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

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

The Long-Rage Directional Behavior of the Nematode C. Elegans

Like any mobile organism, C. elegans relies on sensory cues to find food. In the absence of such cues, animals might display defined search patterns or other stereotyped behavior. The motion of C. elegans has previously been characterized as a sinusoid whose direction can be modulated by gradual steering or by sharp turns, reversals and omega bends. However, such a fine-grained behavioral description does not by itself predict the longrange features of the animals’ pattern of movement. Using large (24 cm x 24 cm) Petri dishes, we characterized the movement pattern of C. elegans in the absence of stimuli. To collect trajectories over such a large surface, we devised an imaging setup employing an array of consumer flatbed scanners. We have confirmed quantitatively the results obtained with the scanner-array setup with a camera imaging setup, in a more stringently homogeneous environment. Wild-type worms display striking behavior in the absence of food. The majority (~60%) of the animals’ paths displays persistence in the direction of motion over length scales that are 50-100 times the body-length of C. elegans. The overall direction of movement differs from animal to animal, suggesting that the directed motion we observe might not be interpreted as a taxis to an external cue in the experimental environment. Interestingly, animals appear to exhibit directionality at large scales despite nondirectional motion at smaller scales. We quantified the extent of local directional persistence by computing the autocorrelation function of the velocities. Unexpectedly, correlations in the direction of motion decay over time scales that are much faster than the scales over which directional persistence appears to be maintained. We sought to establish quantitatively that the worm motion is, in fact, biased. To determine whether a null, random walk-like model of locomotion could account for directional behavior, we generated synthetic trajectories drawing from the same angle and step distributions of individual trajectories, and quantified the probabilities of obtaining larger net displacements than the experimental. Such a model fails to reproduce the experimental results. Moreover, the mean square displacements computed for the data display non-diffusive behavior, further demonstrating that the observed directional persistence cannot be explained by a simple random-walk model. To corroborate the hypothesis of biased movement in a model-independent fashion, we employed a geometrical characterization of the trajectories. Isotropic, unbiased walks result in paths that display a random distribution of turning angles between consecutive segments. In contrast, parsing of the worm’s trajectories yields different results depending on the segmentation scale adopted. In fact, increasing the segment size results in increasingly narrow turning angle distributions, centered around the zero. This suggests the emergence of directional coherence at long time scales. In order to investigate whether directional persistence is attained by a sensory mechanism, we analyzed the paths displayed by animals with impaired sensory function. Animals mutant for che-2, which display disrupted ciliary morphology and pleiotropic behavioral defects, exhibited non-directional behavior. Surprisingly however, daf-19 mutants, which lack sensory cilia altogether, displayed residual directionality, albeit at a lower penetrance (~20%) than the wild-type. This result suggests that directionality might implicate sensory modalities that do not require ciliary function, such as AFD-mediated thermosensation or URX-mediated oxygen sensation. Alternatively, the behavior of daf-19 mutants might imply that neural activity, but not sensory inputs, are required to achieve directed motion. Mutations in osm-9, a TRPV channel implicated in several avoidance behaviors in the worm, did not result in an observable phenotype. In contrast, mutations in tax-2/tax-4, a cGMP-gated channel required to transduce a number of sensory stimuli, resulted in loss of directionality. However, specific mutations targeting the signal transduction pathways for thermotaxis, olfaction, phototaxis, and aerotaxis, upstream of TAX-4, did not disrupt directional behavior. To get further insight into the nature of the stimulus directing the animals’ behavior, if any, we performed rescue experiments of TAX-4 function in specific subsets of neurons. In agreement with the results obtained by genetic lesions in the signal transduction pathways for thermotaxis and odortaxis, no rescue of directional behavior was observed when expressing TAX-4 in the thermosensory neuron AFD, or in the olfactory neurons AWB and AWC. Partial rescue of wild-type behavior was obtained by expression of TAX-4 in a set of five cells, which comprised the oxygen-sensing AQR, PQR and URX neurons as well as the ASJ and ASK sensory neurons, which transduce chemical stimuli and responses to dauer pheromone. To address the concern that the animals’ motion might be directed to a chemosensory cue within the plate, we investigated the correlation between path directions displayed by animals that were assayed on a same plate. We did not observe a detectable correlation between path headings, indicating that the worm is not chemotaxing to a plate-specific cue. In conclusion, our results indicate that the motion of C. elegans cannot be assimilated to a random walk, and that directional persistence arises at long times despite local nondirectional behavior. In addition, although we have not conclusively ruled out a sensorybased explanation, the genetic and phenomenological evidence gathered foreshadows the intriguing possibility that C. elegans might be achieving directional motion by relying solely on self-based information

