Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search

Due to growing endurance, safety and non-invasivity, small drones can be increasingly experimented in unstructured environments. Their moderate computing power can be assimilated into swarm coordination algorithms, performing tasks in a scalable manner. For this purpose, it is challenging to investigate the use of biologically-inspired mechanisms. In this paper the focus is on the coordination aspects between small drones required to perform target search. We show how this objective can be better achieved by combining stigmergic and flocking behaviors. Stigmergy occurs when a drone senses a potential target, by releasing digital pheromone on its location. Multiple pheromone deposits are aggregated, increasing in intensity, but also diffused, to be propagated to neighborhood, and lastly evaporated, decreasing intensity in time. As a consequence, pheromone intensity creates a spatiotemporal attractive potential field coordinating a swarm of drones to visit a potential target. Flocking occurs when drones are spatially organized into groups, whose members have approximately the same heading, and attempt to remain in range between them, for each group. It is an emergent effect of individual rules based on alignment, separation and cohesion. In this paper, we present a novel and fully decentralized model for target search, and experiment it empirically using a multi-agent simulation platform. The different combination strategies are reviewed, describing their performance on a number of synthetic and real-world scenarios

Development and Validation of a LQR-Based Quadcopter Control Dynamics Simulation Model

5The growing applications involving unmanned aerial vehicles (UAVs) are requiring more advanced control algorithms to improve rotary-wing UAVs’ performance. To preliminarily tune such advanced controllers, an experimental approach could take a long time and also be dangerous for the vehicle and the onboard hardware components. In this paper, a simulation model of a quadcopter is developed and validated by the comparison of simulation results and experimental data collected during flight tests. For this purpose, an open-source flight controller for quadcopter UAVs is developed and a linear quadratic regulator (LQR) controller is implemented as the control strategy. The input physical quantities are experimentally measured; hence, the LQR controller parameters are tuned on the simulation model. The same tuning is proposed on the developed flight controller with satisfactory results. Finally, flight data and simulation results are compared showing a reliable approximation of the experimental data by the model. Because numerous state-of-the-art simulation models are available, but accurately validated ones are not easy to find, the main purpose of this work is to provide a reliable tool to evaluate the performance for this UAV configuration. DOI: 10.1061/(ASCE)AS.1943-5525.0001336. © 2021 American Society of Civil Engineers.partially_openopenAlessandro Minervini; Simone Godio; Giorgio Guglieri; Fabio Dovis; Alfredo BiciMinervini, Alessandro; Godio, Simone; Guglieri, Giorgio; Dovis, Fabio; Bici, Alfred

Autonomous Operation of a Reconfigurable Multi-Robot System for Planetary Space Missions

Reconfigurable robots can physically merge and form new types of composite systems. This ability leads to additional degrees of freedom for robot operations especially when dynamically composed robotic systems offer capabilities that none of the individual systems have. Research in the area of reconfigurable multi-robot systems has mainly been focused on swarm-based robots and thereby to systems with a high degree of modularity but a heavily restricted set of capabilities. In contrast, this thesis deals with heterogeneous robot teams comprising individually capable robots which are also modular and reconfigurable. In particular, the autonomous application of such reconfigurable multi-robot systems to enhance robotic space exploration missions is investigated. Exploiting the flexibility of a reconfigurable multi-robot system requires an appropriate system model and reasoner. Hence, this thesis introduces a special organisation model. This model accounts for the key characteristics of reconfigurable robots which are constrained by the availability and compatibility of hardware interfaces. A newly introduced mapping function between resource structures and functional properties permits to characterise dynamically created agent compositions. Since a combinatorial challenge lies in the identification of feasible and functionally suitable agents, this thesis further suggests bounding strategies to reason efficiently with composite robotic systems. This thesis proposes a mission planning algorithm which permits to exploit the flexibility of reconfigurable multi-robot systems. The implemented planner builds upon the developed organisation model so that multi-robot missions can be specified by high-level functionality constraints which are resolved to suitable combinations of robots. Furthermore, the planner synchronises robot activities over time and characterises plans according to three objectives: efficacy, efficiency and safety. The plannera s evaluation demonstrates an optimization of an exemplary space mission. This research is based on the parallel development of theoretical concepts and practical solutions while working with three reconfigurable multi-robot teams. The operation of a reconfigurable robotic team comes with practical constraints. Therefore, this thesis composes and evaluates an operational infrastructure which can serve as reference implementation. The identification and combination of applicable state-of-the-art technologies result in a distributed and dynamically extensible communication infrastructure which can maintain the properties of reconfigurable multi-robot systems. Field tests covering semi-autonomous and autonomous operation have been performed to characterise multi-robot missions and validate the autonomous control approach for reconfigurable multi-robot systems. The practical evaluation identified critical constraints and design elements for a successful application of reconfigurable multi-robot systems. Furthermore, the experiments point to improvements for the organisation model. This thesis is a wholistic approach to automate reconfigurable multi-robot systems. It identifies theoretical as well as practical challenges and it suggests effective solutions which permit an exploitation of an increased level of flexibility in future robotics missions

