Phototaxic foraging of the archaepaddler, a hypothetical deep-sea species

An autonomous agent (animat, hypothetical animal), called the (archae) paddler, is simulated in sufficient detail to regard its simulated aquatic locomotion (paddling) as physically possible. The paddler is supposed to be a model of an animal that might exist, although it is perfectly possible to view it as a model of a robot that might be built. The agent is assumed to navigate in a simulated deep-sea environment, where it hunts autoluminescent prey. It uses a biologically inspired phototaxic foraging-strategy, while paddling in a layer just above the bottom. The advantage of this living space is that the navigation problem is essentially two-dimensional. Moreover, the deep-sea environment is physically simple (and hence easier to simulate): no significant currents, constant temperature, completely dark. A foraging performance metric is developed that circumvents the necessity to solve the travelling salesman problem. A parametric simulation study then quantifies the influence of habitat factors, such as the density of prey, and the body-geometry (e.g. placement, direction and directional selectivity of the eyes) on foraging success. Adequate performance proves to require a specific body-% geometry adapted to the habitat characteristics. In general performance degrades smoothly for modest changes of the geometric and habitat parameters, indicating that we work in a stable region of 'design space'. The parameters have to strike a compromise between on the one hand the ability to 'fixate' an attractive target, and on the other hand to 'see' as many targets at the same time as possible. One important conclusion is that simple reflex-based navigation can be surprisingly efficient. In the second place, performance in a global task (foraging) depends strongly on local parameters like visual direction-tuning, position of the eyes and paddles, etc. Behaviour and habitat 'mould' the body, and the body-geometry strongly influences performance. The resulting platform enables further testing of foraging strategies, or vision and locomotion theories stemming either from biology or from robotics

A Model of Movement Coordinates in Motor Cortex: Posture-Dependent Changes in the Gain and Direction of Single Cell Tuning Curves

Central to the problem of elucidating the cortical mechanisms that mediate movement behavior is an investigation of the coordinate systems by which movement variables are encoded in the firing rates of individual motor cortical neurons. In the last decade, neurophysiologists have probed how the preferred direction of an individual motor cortical cell (as determined by a center-out task) will change with posture because such changes are useful for inferring underlying cordinates. However, while the importance of shifts in preferred direction is well-known and widely accepted, posture-dependent changes in the depth of modulation of a cell's tuning curve, i.e. gain changes, have not been similarly identified as a means of coordinate inference. This paper develops a vector field framework which, by viewing the preferred direction and the gain of a cell's tuning curve as dual components of a unitary response vector, can compute how each aspect of cell response covaries with posture as a function of the coordinate system in which a given cell is hypothesized to encode its movement information. This integrated approach leads to a model of motor cortical cell activity that codifies the following four observations: 1) cell activity correlates with hand movement direction, 2) cell activity correlates with hand movement speed, 3) preferred directions vary with posture, and 4) the modulation depth of tuning curves varies with posture. Finally, the model suggests general methods for testing coordinate hypotheses at the single cell level and example protocols arc simulated for three possible coordinate systems: Cartesian spatial, shoulder-centered, and joint angle.Defense Advanced Research Projects Agency (N00014-92-J-4015); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-90-00530, IRI-97-20333); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-94-l-0940, N00014-95-1-0657)

