Using insect electroantennogram sensors on autonomous robots for olfactory searches

Approximate information for efficient exploration-exploitation strategies

This paper addresses the exploration-exploitation dilemma inherent in decision-making, focusing on multiarmed bandit problems. These involve an agent deciding whether to exploit current knowledge for immediate gains or explore new avenues for potential long-term rewards. We here introduce a class of algorithms, approximate information maximization (AIM), which employs a carefully chosen analytical approximation to the gradient of the entropy to choose which arm to pull at each point in time. AIM matches the performance of Thompson sampling, which is known to be asymptotically optimal, as well as that of Infomax from which it derives. AIM thus retains the advantages of Infomax while also offering enhanced computational speed, tractability, and ease of implementation. In particular, we demonstrate how to apply it to a 50-armed bandit game. Its expression is tunable, which allows for specific optimization in various settings, making it possible to surpass the performance of Thompson sampling at short and intermediary times.

Ear-Bot: Locust Ear-on-a-Chip Bio-Hybrid Platform

During hundreds of millions of years of evolution, insects have evolved some of the most efficient and robust sensing organs, often far more sensitive than their man-made equivalents. In this study, we demonstrate a hybrid bio-technological approach, integrating a locust tympanic ear with a robotic platform. Using an Ear-on-a-Chip method, we manage to create a long-lasting miniature sensory device that operates as part of a bio-hybrid robot. The neural signals recorded from the ear in response to sound pulses, are processed and used to control the robot’s motion. This work is a proof of concept, demonstrating the use of biological ears for robotic sensing and control.

A bio-hybrid odor-guided autonomous palm-sized air vehicle

Biohybrid systems integrate living materials with synthetic devices, exploiting their respective advantages to solve challenging engineering problems. One challenge of critical importance to society is detecting and localizing airborne volatile chemicals. Many flying animals depend their ability to detect and locate the source of aerial chemical plumes for finding mates and food sources. A robot with comparable capability could reduce human hazard and drastically improve performance on tasks such as locating disaster survivors, hazardous gas leaks, incipient fires, or explosives. Three advances are needed before they can rival their biological counterparts: (1) a chemical sensor with a much faster response time that nevertheless satisfies the size, weight, and power constraints of flight, (2) a design, sensor suite, and control system that allows it to move toward the source of a plume fully autonomously while navigating obstacles, and (3) the ability to detect the plume with high specificity and sensitivity among the assortment of chemicals that invariably exist in the air. Here we address the first two, introducing a human-safe palm-sized air vehicle equipped with the odor-sensing antenna of an insect, the first odor-sensing biohybrid robot system to fly. Using this sensor along with a suite of additional navigational sensors, as well as passive wind fins, our robot orients upwind and navigates autonomously toward the source of airborne plumes. Our robot is the first flying biohybrid system to successfully perform odor localization in a confined space, and it is able to do so while detecting and avoiding obstacles in its flight path. We show that insect antennae respond more quickly than metal oxide gas sensors, enabling odor localization at an improved speed over previous flying robots. By using the insect antennae, we anticipate a feasible path toward improved chemical specificity and sensitivity by leveraging recent advances in gene editing.

A Study of Modified Infotaxis Algorithms in 2D and 3D Turbulent Environments

Emergency response to hazardous gases in the environment is an important research field in environmental monitoring. In recent years, with the rapid development of sensor technology and mobile device technology, more autonomous search algorithms for hazardous gas emission sources are proposed in uncertain environment, which can avoid emergency personnel from contacting hazardous gas in a short distance. Infotaxis is an autonomous search strategy without a concentration gradient, which uses scattered sensor data to track the location of the release source in turbulent environment. This paper optimizes the imbalance of exploitation and exploration in the reward function of Infotaxis algorithm and proposes a mobile strategy for the three-dimensional scene. In two-dimensional and three-dimensional scenes, the average steps of search tasks are used as the evaluation criteria to analyze the information trend algorithm combined with different reward functions and mobile strategies. The results show that the balance between the exploitation item and exploration item of the reward function proposed in this paper is better than that of the reward function in the Infotaxis algorithm, no matter in the two-dimensional scenes or in the three-dimensional scenes.