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen

Autonomous Operation of a Reconfigurable Multi-Robot System for Planetary Space Missions

Reconfigurable robots can physically merge and form new types of composite systems. This ability leads to additional degrees of freedom for robot operations especially when dynamically composed robotic systems offer capabilities that none of the individual systems have. Research in the area of reconfigurable multi-robot systems has mainly been focused on swarm-based robots and thereby to systems with a high degree of modularity but a heavily restricted set of capabilities. In contrast, this thesis deals with heterogeneous robot teams comprising individually capable robots which are also modular and reconfigurable. In particular, the autonomous application of such reconfigurable multi-robot systems to enhance robotic space exploration missions is investigated. Exploiting the flexibility of a reconfigurable multi-robot system requires an appropriate system model and reasoner. Hence, this thesis introduces a special organisation model. This model accounts for the key characteristics of reconfigurable robots which are constrained by the availability and compatibility of hardware interfaces. A newly introduced mapping function between resource structures and functional properties permits to characterise dynamically created agent compositions. Since a combinatorial challenge lies in the identification of feasible and functionally suitable agents, this thesis further suggests bounding strategies to reason efficiently with composite robotic systems. This thesis proposes a mission planning algorithm which permits to exploit the flexibility of reconfigurable multi-robot systems. The implemented planner builds upon the developed organisation model so that multi-robot missions can be specified by high-level functionality constraints which are resolved to suitable combinations of robots. Furthermore, the planner synchronises robot activities over time and characterises plans according to three objectives: efficacy, efficiency and safety. The plannera s evaluation demonstrates an optimization of an exemplary space mission. This research is based on the parallel development of theoretical concepts and practical solutions while working with three reconfigurable multi-robot teams. The operation of a reconfigurable robotic team comes with practical constraints. Therefore, this thesis composes and evaluates an operational infrastructure which can serve as reference implementation. The identification and combination of applicable state-of-the-art technologies result in a distributed and dynamically extensible communication infrastructure which can maintain the properties of reconfigurable multi-robot systems. Field tests covering semi-autonomous and autonomous operation have been performed to characterise multi-robot missions and validate the autonomous control approach for reconfigurable multi-robot systems. The practical evaluation identified critical constraints and design elements for a successful application of reconfigurable multi-robot systems. Furthermore, the experiments point to improvements for the organisation model. This thesis is a wholistic approach to automate reconfigurable multi-robot systems. It identifies theoretical as well as practical challenges and it suggests effective solutions which permit an exploitation of an increased level of flexibility in future robotics missions