How simple rules determine pedestrian behavior and crowd disasters

With the increasing size and frequency of mass events, the study of crowd disasters and the simulation of pedestrian flows have become important research areas. Yet, even successful modeling approaches such as those inspired by Newtonian force models are still not fully consistent with empirical observations and are sometimes hard to calibrate. Here, a novel cognitive science approach is proposed, which is based on behavioral heuristics. We suggest that, guided by visual information, namely the distance of obstructions in candidate lines of sight, pedestrians apply two simple cognitive procedures to adapt their walking speeds and directions. While simpler than previous approaches, this model predicts individual trajectories and collective patterns of motion in good quantitative agreement with a large variety of empirical and experimental data. This includes the emergence of self-organization phenomena, such as the spontaneous formation of unidirectional lanes or stop-and-go waves. Moreover, the combination of pedestrian heuristics with body collisions generates crowd turbulence at extreme densities-a phenomenon that has been observed during recent crowd disasters. By proposing an integrated treatment of simultaneous interactions between multiple individuals, our approach overcomes limitations of current physics-inspired pair interaction models. Understanding crowd dynamics through cognitive heuristics is therefore not only crucial for a better preparation of safe mass events. It also clears the way for a more realistic modeling of collective social behaviors, in particular of human crowds and biological swarms. Furthermore, our behavioral heuristics may serve to improve the navigation of autonomous robots.Comment: Article accepted for publication in PNA

Directional Tuning Curves, Elementary Movement Detectors, and the Estimation of the Direction of Visual Movement

Both the insect brain and the vertebrate retina detect visual movement with neurons having broad, cosine-shaped directional tuning curves oriented in either of two perpendicular directions. This article shows that this arrangement can lead to isotropic estimates of the direction of movement: for any direction the estimate is unbiased (no systematic errors) and equally accurate (constant random errors). A simple and robust computational scheme is presented that accounts for the directional tuning curves as measured in movement sensitive neurons in the blowfly. The scheme includes movement detectors of various spans, and predicts several phenomena of movement perception in man.

The Fundamental Diagram of Pedestrian Movement Revisited

The empirical relation between density and velocity of pedestrian movement is not completely analyzed, particularly with regard to the `microscopic' causes which determine the relation at medium and high densities. The simplest system for the investigation of this dependency is the normal movement of pedestrians along a line (single-file movement). This article presents experimental results for this system under laboratory conditions and discusses the following observations: The data show a linear relation between the velocity and the inverse of the density, which can be regarded as the required length of one pedestrian to move. Furthermore we compare the results for the single-file movement with literature data for the movement in a plane. This comparison shows an unexpected conformance between the fundamental diagrams, indicating that lateral interference has negligible influence on the velocity-density relation at the density domain $1 m^{-2}<\rho<5 m^{-2}$. In addition we test a procedure for automatic recording of pedestrian flow characteristics. We present preliminary results on measurement range and accuracy of this method.Comment: 13 pages, 9 figure

Stacking-based visualization of trajectory attribute data

Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well

GPU accelerated Nature Inspired Methods for Modelling Large Scale Bi-Directional Pedestrian Movement

Pedestrian movement, although ubiquitous and well-studied, is still not that well understood due to the complicating nature of the embedded social dynamics. Interest among researchers in simulating pedestrian movement and interactions has grown significantly in part due to increased computational and visualization capabilities afforded by high power computing. Different approaches have been adopted to simulate pedestrian movement under various circumstances and interactions. In the present work, bi-directional crowd movement is simulated where an equal numbers of individuals try to reach the opposite sides of an environment. Two movement methods are considered. First a Least Effort Model (LEM) is investigated where agents try to take an optimal path with as minimal changes from their intended path as possible. Following this, a modified form of Ant Colony Optimization (ACO) is proposed, where individuals are guided by a goal of reaching the other side in a least effort mode as well as a pheromone trail left by predecessors. The basic idea is to increase agent interaction, thereby more closely reflecting a real world scenario. The methodology utilizes Graphics Processing Units (GPUs) for general purpose computing using the CUDA platform. Because of the inherent parallel properties associated with pedestrian movement such as proximate interactions of individuals on a 2D grid, GPUs are well suited. The main feature of the implementation undertaken here is that the parallelism is data driven. The data driven implementation leads to a speedup up to 18x compared to its sequential counterpart running on a single threaded CPU. The numbers of pedestrians considered in the model ranged from 2K to 100K representing numbers typical of mass gathering events. A detailed discussion addresses implementation challenges faced and averted