Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Formation and organisation in robot swarms.

A swarm is defined as a large and independent collection of heterogeneous or homogeneous agents operating in a common environment and seemingly acting in a coherent and coordinated manner. Swarm architectures promote decentralisation and self-organisation which often leads to emergent behaviour. The emergent behaviour of the swarm results from the interactions of the swarm with its environment (or fellow agents), but not as a direct result of design. The creation of artificially simulated swarms or practical robot swarms has become an interesting topic of research in the last decade. Even though many studies have been undertaken using a practical approach to swarm construction, there are still many problems need to be addressed. Such problems include the problem of how to control very simple agents to form patterns; the problem of how an attractor will affect flocking behaviour; and the problem of bridging formation of multiple agents in connecting multiple locations. The central goal of this thesis is to develop early novel theories and algorithms to support swarm robots in. pattern formation tasks. To achieve this, appropriate tools for understanding how to model, design and control individual units have to be developed. This thesis consists of three independent pieces of research work that address the problem of pattern formation of robot swarms in both a centralised and a decentralised way.The first research contribution proposes algorithms of line formation and cluster formation in a decentralised way for relatively simple homogenous agents with very little memory, limited sensing capabilities and processing power. This research utilises the Finite State Machine approach.In the second research contribution, by extending Wilensky's (1999) work on flocking, three different movement models are modelled by changing the maximum viewing angle each agent possesses during the course of changing its direction. An object which releases an artificial potential field is then introduced in the centre of the arena and the behaviours of the collective movement model are studied.The third research contribution studies the complex formation of agents in a task that requires a formation of agents between two locations. This novel research proposes the use Of L-Systems that are evolved using genetic algorithms so that more complex pattern formations can be represented and achieved. Agents will need to have the ability to interpret short strings of rules that form the basic DNA of the formation

Guided Self-Organizing Particle Systems for Basic Problem Solving

In recent years researchers have shown increasing interest in swarm intelligence as a promising approach to adaptive distributed problem solving. Swarm intelligence consists of techniques inspired by nature, especially social insects and aggregations of animals, and even human interactions. They are based on self-organization (a system's overall behavior emerges from the local interactions among its relatively simple components) and are often decentralized and massively distributed. Particle systems are an approach to swarm intelligence that focus on collective movements, and have been used successfully for applications such as computer animation in graphics and control of movements of autonomous robotic vehicle teams. However, particle system techniques have not been applied substantially to problem solving beyond merely collective navigational tasks. In this dissertation, I present an extension to particle systems that incorporates top-down, high-level control to self-organizing mobile agents, thereby guiding the self-organizing process and making it possible for particle systems to undertake problem solving directed by goal-oriented behavior while retaining their decentralized, local nature. This extended particle system approach is critically evaluated through three experimental studies that are adapted from well-known problems in multi-agent systems: search and collect, cooperative transport and logistics. The results provide evidence that extended particle systems are capable of exhibiting behavior important for distributed problem solving, such as cooperative sensing, division of labor, sharing of information, and developing global strategies through local interactions. They also show that aggregated movements can be utilized to create coordination at different levels and phases of the performance of a task, whether those include navigation or not, making extended particle systems a useful tool in the construction of adaptive distributed systems

Distributed Control of a Swarm of Autonomous Unmanned Aerial Vehicles

With the increasing use of Unmanned Aerial Vehicles (UAV)s military operations, there is a growing need to develop new methods of control and navigation for these vehicles. This investigation proposes the use of an adaptive swarming algorithm that utilizes local state information to influence the overall behavior of each individual agent in the swarm based upon the agent\u27s current position in the battlespace. In order to investigate the ability of this algorithm to control UAVs in a cooperative manner, a swarm architecture is developed that allows for on-line modification of basic rules. Adaptation is achieved by using a set of behavior coefficients that define the weight at which each of four basic rules is asserted in an individual based upon local state information. An Evolutionary Strategy (ES) is employed to create initial metrics of behavior coefficients. Using this technique, three distinct emergent swarm behaviors are evolved, and each behavior is investigated in terms of the ability of the adaptive swarming algorithm to achieve the desired emergent behavior by modifying the simple rules of each agent. Finally, each of the three behaviors is analyzed visually using a graphical representation of the simulation, and numerically, using a set of metrics developed for this investigation

