many plume-tracing robots were developed for indoor environments and the testing arena was ldquocleanrdquo of obstacles between the robot and the odor source. However, robots working in real applications are required to avoid collision with obstacles. For instance, indoor environments are normally filled with obstacles which can also affect the odor plume's shape and distribution, making plume tracing indoor a challenge. In this study, we developed a small size plume-tracing mobile robot that can localize odor source under fixed wind direction and fixed wind speed conditions. It can not only safely travel in an obstacle-filled environment but can also localize the odor source without being confused by change of plume shape and distribution.

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.

Experimental study on 3D source localization in indoor environments with weak airflow based on two bionic swarm intelligence algorithms

In this paper, the performance of a prototype standing wave thermoacoustic cooler is optimized using an extremum seeking control (ESC) algorithm. A tunable Helmholtz resonator was developed for a thermoacoustic cooler to change the boundary condition of the standing wave tube. The volume of the resonator is changed by changing the location of a piston on a ball-screw assembly driven by a dc motor. Multiparameter ESC was applied to optimize the cooling power via tuning both the boundary condition (piston location) and the driving frequency. Experiments were conducted for the online optimization under both fixed and varying operating conditions. The experimental results demonstrated the effectiveness of using ESC for maintaining maximum achievable performance. The effect of changing parameters in the ESC algorithm on the transient behavior was also investigated.

This paper researches a robotic localization system based on motion capture indoor environment. We apply motion capture technology to realize multi-robot positioning which achieves the multi-robots’ localization in indoor environment. It includes motion capture system, some robots, and main computer for data calibration and communication. Motions capture system based on the theory of photogrammetry which was analyzed. And the virtual experiment environment was established according to the actual experimental environment. The performance is demonstrated by numerical simulation and experiment.

Global localization problem is one of the classical and important problems in mobile robot. In this paper, we present an approach to solve robot global localization in indoor environments with grid map. It combines Hough Scan Matching (HSM) and grid localization method to get the initial knowledge of robot's pose quickly. For pose tracking, a scan matching technique called Iterative Closest Point (ICP) is used to amend the robot motion model, this can drastically decreases the uncertainty about the robot's pose in prediction step. Then accurate proposal distribution taking into account recent observation is introduced into particle filters to recover the best estimate of robot trajectories, which seriously reduces number of particles for pose tracking. The proposed approach can globally localize mobile robot fast and accurately. Experiment results carried out with robot data in indoor environments demonstrates the effectiveness of the proposed approach.

Due to the influence of turbulence, the practical gas plume in ventilated indoor environment is fluctuant and intermittent. In this case, the problem of odor source localization is quite complicated for robots. This paper proposes a novel odor source localization strategy based on cuckoo search algorithm by a group of mobile robots. This proposed algorithm combines the upwind search and local gas concentration optimization mechanism. It utilizes a robot which detects the highest gas concentration among all robots to guide other robots to search odor source in its upwind direction. Simulation results show that the proposed strategy can coordinate multiple robots to search odor source efficiently in dynamic plume environment. Using this strategy, the robots can also escape from both local high concentration and eddy areas.

The efficiency and accuracy of the odor source localization by robot, which just relies on the olfaction information, the wind direction information, are lower. Human often use the vision information to search suspicious target, and reach the target, and judge it by Olfaction information further. In this paper, inspired by Human's searching and declaration strategy, the robot's odor source localization, which is based on the olfaction and vision information fusion technology, is studied. According to the robot's searching the leak source of the dangerous gas tank, the odor source model is established by fusion of the olfaction and vision information. So the robot has a certain prior knowledge, and can achieve the odor source declaration by using the stereo vision technique. The control system of the robot is designed by basing on the blackboard model. The robot can achieve the odor source localization by the olfaction and vision information fusion technology at last.

This paper discusses odor source localization (OSL) using a mobile robot in an outdoor time-variant airflow environment. A novel OSL algorithm based on particle filters (PF) is proposed. When the odor plume clue is found, the robot performs an exploratory behavior, such as a plume-tracing strategy, to collect more information about the previously unknown odor source. In parallel, the information collected by the robot is exploited by the PF-based OSL algorithm to estimate the location of the odor source in real time. The process of the OSL is terminated if the estimated source locations converge within a given small area. The Bayesian-inference-based method is also performed for comparison. Experimental results indicate that the proposed PF-based OSL algorithm performs better than the Bayesian-inference-based OSL method.

In this paper, the approach of using optimization algorithm for odor source localization was proposed. The concept uses a number of movable odor robots which comprise of an odor sensor, an anemometer and a wireless communication module to sense the targeted odor. The odor robots construct a movable sensor network by which the measured odor intensity is sent to the processor wirelessly to perform the localization in order to point out the position of the odor source by using optimization algorithm. The odor source localization map is obtained as a result. The map represents the possibility that the odor source is located at any point in the area. The result also implies the direction that the odor robots should move toward to reach the odor source remotely. The proposed method was experimentally evaluated by constructing the odor source localization map using three, four, and five sensor nodes in which the accuracy to predict the position of the odor source can be observed.

The application of extremum seeking control (ESC) algorithm to antilock braking systems (ABSs) is attractive for maximizing the longitudinal braking force. Most currently reported results suffer from the drawback of steady-state oscillations around the extremum or the need to have the gradient information. In this article, a new ESC algorithm is proposed for application to ABSs to overcome these two drawbacks. The proposed algorithm is based on a novel dynamic nonlinear gradient observer (DNGO) and a longitudinal braking force estimator. The performance and the ultimate boundedness of the proposed ESC, DNGO, and the force estimator are analyzed. The proposed method is validated by simulation for dry and wet road surfaces and compared with two established results. The method is further validated on an experimental setup in the laboratory for dry and oily surfaces.

This paper proposes an odor source localization method based on bionic olfaction. The special nasal cavity and turnable head make mammalian have excellent odor source localization ability. According to this principle, a turnable bionic odor sensing device is proposed by this paper for odor source localization system. This sensing device can rotate freely within the range level 360°, and the detection directions of its sensing channels are different. This also proposes a pattern recognition algorithm based on K-L transform to analyze the data collected by odor sensing device, and the features of odor source are extracted correctly. Experimental results for odor source localization demonstrate the feasibility of the proposed approach.

New algorithm for footstep localization using seismic sensors in an indoor environment

This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it difficult to use algorithms that have been developed for ground robots. We then provide an overview of our solution to the key problems, including a multilevel sensing and control hierarchy, a high-speed laser scan-matching algorithm, an EKF for data fusion, a high-level SLAM implementation, and an exploration planner.1 Finally, we show experimental results demonstrating the helicopter's ability to navigate accurately and autonomously in unknown environments.