Bioinspired approaches for coordination and behaviour adaptation of aerial robot swarms

Behavioural adaptation is a pervasive component in a myriad of animal societies. A well-known strategy, known as Levy Walk, has been commonly linked to such adaptation in foraging animals, where the motion of individuals couples periods of localized search and long straight forward motions. Despite the vast number of studies on Levy Walks in computational ecology, it was only in the past decade that the first studies applied this concept to robotics tasks. Therefore, this Thesis draws inspiration from the Levy Walk behaviour, and its recent applications to robotics, to design biologically inspired models for two swarm robotics tasks, aiming at increasing the performance with respect to the state of the art. The first task is cooperative surveillance, where the aim is to deploy a swarm so that at any point in time regions of the domain are observed by multiple robots simultaneously. One of the contributions of this Thesis, is the Levy Swarm Algorithm that augments the concept of Levy Walk to include the Reynolds’ flocking rules and achieve both exploration and coordination in a swarm of unmanned aerial vehicles. The second task is adaptive foraging in environments of clustered rewards. In such environments behavioural adaptation is of paramount importance to modulate the transition between exploitation and exploration. Nature enables these adaptive changes by coupling the behaviour to the fluctuation of hormones that are mostly regulated by the endocrine system. This Thesis draws further inspiration from Nature and proposes a second model, the Endocrine Levy Walk, that employs an Artificial Endocrine System as a modulating mechanism of Levy Walk behaviour. The Endocrine Levy Walk is compared with the Yuragi model (Nurzaman et al., 2010), in both simulated and physical experiments where it shows its increased performance in terms of search efficiency, energy efficiency and number of rewards found. The Endocrine Levy Walk is then augmented to consider social interactions between members of the swarm by mimicking the behaviour of fireflies, where individuals attract others when finding suitable environmental conditions. This extended model, the Endocrine Levy Firefly, is compared to the Levy+ model (Sutantyo et al., 2013) and the Adaptive Collective Levy Walk Nauta et al. (2020). This comparison is also made both in simulated and physical experiments and assessed in terms of search efficiency, number of rewards found and cluster search efficiency, strengthening the argument in favour of the Endocrine Levy Firefly as a promising approach to tackle collaborative foragin

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Multiscale Modeling of Bacterial Chemotaxis

One of the central questions of modern systems biology is the role of microscopic parameters of a single cell in the behavior of a cell population. Multiscale models help to address this problem, allowing to understand population behavior from the information about single-cell molecular components and reactions. This goal requires models that are sufficiently detailed to capture central intracellular processes, but at the same time enable simulation of entire cell populations. In this work a novel multiscale (hybrid) model is presented, which describes chemotactic Escherichia coli bacterium by a combination of heterogeneous mathematical approaches in one platform: rapid-equilibrium (algebraic) models, ordinary differential equations, and stochastic processes. The multiscale approach is based on time-scale separation of key reactions. The resulting model of chemotactic bacterium describes signal processing by mixed chemoreceptor clusters (MWC model), adaptation through methylation, running and tumbling of a cell with several flagellar motors. The model is implemented in a program RapidCell. It outperforms the present simulation software in reproducing the experimental data on pathway sensitivity, and simulates bacterial populations in a computationally efficient way. The model was used to investigate chemotaxis in different gradients. A theoretical analysis of the receptor cluster (MWC) model suggested a new, constant-activity type of gradient to systematically study chemotactic behavior of bacteria in silico. Using the unique properties of this gradient, it is shown that the optimal chemotaxis is observed in a narrow range of CheA kinase activity, where concentration of the response regulator CheYp falls into the operating range of flagellar motors. Simulations further confirm that the CheB phosphorylation feedback improves chemotactic efficiency in a number of gradients by shifting the average CheYp concentration to fit the motor operating range. Comparative simulations of motility in liquid and porous media suggest that adaptation time required for optimal chemotaxis depends on the medium. In liquid medium, the variability in adaptation times among cells may be evolutionary favourable to ensure co-existence of subpopulations that will be optimally tactic in different gradients. However, in a porous medium (agar) such variability appears to be less important, because agar structure poses mainly negative selection, against subpopulations with low levels of adaptation enzymes. A detailed model of cell motion predicts existence of an additional mechanism of gradient navigation in E. coli. Based on the experimentally observed dependence of cell tumbling angle on the number of clockwise-rotating motors, the model suggests that not only the tumbling frequency, but also the angle of reorientation during a tumble depends on the swimming direction along the gradient. Although the difference in mean tumbling angles up and down the gradient predicted by the model is small, it results in a dramatic enhancement of the cellular drift velocity along the gradient. This result demonstrates a new level of optimization in E. coli chemotaxis, which arises from collective switching of several flagellar motors and a resulting fine tuning of tumbling angle. Similar strategy is likely to be used by other peritrichously flagellated bacteria, and indicates a yet another level of evolutionary optimization in bacterial chemotaxis. Concluding, multiscale models as the one presented here can be an important research instrument for understanding the cell behavior. They reflect the most important experimental knowledge about the biological system, and allow to carry out computational experiments of high complexity, which may be too complicated for experimental trials. Currently, there is abundant experimental data on signal transduction in living organisms, but there is no general mathematical framework to integrate heterogeneous models over the wide range of scales present in most biological systems. This thesis is a new stone in the work aimed to "bridge the scales" in biology

