A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment

The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology.  An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization

On Advanced Mobility Concepts for Intelligent Planetary Surface Exploration

Surface exploration by wheeled rovers on Earth's Moon (the two Lunokhods) and Mars (Nasa's Sojourner and the two MERs) have been followed since many years already very suc-cessfully, specifically concerning operations over long time. However, despite of this success, the explored surface area was very small, having in mind a total driving distance of about 8 km (Spirit) and 21 km (Opportunity) over 6 years of operation. Moreover, ESA will send its ExoMars rover in 2018 to Mars, and NASA its MSL rover probably this year. However, all these rovers are lacking sufficient on-board intelligence in order to overcome longer dis-tances, driving much faster and deciding autonomously on path planning for the best trajec-tory to follow. In order to increase the scientific output of a rover mission it seems very nec-essary to explore much larger surface areas reliably in much less time. This is the main driver for a robotics institute to combine mechatronics functionalities to develop an intelligent mo-bile wheeled rover with four or six wheels, and having specific kinematics and locomotion suspension depending on the operational terrain of the rover to operate. DLR's Robotics and Mechatronics Center has a long tradition in developing advanced components in the field of light-weight motion actuation, intelligent and soft manipulation and skilled hands and tools, perception and cognition, and in increasing the autonomy of any kind of mechatronic systems. The whole design is supported and is based upon detailed modeling, optimization, and simula-tion tasks. We have developed efficient software tools to simulate the rover driveability per-formance on various terrain characteristics such as soft sandy and hard rocky terrains as well as on inclined planes, where wheel and grouser geometry plays a dominant role. Moreover, rover optimization is performed to support the best engineering intuitions, that will optimize structural and geometric parameters, compare various kinematics suspension concepts, and make use of realistic cost functions like mass and consumed energy minimization, static sta-bility, and more. For self-localization and safe navigation through unknown terrain we make use of fast 3D stereo algorithms that were successfully used e.g. in unmanned air vehicle ap-plications and on terrestrial mobile systems. The advanced rover design approach is applica-ble for lunar as well as Martian surface exploration purposes. A first mobility concept ap-proach for a lunar vehicle will be presented

Desarrollo y programación de algoritmos para la evitación automática de colisiones. Aplicación a robots móviles terrestres

