Sector search strategies for odor trail tracking

Ants, mice, and dogs often use surface-bound scent trails to establish navigation routes or to find food and mates, yet their tracking strategies remain poorly understood. Chemotaxis-based strategies cannot explain casting, a characteristic sequence of wide oscillations with increasing amplitude performed upon sustained loss of contact with the trail. We propose that tracking animals have an intrinsic, geometric notion of continuity, allowing them to exploit past contacts with the trail to form an estimate of where it is headed. This estimate and its uncertainty form an angular sector, and the emergent search patterns resemble a “sector search.” Reinforcement learning agents trained to execute a sector search recapitulate the various phases of experimentally observed tracking behavior. We use ideas from polymer physics to formulate a statistical description of trails and show that search geometry imposes basic limits on how quickly animals can track trails. By formulating trail tracking as a Bellman-type sequential optimization problem, we quantify the geometric elements of optimal sector search strategy, effectively explaining why and when casting is necessary. We propose a set of experiments to infer how tracking animals acquire, integrate, and respond to past information on the tracked trail. More generally, we define navigational strategies relevant for animals and biomimetic robots and formulate trail tracking as a behavioral paradigm for learning, memory, and planning.

Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking

A general control system tracking learning framework is proposed, by which an optimal learned tracking behavior called ‘primitive’ is extrapolated to new unseen trajectories without requiring relearning. This is considered intelligent behavior and strongly related to the neuro-motor cognitive control of biological (human-like) systems that deliver suboptimal executions for tasks outside of their current knowledge base, by using previously memorized experience. However, biological systems do not solve explicit mathematical equations for solving learning and prediction tasks. This stimulates the proposed hierarchical cognitive-like learning framework, based on state-of-the-art model-free control: (1) at the low-level L1, an approximated iterative Value Iteration for linearizing the closed-loop system (CLS) behavior by a linear reference model output tracking is first employed; (2) an experiment-driven Iterative Learning Control (EDILC) applied to the CLS from the reference input to the controlled output learns simple tracking tasks called ‘primitives’ in the secondary L2 level, and (3) the tertiary level L3 extrapolates the primitives’ optimal tracking behavior to new tracking tasks without trial-based relearning. The learning framework relies only on input-output system data to build a virtual state space representation of the underlying controlled system that is assumed to be observable. It has been shown to be effective by experimental validation on a representative, coupled, nonlinear, multivariable real-world system. Able to cope with new unseen scenarios in an optimal fashion, the hierarchical learning framework is an advance toward cognitive control systems.

Prescribed performance-based adaptive sliding mode control for the autopilot design of missile with lateral jets

A novel prescribed performance-based adaptive sliding mode control is investigated for the autopilot design of missile with lateral reaction jets. An integral sliding mode surface is designed for a class of nonlinear systems such that the prescribed output-tracking behavior is incorporated into the sliding mode dynamics. An adaptive algorithm is developed using the concept of equivalent control to attenuate the chattering effect. Then, the method is applied to the autopilot design where the sliding mode control law is allocated to two sets of actuators according to their respective characteristics. The proposed integral sliding surface guarantees that the missile output can track the given reference command with the prescribed performance indices from the very beginning of the time. Moreover, the adaptation laws allow the reduction of the jets consumption. Several simulations conducted at different set-points show the efficacy of the proposed methods.

Stability of Chemically Degraded Friction Reducers and Their Relationship to Regain Conductivity

Previously, it was shown that zeta potential could be used as a metric to determine friction reducer (FR) performance. Specifically, the extent of and how quickly the FR reaches peak friction reduction in source water. A correlation postulated from the previous work is zeta potentials relationship to an FR's stability during mechanical or chemical degradation. In other words, can zeta potential be used as a metric to determine the extent of polymer breaking and can this relationship be translated to regained conductivity? This paper describes a laboratory study of zeta potential measurements to track breaker reaction rates, stability of broken polymer dispersions, and the relationship between chemical degradation of FRs and regained conductivity. The approach of this investigation involves measuring zeta potential of frac fluids formulated using anionic and cationic FRs with varying types and concentrations of breakers at different temperatures and times. These metrics are then correlated with regain conductivity. A quantitative relationship exists between zeta potential, fluid rheology, and regain conductivity. Zeta potential evaluation of degraded FR's in frac fluids correlate to performance in regain conductivity testing. These measurements can expedite the selection of chemical breakers with respect to performance. Zeta potential measurements of degraded FR are indicative of broken FR dispersion stability which has impact on regain conductivity. Tracking behavior of cationic FR's using zeta potential reveals the materials can become anionic with time and temperature and become susceptible to agglomeration with iron. Zeta potential measurements can be used during a chemical breaker selection process as a viable supplement to industry standard tests for assessing the comparative effectiveness of chemical breakers in frac fluids.