Light-Weight Portable Electroantennography Device as a Future Field-Based Tool for Applied Chemical Ecology

Portable electroantennograms (pEAG) can further our understanding of odor plume dynamics and complement laboratory-based electroantennogram tools. pEAG's can help to address important questions such as the influence of plume structure on insect behavior, the active space of semiochemical-baited traps, and the impact of biotic and abiotic factors on this active space. Challenges associated with pEAGs include their miniaturization and sensitivity, confounding environmental odors, and processing of data. Here, we describe a pEAG built with modern engineering hardware and techniques that is portable in being both light in weight (516 g) and smaller (12 × 12 × 8 cm, volume 1152 cm³) than earlier models. It is able to incorporate insects of a range of sizes (4 to 30 mm antennal length), has wireless communication (communication range of 600 m urban, 10 km line of sight), a stand-alone power supply, and uses both antennae of the test insect. We report normalized antennal responses from Epiphyas postvittana in a dose response experiment where our pEAG compared favorably with traditional laboratory EAG equipment for this species. Dose-response comparisons between E. postvittana, Agrotis ipsilon, and Lymantria dispar dispar showed mean detection limits from a pheromone source dose of 100, 100, and 1 ng, respectively, for our pEAG. This pEAG should allow future real-time analysis of EAG responses in the field in research on how insects interact with odor plumes and the factors that influence the active space of semiochemical-baited traps.

Dropping Counter: A Detection Algorithm for Identifying Odour-Evoked Responses from Noisy Electroantennograms Measured by a Flying Robot

The electroantennogram (EAG) is a technique used for measuring electrical signals from the antenna of an insect. Its rapid response time, quick recovery speed, and high sensitivity make it suitable for odour-tracking tasks employing mobile robots. However, its application to flying robots has not been extensively studied owing to the electrical and mechanical noises generated. In this study, we investigated the characteristics of the EAG mounted on a tethered flying quadcopter and developed a special counter-based algorithm for detecting the odour-generated responses. As the EAG response is negative, the algorithm creates a window and compares the values inside it. Once a value is smaller than the first one, the counter will increase by one and finally turns the whole signal into a clearer odour stimulated result. By experimental evaluation, the new algorithm gives a higher cross-correlation coefficient when compared with the fixed-threshold method. The result shows that the accuracy of this novel algorithm for recognising odour-evoked EAG signals from noise exceeds that of the traditional method; furthermore, the use of insect antennae as odour sensors for flying robots is demonstrated to be feasible.

Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution

Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this paper, we show that the slow response of MOX sensors can be compensated by deconvolution, provided that an invertible, parametrized, sensor model is available. We consider a nonlinear, first-order dynamic model that is mathematically tractable for MOX identification and deconvolution. By transforming the sensor signal in the log-domain, the system becomes linear in the parameters and these can be estimated by the least-squares techniques. Moreover, we use the MOX diversity in a sensor array to avoid training with a supervised signal. The information provided by two (or more) sensors, exposed to the same flow but responding with different dynamics, is exploited to recover the ground truth signal (gas input). This approach is known as blind deconvolution. We demonstrate its efficiency on MOX sensors recorded in turbulent plumes. The reconstructed signal is similar to the one obtained with a fast photo-ionization detector (PID). The technique is thus relevant to track a fast-changing gas concentration with MOX sensors, resulting in a compensated response time comparable to that of a PID.

Organismal Engineering: Towards a Robotic Taxonomic Key for Devices Using Organic Materials

Can we create robots with the behavioral flexibility and robustness of animals? Engineers often use bio-inspiration to mimic animals. Recent advances in tissue engineering now allow the use of components from animals. By integrating organic and synthetic components, researchers are moving towards the development of engineered organisms whose structural framework, actuation, sensing, and control are partially or completely organic. This review discusses recent exciting work demonstrating how organic components can be used for all facets of robot development. Based on this analysis, we propose a Robotic Taxonomic Key to guide the field towards a unified lexicon for device description.