Analysis of behaviours in swarm systems

In nature animal species often exist in groups. We talk of insect swarms, flocks of birds, packs of lions, herds of wildebeest etc. These are characterised by individuals interacting by following their own rules, privy only to local information. Robotic swarms or simulations can be used explore such interactions. Mathematical formulations can be constructed that encode similar ideas and allow us to explore the emergent group behaviours. Some behaviours show characteristics reminiscent of the phenomena of criticality. A bird flock may show near instantaneous collective shifts in direction: velocity changes that appear to correlated over distances much larger individual separations. Here we examine swarm systems inspired by flocks of birds and the role played by criticality. The first system, Particle Swarm Optimisation (PSO), is shown to behave optimally when operating close to criticality. The presence of a critical point in the algorithm’s operation is shown to derive from the swarm’s properties as a random dynamical system. Empirical results demonstrate that the optimality lies on or near this point. A modified PSO algorithm is presented which uses measures of the swarm’s diversity as a feedback signal to adjust the behaviour of the swarm. This achieves a statistically balanced mixture of exploration and exploitation behaviours in the resultant swarm. The problems of stagnation and parameter tuning often encountered in PSO are automatically avoided. The second system, Swarm Chemistry, consists of heterogeneous particles combined with kinetic update rules. It is known that, depending upon the parametric configuration, numerous structures visually reminiscent of biological forms are found in this system. The parameter set discovered here results in a cell-division-like behaviour (in the sense of prokaryotic fission). Extensions to the swarm system produces a swarm that shows repeated cell division. As such, this model demonstrates a behaviour of interest to theories regarding the origin of life

Route navigation inspired by foraging insects: following and finding a route again.

Bertrand O. Route navigation inspired by foraging insects: following and finding a route again. Bielefeld: Universität Bielefeld; 2017.Navigation is one of the most complex behaviours observed in the animal kingdom. A navigating animal needs to learn and recognise the characteristics at certain locations, to decide in which direction to move to reach its destination and to avoid collisions with objects during its journey. Many insects – bees, ants, and wasps – are fascinating navigators, and their behaviour has been scrutinised in great detail over the past century. With their brain weighing only a few milligrammes, these insects have been an amazing source of inspiration for engineers to develop computationally parsimonious and energy-efficient algorithms, and puzzled scientists about how such a tiny animal can navigate efficiently in a complex world. The thesis has been inspired by the stunning navigational skills of foraging insects. One of their skills is the ability to follow a habitual route between two locations. As it will be shown in the thesis, route navigation can arise from simple mechanisms; knowing its overall goal direction and employing a collision avoidance algorithm is sufficient to follow a route. However, the journey along a route of an agent, i.e. a biological or technical system, is not always smooth. The journey may be disrupted suddenly by external factors – such as wind or an impending danger – or by internal sources that lead to navigational errors. The agent will, thus, be at an unknown location away from its habitual route and have to find its route again to complete its journey. I will reveal in the thesis a variety of search strategies that an agent may use to find its route again in a cluttered environment, such as a city or a forest. Since a unique optimal search strategy does not exist, it will be shown that the agent can decide which strategy to follow, assuming it can estimate the distance it plans to travel and the distance it has been displaced from its route. The thesis addresses fundamental questions of navigation by focussing on following and finding a habitual route again. To frame these dilemmas, (1) an overview of navigation will also be given to highlight common fundamental problems faced by any navigating agent, (2) the various degrees of complexity of different strategies to solve navigational tasks and (3) essential aspects of research on insect navigation. Although my modelling approach is inspired by the behaviour of foraging insects, it aims to provide general solutions for any moving agent on how to commute between two locations efficiently