Design and Performance Evaluation of an Infotaxis-Based Three-Dimensional Algorithm for Odor Source Localization

In this paper we tackle the problem of finding the source of a gaseous leak with a robot in a three-dimensional (3-D) physical space. The proposed method extends the operational range of the probabilistic Infotaxis algorithm [1] into 3-D and makes multiple improvements in order to increase its performance in such settings. The method has been tested systematically through high-fidelity simulations and in a wind tunnel emulating realistic conditions. The impact of multiple algorithmic and environmental parameters has been studied in the experiments. The algorithm shows good performance in various environmental conditions, particularly in high wind speeds and different source release rates

Adaptive Lévy Taxis for Odor Source Localization in Realistic Environmental Conditions

Odor source localization with mobile robots has recently been subject to many research works, but remains a challenging task mainly due to the large number of environmental parameters that make it hard to describe gas concentration fields. We designed a new algorithm called Adaptive Lévy Taxis (ALT) to achieve odor plume tracking through a correlated random walk. In order to compare its performances with well-established solutions, we have implemented three moth-inspired algorithms on the same robotic platform. To improve the performance of the latter algorithms, we developed a rigorous way to determine one of their key parameters, the odor concentration threshold at which the robot considers to be inside or outside the plume. The methods have been systematically evaluated in a large wind tunnel under various environmental conditions. Experiments revealed that the performance of ALT is consistently good in all environmental conditions (in particular when compared to the three reference algorithms) in terms of both distance traveled to find the source and success rate

A 3-D Bio-inspired Odor Source Localization and its Validation in Realistic Environmental Conditions

Finding the source of gaseous compounds released in the air with robots finds several applications in various critical situations, such as search and rescue. While the distribution of gas in the air is inherently a 3D phenomenon, most of the previous works have downgraded the problem into 2D search, using only ground robots. In this paper, we have designed a bio-inspired 3D algorithm involving cross-wind Levy Walk, spiralling and upwind surge. The algorithm has been validated using high-fidelity simulations, and evaluated in a wind tunnel which represents a realistic controlled environment, under different conditions in terms of wind speed, source release rates and odor threshold. Studying success rate and execution time, the results show that the proposed method outperforms its 2D counterpart and is robust to the various setup conditions, especially to the source release rate and the odor threshold

Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots.

The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot’s control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel.Partial funding for open access charge: Universidad de Málag

Predicting extraversion from non-verbal features during a face-to-face human-robot interaction

International audienceIn this paper we present a system for automatic prediction of extraversion during the first thin slices of human-robot interaction (HRI). This work is based on the hypothesis that personality traits and attitude towards robot appear in the behavioural response of humans during HRI. We propose a set of four non-verbal movement features that characterize human behavior during interaction. We focus our study on predicting Extraversion using these features , extracted from a dataset consisting of 39 healthy adults interacting with the humanoid iCub. Our analysis shows that it is possible to predict to a good level (64%) the Extraversion of a human from a thin slice of interaction relying only on non-verbal movement features. Our results are comparable to the state-of-the-art obtained in HHI [ 23 ]

A Distributed Source Term Estimation Algorithm for Multi-Robot Systems

Finding sources of airborne chemicals with mobile sensing systems finds applications in safety, security, and emergency situations related to medical, domestic, and environmental domains. Given the often critical nature of all the applications, it is important to reduce the amount of time necessary to accomplish this task through intelligent systems and algorithms. In this paper, we extend a previously presented algorithm based on source term estimation for odor source localization for homogeneous multi-robot systems. By gradually increasing the level of coordination among multiple mobile robots, we study the benefits of a distributed system on reducing the amount of time and resources necessary to achieve the task at hand. The method has been evaluated systematically through high-fidelity simulations and in a wind tunnel emulating realistic and repeatable conditions in different coordination scenarios and with different number of robots

Design and Performance Evaluation of an Algorithm Based on Source Term Estimation for Odor Source Localization

Finding sources of airborne chemicals with mobile sensing systems finds applications across safety, security, environmental monitoring, and medical domains. In this paper, we present an algorithm based on Source Term Estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose a novel strategy to balance exploration and exploitation in navigation. Moreover, we study two variants of the algorithm, one exploiting a global and the other one a local framework. The method was evaluated through high-fidelity simulations and in a wind tunnel emulating a quasi-laminar air flow in a controlled environment, in particular by systematically investigating the impact of multiple algorithmic and environmental parameters (wind speed and source release rate) on the overall performance. The outcome of the experiments showed that the algorithm is robust to different environmental conditions in the global framework, but, in the local framework, it is only successful in relatively high wind speeds. In the local framework, on the other hand, the algorithm is less demanding in terms of energy consumption as it does not require any absolute positioning information from the environment and the robot travels less distance compared to the global framework

Role of interphase layers in mechanical properties of nacreous structures

An exceptional combination of mechanical properties such as high stiffness, high strength, and high toughness makes nacre one of the most remarkable natural materials. It has been shown that these outstanding properties arise from the multilayered brick-and-mortar structure of the nacre and the vital nanoscale features in its structure. Moreover, problem-solving strategies of naturally growing composites such as nacre give us a unique vision to design, optimize and fabricate simultaneously tough, stiff, and strong composites. It is widely known that nature’s biologically evolved, complex and effective functionally graded interfaces essentially enable the exceptional mechanical properties of biological composites. Particularly in nacre, the organic–inorganic interface referred hereto as interphase, in which the confined protein layer behaves stiffer and stronger in the proximity of the calcium carbonate minerals platelets, provides a key role in its toughness that is orders of magnitude higher than aragonite platelets as its main constituent. Hence, understanding the role of the interphase properties in the toughening mechanisms is paramount in the design and synthesis of high-performing bioinspired materials. In this study, a micromechanical model of the “Brick-Mortar” and “Brick-Bridge-Mortar” composites is presented focusing on the role of interphase properties on strengthening and toughening mechanisms. Shear-lag theory is employed on a simplified two-dimensional unit-cell of a multilayered composite. The computed closed-form solutions for the displacements as a function of constituent properties are used to calculate elastic modulus, strength, and work-of-fracture for a wide range of multilayered materials. Our results show that the properties of the soft phase in mineral bridges proximity can have a significant effect on the mechanical properties. The detailed relationships presented can help identify the future directions for advanced material design and development.<br/

An Algorithm for Odor Source Localization based on Source Term Estimation

Finding sources of airborne chemicals with mobile sensing systems finds applications across the security, safety, domestic, medical, and environmental domains. In this paper, we present an algorithm based on source term estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose an innovative strategy to balance exploration and exploitation in navigation. The method has been evaluated systematically through high-fidelity simulations and in a wind tunnel emulating realistic and repeatable conditions. The impact of multiple algorithmic and environmental parameters has been studied in the experiments