Outdoor operations of multiple quadrotors in windy environment

Coordinated multiple small unmanned aerial vehicles (sUAVs) offer several advantages over a single sUAV platform. These advantages include improved task efficiency, reduced task completion time, improved fault tolerance, and higher task flexibility. However, their deployment in an outdoor environment is challenging due to the presence of wind gusts. The coordinated motion of a multi-sUAV system in the presence of wind disturbances is a challenging problem when considering collision avoidance (safety), scalability, and communication connectivity. Performing wind-agnostic motion planning for sUAVs may produce a sizeable cross-track error if the wind on the planned route leads to actuator saturation. In a multi-sUAV system, each sUAV has to locally counter the wind disturbance while maintaining the safety of the system. Such continuous manipulation of the control effort for multiple sUAVs under uncertain environmental conditions is computationally taxing and can lead to reduced efficiency and safety concerns. Additionally, modern day sUAV systems are susceptible to cyberattacks due to their use of commercial wireless communication infrastructure. This dissertation aims to address these multi-faceted challenges related to the operation of outdoor rotor-based multi-sUAV systems. A comprehensive review of four representative techniques to measure and estimate wind speed and direction using rotor-based sUAVs is discussed. After developing a clear understanding of the role wind gusts play in quadrotor motion, two decentralized motion planners for a multi-quadrotor system are implemented and experimentally evaluated in the presence of wind disturbances. The first planner is rooted in the reinforcement learning (RL) technique of state-action-reward-state-action (SARSA) to provide generalized path plans in the presence of wind disturbances. While this planner provides feasible trajectories for the quadrotors, it does not provide guarantees of collision avoidance. The second planner implements a receding horizon (RH) mixed-integer nonlinear programming (MINLP) model that is integrated with control barrier functions (CBFs) to guarantee collision-free transit of the multiple quadrotors in the presence of wind disturbances. Finally, a novel communication protocol using Ethereum blockchain-based smart contracts is presented to address the challenge of secure wireless communication. The U.S. sUAV market is expected to be worth $92 Billion by 2030. The Association for Unmanned Vehicle Systems International (AUVSI) noted in its seminal economic report that UAVs would be responsible for creating 100,000 jobs by 2025 in the U.S. The rapid proliferation of drone technology in various applications has led to an increasing need for professionals skilled in sUAV piloting, designing, fabricating, repairing, and programming. Engineering educators have recognized this demand for certified sUAV professionals. This dissertation aims to address this growing sUAV-market need by evaluating two active learning-based instructional approaches designed for undergraduate sUAV education. The two approaches leverages the interactive-constructive-active-passive (ICAP) framework of engagement and explores the use of Competition based Learning (CBL) and Project based Learning (PBL). The CBL approach is implemented through a drone building and piloting competition that featured 97 students from undergraduate and graduate programs at NJIT. The competition focused on 1) drone assembly, testing, and validation using commercial off-the-shelf (COTS) parts, 2) simulation of drone flight missions, and 3) manual and semi-autonomous drone piloting were implemented. The effective student learning experience from this competition served as the basis of a new undergraduate course on drone science fundamentals at NJIT. This undergraduate course focused on the three foundational pillars of drone careers: 1) drone programming using Python, 2) designing and fabricating drones using Computer-Aided Design (CAD) and rapid prototyping, and 3) the US Federal Aviation Administration (FAA) Part 107 Commercial small Unmanned Aerial Vehicles (sUAVs) pilot test. Multiple assessment methods are applied to examine the students’ gains in sUAV skills and knowledge and student attitudes towards an active learning-based approach for sUAV education. The use of active learning techniques to address these challenges lead to meaningful student engagement and positive gains in the learning outcomes as indicated by quantitative and qualitative assessments

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