Gábor Vásárhelyi

Outdoor flocking and formation flight with autonomous aerial robots

We present the first decentralized multi-copter flock that performs stable autonomous outdoor flight with up to 10 flying agents. By decentralized and autonomous we mean that all members navigate themselves based on the dynamic information received from other robots in the vicinity. We do not use central data processing or control; instead, all the necessary computations are carried out by miniature on-board computers. The only global information the system exploits is from GPS receivers, while the units use wireless modules to share this positional information with other flock members locally. Collective behavior is based on a decentralized control framework with bio-inspiration from statistical physical modelling of animal swarms. In addition, the model is optimized for stable group flight even in a noisy, windy, delayed and error-prone environment. Using this framework we successfully implemented several fundamental collective flight tasks with up to 10 units: i) we achieved self-propelled flocking in a bounded area with self-organized object avoidance capabilities and ii) performed collective target tracking with stable formation flights (grid, rotating ring, straight line). With realistic numerical simulations we demonstrated that the local broadcast-type communication and the decentralized autonomous control method allows for the scalability of the model for much larger flocks.

Context-dependent hierarchies in pigeons

Hierarchical organization is widespread in the societies of humans and other animals, both in social structure and in decision-making contexts. In the case of collective motion, the majority of case studies report that dominant individuals lead group movements, in agreement with the common conflation of the terms “dominance” and “leadership.” From a theoretical perspective, if social relationships influence interactions during collective motion, then social structure could also affect leadership in large, swarm-like groups, such as fish shoals and bird flocks. Here we use computer-vision–based methods and miniature GPS tracking to study, respectively, social dominance and in-flight leader–follower relations in pigeons. In both types of behavior we find hierarchically structured networks of directed interactions. However, instead of being conflated, dominance and leadership hierarchies are completely independent of each other. Although dominance is an important aspect of variation among pigeons, correlated with aggression and access to food, our results imply that the stable leadership hierarchies in the air must be based on a different set of individual competences. In addition to confirming the existence of independent and context-specific hierarchies in pigeons, we succeed in setting out a robust, scalable method for the automated analysis of dominance relationships, and thus of social structure, applicable to many species. Our results, as well as our methods, will help to incorporate the broader context of animal social organization into the study of collective behavior.

Flocking algorithm for autonomous flying robots

Animal swarms displaying a variety of typical flocking patterns would not exist without the underlying safe, optimal and stable dynamics of the individuals. The emergence of these universal patterns can be efficiently reconstructed with agent-based models. If we want to reproduce these patterns with artificial systems, such as autonomous aerial robots, agent-based models can also be used in their control algorithms. However, finding the proper algorithms and thus understanding the essential characteristics of the emergent collective behaviour requires thorough and realistic modeling of the robot and also the environment. In this paper, we first present an abstract mathematical model of an autonomous flying robot. The model takes into account several realistic features, such as time delay and locality of communication, inaccuracy of the on-board sensors and inertial effects. We present two decentralized control algorithms. One is based on a simple self-propelled flocking model of animal collective motion, the other is a collective target tracking algorithm. Both algorithms contain a viscous friction-like term, which aligns the velocities of neighbouring agents parallel to each other. We show that this term can be essential for reducing the inherent instabilities of such a noisy and delayed realistic system. We discuss simulation results on the stability of the control algorithms, and perform real experiments to show the applicability of the algorithms on a group of autonomous quadcopters. In our case, bio-inspiration works in two ways. On the one hand, the whole idea of trying to build and control a swarm of robots comes from the observation that birds tend to flock to optimize their behaviour as a group. On the other hand, by using a realistic simulation framework and studying the group behaviour of autonomous robots we can learn about the major factors influencing the flight of bird flocks.

Characterization of an Integrable Single-Crystalline 3-D Tactile Sensor

Porous-Si-micromachining technique was used for the formation of single-crystalline force-sensor elements, capable of resolving the three vector components of the loading force. Similar structures presented so far are created from deposited polycrystalline Si resistors embedded in multilayered SiO2/Si3N4 membranes, using surface micromachining technique for a cavity formation. In this paper, the authors implanted four piezoresistors in an n-type-perforated membrane, having their reference pairs on the substrate in order to form four half bridges for the transduction of the mechanical stress. They successfully combined the HF-based porous-Si process with conventional doping and Al metallization, thereby offering the possibility of integration with readout and amplifying electronics. The 300times300 mum2 membrane size allows for the formation of large tactile arrays using single-crystalline-sensing elements of superior mechanical properties. They used the finite-element method for modeling the stress distribution in the sensor, and verified the results with real measurements. Finally, they covered the sensors with different elastic silicon-rubber layers, and measured the sensors altered properties. They used continuum mechanics to describe the behavior of the rubber layer

Identification of Behaviour in Freely Moving Dogs (Canis familiaris) Using Inertial Sensors