[ES] El objetivo de este Trabajo Fin de Máster es diseñar e implementar una metodología y sistema para dotar a un robot terrestre móvil con la capacidad de alcanzar eficientemente una posición objetivo dentro de un entorno limitado y que puede verse condicionado por obstáculos desconocidos. Para cumplir este objetivo, el sistema robótico identifica en primer lugar los obstáculos presentes en el entorno y, a continuación, planifica la mejor trayectoria que permita al robot evitar de forma eficiente cualquier posible colisión, lo cuál es un requisito necesario para su seguridad y completa autonomía. La técnica propuesta aborda la planificación y el despliegue de tales maniobras complejas del robot en un entorno real y son aplicables a cualquier entorno industrial. Esto se logra combinando modelos simples, implementaciones eficientes y simulaciones interactivas que aprovechan la agilidad y la maniobrabilidad del robot. En primer lugar, se obtiene una solucion óptima al problema de la evasión de obstáculos mediante el desarrollo de un controlador en Simulink que se basa en una implementación en Matlab del algoritmo A*, un algoritmo clásico de Inteligencia Artificial que permite calcular trayectorias de coste mínimo desde un punto inicial hasta un objetivo dado en un área delimitada. La estrategia de navegación propuesta se prueba primero con SFunctions para validar interactivamente el comportamiento del controlador, y luego en cosimulación de Simulink y el framework Simscape Multibody, para obtener una visión más real al considerar las propiedades físicas del robot y la fricción. A partir de una matriz de entradas que representa una imagen del entorno obtenida utilizando una cámara monocular, la imagen se segmenta y se procesa utilizando la herramienta software Matlab, y se calcula una trayectoria optima para ir desde un punto inicial a un punto final evadiendo los obstáculos del entorno. La trayectoria calculada se evalúa entonces mediante co-simulación, lo que permite analizar visualmente la ruta estimada seguida por el robot. A continuación, la técnica se implementa en un sistema robótico real, el LEGO MINDOSTORMS EV3, siguiendo una metodología que combina también dos sistemas software diferentes siguiendo dos enfoques complementarios: uno más académico, utilizando la biblioteca de soporte de LEGO MINDSTORMS EV3 para Simulink, que permite evaluar su comportamiento en tiempo real y otro más profesional, utilizando el lenguaje de programación basado en C para róbótica RobotC, que atestigua su aplicabilidad a cualquier sistema industrial.[EN] The goal of this Master Thesis is to design and implement a methodology and system to endow a mobile terrestrial robot with the capability to efficiently reach its goals in a bounded environment that can be constrained with unknown obstacles. To meet this goal, the robotic system first identifies the obstacles in the environment and then plans the best robot trajectory that efficiently avoids any possible collision, which is a necessary requirement for the robot to stay safe and completely autonomous. The technique proposed in this thesis supports planning and deploying of such complex robot maneuvers in a real environment and can be applied to any industrial environment. This is done by combining simple models, efficient implementations, and interactive simulations that leverage the agility and maneuverability of the robot. First, an optimal solution to the problem of obstacle avoidance is achieved by developing in Simulink a controller that relies on a Matlab implementation of the A* algorithm, a classical Artificial Intelligence algorithm that is able to compute minimal cost paths from a start point to a target point in a bounded area. The proposed navigation strategy is first tested with S-Functions to interactively validate the controller¿s behavior, and then in co-simulation of Simulink and the Simscape Multibody framework for a more real view that considers the robot¿s physical properties and friction. Starting from a matrix of input values that correspond to an image of the environment obtained by using a monocular camera, the image is segmented and processed in the Matlab software environment, and an optimal trajectory is computed which leads from the starting point to the target point without collision. The path trajectory is evaluated by using co-simulation to visually analyze the estimated path trajectory followed by the robot. Then, the technique is implemented in a real robotic system, the LEGO MINDOSTORMS EV3, by following a methodology that also combines two different software systems following two complementary approaches: a more academic one, by using LEGO MINDSTORMS EV3 support library for Simulink, which allows the robot real time behavior to be evaluated, and a more professional one, by using the C-based robotics programming language RobotC, which witnesses its applicability to any industrial system.Bahilo Alpuente, P. (2018). Desarrollo y programación de algoritmos para la evitación automática de colisiones. Aplicación a robots móviles terrestres. http://hdl.handle.net/10251/107990TFG

Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper

Integration of aerial and terrestrial locomotion modes in a bioinspired robotic system

In robotics, locomotion is a fundamental task for the development of high-level activities such as navigation. For a robotic system, the challenge of evading environmental obstacles depends both on its physical capabilities and on the strategies followed to achieve it. Thus, a robot with the ability to develop several modes of locomotion (walking, flying or swimming) has a greater probability of success in achieving its goal than a robot that develops only one. In nature, Hymenoptera insects use terrestrial and aerial modes of locomotion to carry out their activities. Mimicry the physical capabilities of these insects opens the possibility of improvements in the area of robotic locomotion. Therefore, this work seeks to generate a bio-inspired robotic system that integrates the terrestrial and aerial modes of locomotion. The methodology used in this research project has considered the anatomical study and characterization of Hymenoptera insects locomotion, the proposal of conceptual models that integrate terrestrial and aerial modes locomotion, the construction of a physical platform and experimental testing of the system. In addition, a gait generation approach based on an artificial nervous system of coupled nonlinear oscillators has been proposed. This approach has resulted in the generation of a coherent and functional gait pattern that, in combination with the flight capabilities of the system, has constituted an aero-terrestrial robot. The results obtained in this work include the construction of a bioinspired physical platform, the generation of the gait process using an artificial nervous system and the experimental tests on the integration of aero-terrestrial locomotion.Conacyt - Becario Naciona