A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective

Exploration-exploitation model of moth-inspired olfactory navigation

Navigation of male moths towards females during the mating search offers a unique perspective on the exploration-exploitation (EE) model in decision-making. This study uses the EE model to explain male moth pheromone-driven flight paths. Wind tunnel measurements and three-dimensional tracking using infrared cameras have been leveraged to gain insights into male moth behaviour. During the experiments in the wind tunnel, disturbance to the airflow has been added and the effect of increased fluctuations on moth flights has been analysed, in the context of the proposed EE model. The exploration and exploitation phases are separated using a genetic algorithm to the experimentally obtained dataset of moth three-dimensional trajectories. First, the exploration-to-exploitation rate (EER) increases with distance from the source of the female pheromone is demonstrated, which can be explained in the context of the EE model. Furthermore, our findings reveal a compelling relationship between EER and increased flow fluctuations near the pheromone source. Using an olfactory navigation simulation and our moth-inspired navigation model, the phenomenon where male moths exhibit an enhanced EER as turbulence levels increase is explained. This research extends our understanding of optimal navigation strategies based on general biological EE models and supports the development of bioinspired navigation algorithms.

Neural Network-Based Autonomous Search Model with Undulatory Locomotion Inspired by Caenorhabditis Elegans

Caenorhabditis elegans (C. elegans) exhibits sophisticated chemotaxis behavior with a unique locomotion pattern using a simple nervous system only and is, therefore, well suited to inspire simple, cost-effective robotic navigation schemes. Chemotaxis in C. elegans involves two complementary strategies: klinokinesis, which allows reorientation by sharp turns when moving away from targets; and klinotaxis, which gradually adjusts the direction of motion toward the preferred side throughout the movement. In this study, we developed an autonomous search model with undulatory locomotion that combines these two C. elegans chemotaxis strategies with its body undulatory locomotion. To search for peaks in environmental variables such as chemical concentrations and radiation in directions close to the steepest gradients, only one sensor is needed. To develop our model, we first evolved a central pattern generator and designed a minimal network unit with proprioceptive feedback to encode and propagate rhythmic signals; hence, we realized realistic undulatory locomotion. We then constructed adaptive sensory neuron models following real electrophysiological characteristics and incorporated a state-dependent gating mechanism, enabling the model to execute the two orientation strategies simultaneously according to information from a single sensor. Simulation results verified the effectiveness, superiority, and realness of the model. Our simply structured model exploits multiple biological mechanisms to search for the shortest-path concentration peak over a wide range of gradients and can serve as a theoretical prototype for worm-like navigation robots.

Navigation Along Windborne Plumes of Pheromone and Resource-Linked Odors

Many insects locate resources such as a mate, a host, or food by flying upwind along the odor plumes that these resources emit to their source. A windborne plume has a turbulent structure comprised of odor filaments interspersed with clean air. As it propagates downwind, the plume becomes more dispersed and dilute, but filaments with concentrations above the threshold required to elicit a behavioral response from receiving organisms can persist for long distances. Flying insects orient along plumes by steering upwind, triggered by the optomotor reaction. Sequential measurements of differences in odor concentration are unreliable indicators of distance to or direction of the odor source. Plume intermittency and the plume's fine-scale structure can play a role in setting an insect's upwind course. The prowess of insects in navigating to odor sources has spawned bioinspired virtual models and even odor-seeking robots, although some of these approaches use mechanisms that are unnecessarily complex and probably exceed an insect's processing capabilities.

Towards Fast Plume Source Estimation with a Mobile Robot

The estimation of the parameters of an odour source is of high relevance for multiple applications, but it can be a slow and error prone process. This work proposes a fast particle filter-based method for source term estimation with a mobile robot. Two strategies are implemented in order to reduce the computational cost of the filter and increase its accuracy: firstly, the sampling process is adapted by the mobile robot in order to optimise the quality of the data provided to the estimation process; secondly, the filter is initialised only after collecting preliminary data that allow limiting the solution space and use a shorter number of particles than it would be normally necessary. The method assumes a Gaussian plume model for odour dispersion. This models average odour concentrations, but the particle filter was proved adequate to fit instantaneous concentration measurements to that model, while the environment was being sampled. The method was validated in an obstacle free controlled wind tunnel and the validation results show its ability to quickly converge to accurate estimates of the plume’s parameters after a reduced number of plume crossings.

Intelligent 3D Perception System for Semantic Description and Dynamic Interaction

This work proposes a novel semantic perception system based on computer vision and machine learning techniques. The main goal is to identify objects in the environment and extract their characteristics, allowing a dynamic interaction with the environment. The system is composed of a GPU processing source and a 3D vision sensor that provides RGB image and PointCloud data. The perception system is structured in three steps: Lexical Analysis, Syntax Analysis and finally an Analysis of Anticipation. The Lexical Analysis detects the actual position of the objects (or tokens) in the environment, through the combination of RGB image and PointCloud, surveying their characteristics. All information extracted from the tokens will be used to retrieve relevant features such as object velocity, acceleration and direction during the Syntax Analysis step. The anticipation step predicts future behaviors for these dynamic objects, promoting an interaction with them in terms of collisions, pull, and push actions. As a result, the proposed perception source can assign relevant information to mobile robots, not only about distances as traditional sensors, but about other environment characteristics and object behaviors. This novel perception source introduces a new class of skills to mobile robots. Experimental results obtained with a real robot are presented, showing the proposed perception source efficacy and potential.