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor sources is an essential task for organism survival. Insects are well-studied organisms with regard to odor tracking, and their behavioral strategies have been applied to mobile robots for evaluation. This "bottom-up" approach is a fundamental way to develop biomimetic robots; however, the biological analyses and the modeling of behavioral mechanisms are still ongoing. Therefore, it is still unknown how such a biological system actually works as the controller of a robotic platform. To answer this question, we have developed an insect-controlled robot in which a male adult silkmoth (Bombyx mori) drives a robot car in response to odor stimuli; this can be regarded as a prototype of a future insect-mimetic robot. In the cockpit of the robot, a tethered silkmoth walked on an air-supported ball and an optical sensor measured the ball rotations. These rotations were translated into the movement of the two-wheeled robot. The advantage of this "hybrid" approach is that experimenters can manipulate any parameter of the robot, which enables the evaluation of the odor-tracking capability of insects and provides useful suggestions for robotic odor-tracking. Furthermore, these manipulations are non-invasive ways to alter the sensory-motor relationship of a pilot insect and will be a useful technique for understanding adaptive behaviors.

New electroantennography method on a marine shrimp in water

Antennular chemoreception in aquatic decapods is well studied via the recording of single chemoreceptor neuron activity in the antennule, but global responses of the antennule (or antennae in insects) by electroantennography (EAG) has so far been mainly restricted to aerial conditions. We present here a well-established underwater EAG method to record the global antennule activity in the marine shrimp Palaemon elegans in natural (aqueous) conditions. EAG responses to food extracts, recorded as net positive deviations of the baseline, are reproducible, dose-dependent and exhibit sensory adaptation. This new EAG method opens a large field of possibilities for studying in vivo antennular chemoreception in aquatic decapods, in a global approach to supplement current, more specific techniques.

High-speed odor transduction and pulse tracking by insect olfactory receptor neurons

Sensory systems encode both the static quality of a stimulus (e.g., color or shape) and its kinetics (e.g., speed and direction). The limits with which stimulus kinetics can be resolved are well understood in vision, audition, and somatosensation. However, the maximum temporal resolution of olfactory systems has not been accurately determined. Here, we probe the limits of temporal resolution in insect olfaction by delivering high frequency odor pulses and measuring sensory responses in the antennae. We show that transduction times and pulse tracking capabilities of olfactory receptor neurons are faster than previously reported. Once an odorant arrives at the boundary layer of the antenna, odor transduction can occur within less than 2 ms and fluctuating odor stimuli can be resolved at frequencies more than 100 Hz. Thus, insect olfactory receptor neurons can track stimuli of very short duration, as occur when their antennae encounter narrow filaments in an odor plume. These results provide a new upper bound to the kinetics of odor tracking in insect olfactory receptor neurons and to the latency of initial transduction events in olfaction.

Reactive searching and infotaxis in odor source localization

Male moths aiming to locate pheromone-releasing females rely on stimulus-adapted search maneuvers complicated by a discontinuous distribution of pheromone patches. They alternate sequences of upwind surge when perceiving the pheromone and cross- or downwind casting when the odor is lost. We compare four search strategies: three reactive versus one cognitive. The former consist of pre-programmed movement sequences triggered by pheromone detections while the latter uses Bayesian inference to build spatial probability maps. Based on the analysis of triphasic responses of antennal lobe neurons (On, inhibition, Off), we propose three reactive strategies. One combines upwind surge (representing the On response to a pheromone detection) and spiral casting, only. The other two additionally include crosswind (zigzag) casting representing the Off phase. As cognitive strategy we use the infotaxis algorithm which was developed for searching in a turbulent medium. Detection events in the electroantennogram of a moth attached to a robot indirectly control this cyborg, depending on the strategy in use. The recorded trajectories are analyzed with regard to success rates, efficiency, and other features. In addition, we qualitatively compare our robotic trajectories to behavioral search paths. Reactive searching is more efficient (yielding shorter trajectories) for higher pheromone doses whereas cognitive searching works better for lower doses. With respect to our experimental conditions (2 m from starting position to pheromone source), reactive searching with crosswind zigzag yields the shortest trajectories (for comparable success rates). Assuming that the neuronal Off response represents a short-term memory, zigzagging is an efficient movement to relocate a recently lost pheromone plume. Accordingly, such reactive strategies offer an interesting alternative to complex cognitive searching.