Monitoring and describing the physical movements and body postures of animals is one of the most fundamental tasks of ethology. The more precise the observations are the more sophisticated the interpretations can be about the biology of a certain individual or species. Animal-borne data loggers have recently contributed much to the collection of motion-data from individuals, however, the problem of translating these measurements to distinct behavioural categories to create an ethogram is not overcome yet. The objective of the present study was to develop a “behaviour tracker”: a system composed of a multiple sensor data-logger device (with a tri-axial accelerometer and a tri-axial gyroscope) and a supervised learning algorithm as means of automated identification of the behaviour of freely moving dogs. We collected parallel sensor measurements and video recordings of each of our subjects (Belgian Malinois, N=12; Labrador Retrievers, N=12) that were guided through a predetermined series of standard activities. Seven behavioural categories (lay, sit, stand, walk, trot, gallop, canter) were pre-defined and each video recording was tagged accordingly. Evaluation of the measurements was performed by support vector machine (SVM) classification. During the analysis we used different combinations of independent measurements for training and validation (belonging to the same or different individuals or using different training data size) to determine the robustness of the application. We reached an overall accuracy of above 90% perfect identification of all the defined seven categories of behaviour when both training and validation data belonged to the same individual, and over 80% perfect recognition rate using a generalized training data set of multiple subjects. Our results indicate that the present method provides a good model for an easily applicable, fast, automatic behaviour classification system that can be trained with arbitrary motion patterns and potentially be applied to a wide range of species and situations.

Patterns, transitions and the role of leaders in the collective dynamics of a simple robotic flock

We have developed an experimental setup of very simple self-propelled robots to observe collective motion emerging as a result of inelastic collisions only. A circular pool and commercial RC boats were the basis of our first setup, where we demonstrated that jamming, clustering, disordered and ordered motion are all present in such a simple experiment and showed that the noise level has a fundamental role in the generation of collective dynamics. Critical noise ranges and the transition characteristics between the different collective patterns were also examined. In our second experiment we used a real-time tracking system and a few steerable model boats to introduce intelligent leaders into the flock. We demonstrated that even a very small portion of guiding members can determine group direction and enhance ordering through inelastic collisions. We also showed that noise can facilitate and speed up ordering with leaders. Our work was extended with an agent-based simulation model, too, and close similarity between real and simulation results was observed. The simulation results show clear statistical evidence of three states and negative correlation between density and ordered motion due to the onset of jamming. Our experiments confirm the different theoretical studies and simulation results in the literature on the subject of collision-based, noise-dependent and leader-driven self-propelled particle systems.

Three dimensional single crystalline force sensor by porous Si micromachining

A porous Si micromachining technique was used for the formation of single crystalline force sensor elements, capable of resolving the three vectorial components of the load. Similar structures presented so far, are formed from deposited polycrystalline Si resistors embedded in multilayered SiO/sub 2//Si/sub 3/N/sub 4/ membranes, using a surface micromachining technique for cavity formation. In the present work, in the n-type perforated membrane, four implanted piezoresistors were fabricated with their reference pairs on the substrate, in order to form 4 half-bridges for the transduction of the mechanical stress. The HF based porous Si process was successfully combined with conventional doping and Al metallization, thereby offering a possible integration of read-out and amplifying electronics. The 300/spl times/300 /spl mu/m/sup 2/ membrane size allows the formation of large area arrays for tactile sensing using single crystalline sensing elements of superior mechanical properties.

Self-organized UAV traffic in realistic environments

We investigated different dense multirotor UAV traffic simulation scenarios in open 2D and 3D space, under realistic environments with the presence of sensor noise, communication delay, limited communication range, limited sensor update rate and finite inertia. We implemented two fundamental self-organized algorithms: one with constant direction and one with constant velocity preference to reach a desired target. We performed evolutionary optimization on both algorithms in five basic traffic scenarios and tested the optimized algorithms under different vehicle densities. We provide optimal algorithm and parameter selection criteria and compare the maximal flux and collision risk of each solution and situation. We found that i) different scenarios and densities require different algorithmic approaches, i.e., UAVs have to behave differently in sparse and dense environments or when they have common or different targets; ii) a slower-is-faster effect is implicitly present in our models, i.e., the maximal flux is achieved at densities where the average speed is far from maximal; iii) communication delay is the most severe destabilizing environmental condition that has a fundamental effect on performance and needs to be taken into account when designing algorithms to be used in real life.

Integrated tactile system for dynamic 3D contact force mapping

We present the first integrated tactile system that is based on dynamic, spatially distributed, three-axial contact force data. Compared to general pressure mapping systems, our devices measure not only one, but all three components of contact forces (normal and shear) with up to 64 independent micromachined force sensing elements integrated on a single chip. The spatially distributed shear force sensing adds new dimensions and directions to tactile data analysis, including pre-slip detection, enhanced robotic grasping or high quality tactile texture classification. In this paper we briefly describe the components of the novel system: the sensor arrays, the data acquisition methods and the data analysis software. We also present two example applications that exploit the advantages of real time three-dimensional contact force mapping.

Application of artificial neural network in holographic and speckle interferometry

Applications of artificial neural network in holographic interferometry and speckle metrology have been presented. Back-propagation neural network has been used for defect detection. Self-organizing networks has been successfully applied to determine interferometric fringe centerlines.