Secure indoor navigation and operation of mobile robots

In future work environments, robots will navigate and work side by side to humans. This raises big challenges related to the safety of these robots. In this Dissertation, three tasks have been realized: 1) implementing a localization and navigation system based on StarGazer sensor and Kalman filter; 2) realizing a human-robot interaction system using Kinect sensor and BPNN and SVM models to define the gestures and 3) a new collision avoidance system is realized. The system works on generating the collision-free paths based on the interaction between the human and the robot.In zukünftigen Arbeitsumgebungen werden Roboter navigieren nebeneinander an Menschen. Das wirft Herausforderungen im Zusammenhang mit der Sicherheit dieser Roboter auf. In dieser Dissertation drei Aufgaben realisiert: 1. Implementierung eines Lokalisierungs und Navigationssystem basierend auf Kalman Filter: 2. Realisierung eines Mensch-Roboter-Interaktionssystem mit Kinect und AI zur Definition der Gesten und 3. ein neues Kollisionsvermeidungssystem wird realisiert. Das System arbeitet an der Erzeugung der kollisionsfreien Pfade, die auf der Wechselwirkung zwischen dem Menschen und dem Roboter basieren

Navigational Path Analysis of Mobile Robot in Various Environments

This dissertation describes work in the area of an autonomous mobile robot. The objective is navigation of mobile robot in a real world dynamic environment avoiding structured and unstructured obstacles either they are static or dynamic. The shapes and position of obstacles are not known to robot prior to navigation. The mobile robot has sensory recognition of specific objects in the environments. This sensory-information provides local information of robots immediate surroundings to its controllers. The information is dealt intelligently by the robot to reach the global objective (the target). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimisation problem and thus can be analyzed and solved using AI techniques. The optimisation of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A successful way of structuring the navigation task deals with the issues of individual behaviour design and action coordination of the behaviours. The navigation objective is addressed using fuzzy logic, neural network, adaptive neuro-fuzzy inference system and different other AI technique.The research also addresses distributed autonomous systems using multiple robot

Planning for Autonomous Operation of Unmanned Surface Vehicles

The growing variety and complexity of marine research and application oriented tasks requires unmanned surface vehicles (USVs) to operate fully autonomously over long time horizons even in environments with significant civilian traffic. The autonomous operations of the USV over long time horizons requires a path planner to compute paths over long distances in complex marine environments consisting of hundreds of islands of complex shapes. The available free space in marine environment changes over time as a result of tides, environmental restrictions, and weather. Secondly, the maximum velocity and energy consumption of the USV is significantly influenced by the fluid medium flows such as strong currents. Finally, the USV have to operate in an unfamiliar, unstructured marine environment with obstacles of variable dimensions, shapes, and motion dynamics such as other unmanned surface vehicles, civilian boats, shorelines, or docks poses numerous planning challenges. The proposed Ph.D. dissertation explores the above mentioned problems by developing computationally efficient path and trajectory planning algorithms that enables the long term autonomous operation of the USVs. We have developed a lattice-based 5D trajectory planner for the USVs operating in the environment with the congested civilian traffic. The planner estimates collision risk and reasons about the availability of contingency maneuvers to counteract unpredictable behaviors of civilian vessels. Secondly, we present a computationally efficient and optimal algorithm for long distance path planning in complex marine environments using A* search on visibility graphs defined over quad trees. Finally, we present an A* based path planning algorithm with newly developed admissible heuristics for computing energy efficient paths in environment with significant fluid flows. The effectiveness of the planning algorithms is demonstrated in the simulation environments by using systems identified dynamics model of the wave amplitude modular vessel (WAM-V) USV14

Doctor of Philosophy

dissertationThis dissertation solves the collision avoidance problem for single- and multi-robot systems where dynamic effects are significant. In many robotic systems (e.g., highly maneuverable and agile unmanned aerial vehicles) the dynamics cannot be ignored and collision avoidance schemes based on kinematic models can result in collisions or provide limited performance, especially at high operating speeds. Herein, real-time, model-based collision avoidance algorithms that explicitly consider the robots' dynamics and perform real-time input changes to alter the trajectory and steer the robot away from potential collisions are developed, implemented, and verified in simulations and physical experiments. Such algorithms are critical in applications where a high degree of autonomy and performance are needed, for example in robot-assisted first response where aerial and/or mobile ground robots are required to maneuver quickly through cluttered and dangerous environments in search of survivors. Firstly, the research extends reciprocal collision avoidance to robots with dynamics by unifying previous approaches to reciprocal collision avoidance under a single, generalized representation using control obstacles. In fact, it is shown how velocity obstacles, acceleration velocity obstacles, continuous control obstacles, and linear quadratic regulator (LQR)-obstacles are special instances of the generalized framework. Furthermore, an extension of control obstacles to general reciprocal collision avoidance for nonlinear, nonhomogeneous systems where the robots may have different state spaces and different nonlinear equations of motion from one another is described. Both simulations and physical experiments are provided for a combination of differential-drive, differential-drive with a trailer, and car-like robots to demonstrate that the approach is capable of letting a nonhomogeneous group of robots with nonlinear equations of motion safely avoid collisions at real-time computation rates. Secondly, the research develops a stochastic collision avoidance algorithm for a tele-operated unmanned aerial vehicle (UAV) that considers uncertainty in the robot's dynamics model and the obstacles' position as measured from sensors. The model-based automatic collision avoidance algorithm is implemented on a custom-designed quadcopter UAV system with on-board computation and the sensor data are processed using a split-and-merge segmentation algorithm and an approximate Minkowski difference. Flight tests are conducted to validate the algorithm's capabilities for providing tele-operated collision-free operation. Finally, a set of human subject studies are performed to quantitatively compare the performance between the model-based algorithm, the basic risk field algorithm (a variant on potential field), and full manual control. The results show that the model-based algorithm performs significantly better than manual control in both the number of collisions and the UAV's average speed, both of which are extremely vital, for example, for UAV-assisted search and rescue applications. Compared to the potential-field-based algorithm, the model-based algorithm allowed the pilot to operate the UAV with higher average speeds