Swarm robotic systems: ypod formation with analysis on scalability and stability

Aquesta tesi se central en la formació d’eixams, on s’estudia el comportament coordinat d’un grup de robots per formar un patró quan s’observa a nivell global. En aquest sentit, la formació de la forma general és un dels problemes actuals en d’intel·ligència d’eixams artificials. En aquesta tesi s’introdueix una nova formació en forma de Y, la qual presenta una gran quantitat d’aplicacions en comparació amb altres tècniques de formació. Per exemple, la formació en Y es pot aplicar com a formació estratègica per totes les escales, presenta facilitat per canviar de forma i grandària a més de resoldre els problemes de redundància, d’autoorganització i autoreparació. L’objectiu principal d’aquesta tesi és aconseguir la formació en Y d’un eixam de robots. La implementació de dita formació únicament s’ha dut a terme mitjançant un entorn de simulació tot i que se han tingut en compte diferents aspectes que es podrien donar en una implementació real. El disseny del control de l’eixam per a diferents eixos s’ha realitzat a partir d’un model capaç de predir el comportament global de l’eixam, de la definició del temps d’establiment i l’aplicació de tècniques de localització de pols. Per controlar l’eixam en forma Y en termes d’orientació i el seu moviment com un bloc, s’han combinat el controlador lineal proposat, amb funcions límit i l’ajust d’alguns paràmetres per simulació. Els paràmetres s’han escollit per la formació desitjada i segons les constants definides per l’usuari. En comparació amb altres treballs, la solució proposta és simple, computacionalment eficient i tant per models d’eixams centralitzats com descentralitzats.Esta tesis se centra en la formación de enjambres, donde se estudia el comportamiento coordinado de un grupo de robots para formar un patrón cuando se observa a nivel global. En este sentido, la formación de la forma general es uno de los problemas actuales en la inteligencia de enjambres artificiales. En esta tesis se introduce una nueva formación en forma de Y, la cual presenta una gran cantidad de aplicaciones en comparación con otras técnicas de formación. Por ejemplo, la formación en Y se puede aplicar como formación estratégica para todas las escalas, presenta facilidad para cambiar de forma y tamaño además de resolver los problemas de redundancia, de auto-organización y auto-reparación. El objetivo principal de esta tesis es conseguir la formación en Y de un enjambre de robots. La implementación de dicha formación se ha llevado a cabo únicamente mediante un entorno de simulación aunque se han tenido en cuenta diferentes aspectos que se podrían dar en una implementación real. El diseño del control del enjambre para diferentes ejes se ha realizado a partir de un modelo capaz de predecir el comportamiento global del enjambre, de la definición del tiempo de establecimiento y la aplicación de técnicas de localización de polos. Para controlar el enjambre en forma de Y en términos de orientación y movimientos del enjambre como un bloque, se han combinado el controlador lineal propuesto, funciones límite y el ajuste de algunos parámetros por simulación. Los parámetros se han escogido para la formación deseada y según las constantes definidas por el usuario. En comparación con otros trabajos, la solución propuesta es simple, computacionalmente eficiente, y tanto para modelos de enjambres centralizados como descentralizados.The context of this work is the innovative young filed of swarm robotics. Particularly, in this thesis focused on swarm formation, which is important in swarm robotics too since coordinated behaviour of a group of robots to form a pattern when viewed globally. In this regard, global shape formation is one of the ongoing problems in artificial swarm intelligence. In nature, it is performed for various purposes, and search and rescue swarms could be used in disaster areas .In robotics phenomena, there exist various shape formations in the literature, but in this thesis, introduced new shape formation Y-Pod, which has vast applications compare to other formation techniques. In the discussion of our research journey, me and my supervisor discussed about various shape formations but finally exploit new shape formation Y-Pod and when we think about it, arise some questions ,why Y-Pod swarm formation and what it will serve, so in our casual discussion some important advantages are identified, those are : The Y-Pod can be utilized for formation strategy on all scales, Global shape formation, when viewed globally, Changes shapes, Easy to expand, overcome the redundancy problems and Self-organized and self-repair problems. The main objective of the proposed approach is to form a Y-pod formation of swarm robots. As well as we keep in our mind for real robot performance task, but the original work is delivered in simulation based environment only. Several parameters that significantly define the resulting behavior. We have proposed system equilibrium parameters with settling time and pole based problems, to control the swarm system in various axis an accurate model will predict the global behavior of the Y-Pod swarm formation based on the mathematical identified parameters. The proposed linear controller, limiting functions and simulation tuned parameters are combined to control Y-Pod swarm formation in terms of orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. This approach compared to others, is simple, computationally efficient, scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms