Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

Robotic Olfactory-Based Navigation with Mobile Robots

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. It has been viewed as challenging due to the turbulent nature of airflows and the resulting odor plume characteristics. The key to correctly finding an odor source is designing an effective olfactory-based navigation algorithm, which guides the robot to detect emitted odor plumes as cues in finding the source. This dissertation proposes three kinds of olfactory-based navigation methods to improve search efficiency while maintaining a low computational cost, incorporating different machine learning and artificial intelligence methods. A. Adaptive Bio-inspired Navigation via Fuzzy Inference Systems. In nature, animals use olfaction to perform many life-essential activities, such as homing, foraging, mate-seeking, and evading predators. Inspired by the mate-seeking behaviors of male moths, this method presents a behavior-based navigation algorithm for using on a mobile robot to locate an odor source. Unlike traditional bio-inspired methods, which use fixed parameters to formulate robot search trajectories, a fuzzy inference system is designed to perceive the environment and adjust trajectory parameters based on the current search situation. The robot can automatically adapt the scale of search trajectories to fit environmental changes and balance the exploration and exploitation of the search. B. Olfactory-based Navigation via Model-based Reinforcement Learning Methods. This method analogizes the odor source localization as a reinforcement learning problem. During the odor plume tracing process, the belief state in a partially observable Markov decision process model is adapted to generate a source probability map that estimates possible odor source locations. A hidden Markov model is employed to produce a plume distribution map that premises plume propagation areas. Both source and plume estimates are fed to the robot. A decision-making model based on a fuzzy inference system is designed to dynamically fuse information from two maps and balance the exploitation and exploration of the search. After assigning the fused information to reward functions, a value iteration-based path planning algorithm solves the optimal action policy. C. Robotic Odor Source Localization via Deep Learning-based Methods. This method investigates the viability of implementing deep learning algorithms to solve the odor source localization problem. The primary objective is to obtain a deep learning model that guides a mobile robot to find an odor source without explicating search strategies. To achieve this goal, two kinds of deep learning models, including adaptive neuro-fuzzy inference system (ANFIS) and deep neural networks (DNNs), are employed to generate the olfactory-based navigation strategies. Multiple training data sets are acquired by applying two traditional methods in both simulation and on-vehicle tests to train deep learning models. After the supervised training, the deep learning models are verified with unseen search situations in simulation and real-world environments. All proposed algorithms are implemented in simulation and on-vehicle tests to verify their effectiveness. Compared to traditional methods, experiment results show that the proposed algorithms outperform them in terms of the success rate and average search time. Finally, the future research directions are presented at the end of the dissertation

An open-source autopilot and bio-inspired source localisation strategies for miniature blimps

An Uncrewed Aerial Vehicle (UAV) is an airborne vehicle that has no people onboard and thus is either controlled remotely via radio signals or by autonomous capability. This thesis highlights the feasibility of using a bio-inspired miniature lighter than air UAV for indoor applications. While multicopters are the most used type of UAV, the smaller multicopter UAVs used in indoor applications have short flight times and are fragile making them vulnerable to collisions. For tasks such as gas source localisation where the agent would be deployed to detect a gas plume, the amount of air disturbance they create is a disadvantage. Miniature blimps are another type of UAV that are more suited to indoor applications due to their significantly higher collision tolerance. This thesis focuses on the development of a bio-inspired miniature blimp, called FishBlimp. A blimp generally creates significantly less disturbance to the airflow as it doesn’t have to support its own weight. This also usually enables much longer flight times. Using fins instead of propellers for propulsion further reduces the air disturbance as the air velocity is lower. FishBlimp has four fins attached in different orientations along the perimeter of a helium filled spherical envelope to enable it to move along the cardinal axes and yaw. Support for this new vehicle-type was added to the open-source flight control firmware called ArduPilot. Manual control and autonomous functions were developed for this platform to enable position hold and velocity control mode, implemented using a cascaded PID controller. Flight tests revealed that FishBlimp displayed position control with maximum overshoot of about 0.28m and has a maximum flight speed of 0.3m/s. FishBlimp was then applied to source localisation, firstly as a single agent seeking to identify a plume source using a modified Cast & Surge algorithm. FishBlimp was also developed in simulation to perform source localisation with multiple blimps, using a Particle Swarm Optimisation (PSO) algorithm. This enabled them to work cooperatively in order to reduce the time taken for them to find the source. This shows the potential of a platform like FishBlimp to carry out successful indoor source localisation missions

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

The radical integration of science, religion, and poetry in the writings of Loren Eiseley and Richard Wilbur

In a postmodern world turning away from the rigid categories of the past and "the univocal literalism" (Tarnas) of the modern mind, Loren Eiseley and Richard Wilbur bridge the schism between religion and science. Their essays and poems reinvigorate the romantic reconciliation between the mind and nature, subject and object, because, like Goethe, Wilbur and Eiseley see the human mind as a product of nature and the agent of nature's self revelation