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

Coordination on Systems of Multiple UAVs

Esta tesis trata acerca de métodos para coordinar las trayectorias de un sistema de Vehículos Aéreos no Tripulados y Autónomos (en adelante UAVs). El primer conjunto de técnicas desarrolladas durante la tesis se agrupan dentro de las técnicas de planificación de trayectorias. En este caso, el objetivo es generar planes de vuelo para un conjunto de vehículos coordinadamente de forma que no se produzcan colisiones entre ellos. Además, este tipo de técnicas puede usarse para modificar el plan de vuelo de un subconjunto de UAVs en tiempo real. Entre los algoritmos desarrollados en la tesis podemos destacar la adaptación de algoritmos evolutivos como los Algoritmos Genéticos y el Particle Swarm (Enjambre de Partículas), la incorporación de nuevas formas de muestreo del espacio para la aplicación del algoritmo Optimal Rapidly Exploring Random Trees (RRT*) en sistemas multi-UAV usando técnicas de muestreo novedosas. También se ha estudiado el comportamiento de parte de estos algoritmos en situaciones variables de incertidumbre del estado del sistema. En particular, se propone el uso del Filtro de Partículas para estimar la posición relativa entre varios UAVs. Además, se estudia la aplicación de métodos reactivos para la resolución de colisiones en tiempo real. Esta tesis propone un nuevo algoritmo para la resolución de colisiones entre múltiples UAVs en presencia de obstáculos fijos llamado G-ORCA. Este algoritmo soluciona varios problemas que han surgido al aplicar el algoritmo ORCA en su variante 3D en sistemas compuestos por vehículos reales. Su seguridad se ha demostrado tanto analíticamente, como empíricamente en pruebas con sistemas reales. De hecho, durante esta tesis numerosos experimentos en sistemas multi-UAV reales compuestos hasta por 4 UAVs han sido ejecutados. En dichos experimentos, se realiza una coordinación autónoma de UAVs en las que se asegura la ejecución de trayectorias libres de colisiones garantizando por tanto la seguridad del sistema. Una característica reseñable de esta tesis es que los algoritmos desarrollados han sido probados e integrados en sistemas más complejos que son usados en aplicaciones reales. En primer lugar, se presenta un sistema para aumentar la duración del vuelo de planeadores aprovechando las corrientes ascendentes de viento generadas por el calor (térmicas). En segundo lugar, un sistema de detección y resolución de colisiones coordinado para sistemas con múltiples UAVs reactivo ha sido diseñado, desarrollado y probado experimentalmente. Este sistema ha sido integrado dentro de un sistema automático de construcción de estructuras mediante múltiples UAVs.The aim of this thesis is to propose methods to coordinately generate trajectories for a system of Autonomous Unmanned Aerial Vehicles (UAVs). The first set of proposed techniques developed in this thesis can be defined as trajectory planning techniques. In this case, the objective is to generate coordinated flight plans for a system of UAVs in such a way that no collision are produced among each pair of UAVs. Besides, these techniques can be applied online in order to modify the original flight plan whenever a potential collision is detected. Amongst the developed algorithms in this thesis we can highlight the adaptation of evolutionary algorithms such as Genetic Algorithms and Particle Swarm, and the application of Optimal Rapidly Exploring Random Trees (RRT*) algorithm into a system of several UAVs with novel sampling techniques. In addition, many of these techniques have been adapted in order to be applicable when only uncertain knowledge of the state of the system is available. In particular, the use of the Particle Filter is proposed in order to estimate the relative position between UAVs. The estimation of the position as well as the uncertainty related to this estimation are then taken into account in the conflict resolution system. All techniques proposed in this thesis have been validated by performing several simulated and real tests. For this purpose, a method for randomly generating a huge test batch is presented in chapter 3. This will allow to test the behavior of the proposed methods in a great variety of situations. During the thesis, several real experimentations with fleets composed by up to four UAVs are presented. In these experiments, the UAVs in the system are automatically coordinated in order to ensure collision-free trajectories and thus guarantee the safety of the system. The other main topic of this thesis is the application of reactive methods for real-time conflict resolution. This thesis proposes a novel algorithm for collision resolution amongst multiple UAVs in the presence of static obstacles, which has been called Generalized-Optimal Reciprocal Collision Avoidance (G-ORCA). This algorithm overcomes several issues that have been detected into the algorithm 3D-ORCA in real applications. A remarkable characteristic of this thesis is that the developed algorithms have been applied as a part of more complex systems. First, a coordinated system for flight endurance extension of gliding aircrafts by profiting the ascending wind is presented. Second, a reactive collision avoidance block has been designed, developed and tested experimentally based in the aforementioned G-ORCA algorithm. This block has been integrated into a system for assembly construction with multiple UAVs