Optimized intelligent data management framework for a cyber-physical system for computational applications

Data management is one obstacle in the production sector to be reconfigured and adapted through optimum parameterization in industry cyber-physical systems. This paper presents an intelligent data management framework for a cyber-physical system (IDMF-CPS) with machine-learning methods. A training approach based on two enhanced training procedures, running concurrently to upgrade the processing and communication strategy and the predictive models, is contained in the suggested reasoning modules. The method described spreads computational and analytical engines in several levels and autonomous modules to enhance intelligence and autonomy for controlling and tracking behavior on the work floor. The appropriateness of the suggested solution is supported by rapid reaction time and a suitable establishment of optimal operating variables for the required quality during macro- and micro-operations.

Statistical Tracking Behavior Analysis for the Affine Projection Algorithm Based on Direction Error

Under the condition that the step size is less than one, a statistical tracking behavior analysis for the affine projection algorithm based on direction error is discussed. When the unknown true weight vector is modeled by the stochastic walk model, the mean weight error is derived under the four assumptions based on the deterministic recursive equation. Furthermore, the statistical tracking behavior of the steady state is analyzed for the affine projection algorithm based on direction error. Simulation analysis is shown to suppniort the mathematical results.

Using a Fully Expressive Avatar to Collaborate in Virtual Reality: Evaluation of Task Performance, Presence, and Attraction

Avatar-mediated collaboration in virtual environments is becoming more and more prevalent. However, current consumer systems are not suited to fully replicate real-world nonverbal communication. We present a novel avatar system for collaboration in virtual reality, which supports high levels of nonverbal expression by tracking behavior such as body movement, hand gesture, and facial expression. The system was built using camera tracking technology only. Therefore, in contrast to many other high-level tracking systems, it does not require users to wear additional trackers on their bodies. We compared our highly expressive system with a consumer setup extended with two body-worn trackers in a dyadic study. We investigated users’ performance, such as completion time and accuracy, as well as the presence and interpersonal attraction in a virtual charades game using an asymmetric control scheme. The results show that participants interacting with highly expressive avatars felt more social presence and attraction and exhibited better task performance than those interacting with partners represented using low-expressive avatars. Hence, we conclude that virtual reality avatar systems benefit from a higher level of nonverbal expressiveness, which can be achieved without additional body-worn trackers.

Sector search strategies for odor trail tracking

Terrestrial animals such as ants, mice and dogs often use surface-bound scent trails to establish navigation routes or to find food and mates, yet their tracking strategies are poorly understood. Tracking behavior features zig-zagging paths with animals often staying in close contact with the trail. Upon sustained loss of contact, animals execute a characteristic sequence of sweeping “casts” – wide oscillations with increasing amplitude. Here, we provide a unified description of trail-tracking behavior by introducing an optimization framework where animals search in the angular sector defined by their estimate of the trail’s heading and its uncertainty.In silicoexperiments using reinforcement learning based on this hypothesis recapitulate experimentally observed tracking patterns. We show that search geometry imposes limits on the tracking speed, and quantify its dependence on trail statistics and memory of past contacts. By formulating trail-tracking as a Bellman-type sequential optimization problem, we quantify the basic geometric elements of optimal sector search strategy, effectively explaining why and when casting is necessary. We propose a set of experiments to infer how tracking animals acquire, integrate and respond to past information on the tracked trail. More generally, we define navigational strategies relevant for animals and bio-mimetic robots, and formulate trail-tracking as a novel behavioral paradigm for learning, memory and planning.

Olfactory tracking strategies in a neotropical fruit bat

ABSTRACTMany studies have characterized olfactory-tracking behaviors in animals, and it has been proposed that search strategies may be generalizable across a wide range of species. Olfaction is important for fruit- and nectar-feeding bats, but it is uncertain whether existing olfactory search models can predict the strategies of flying mammals that emit echolocation pulses through their nose. Quantitative assessments of how well echolocating bats track and localize odor sources are lacking, so we developed a behavioral assay to characterize the olfactory detection and tracking behavior of crawling northern yellow-shouldered bats (Sturnira parvidens), a common neotropical frugivore. Trained bats were presented with a choice between control and banana-odor-infused solutions in a series of experiments that confirmed that bats are able to locate a reward based on odor cues alone and examined the effect of odor concentration on olfactory search behaviors. Decision distance (the distance from which bats made their change in direction before directly approaching the target) was distinctly bimodal, with an observed peak that coincided with an inflection point in the odor concentration gradient. We observed two main search patterns that are consistent with both serial sampling and learned route-following strategies. These results support the hypothesis that bats can combine klinotaxis with spatial awareness of experimental conditions to locate odor sources, similar to terrestrial mammals. Contrary to existing models, bats did not display prominent head-scanning behaviors during their final approach, which may be due to constraints of nasal-emitted biosonar for orientation.