Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

This paper introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic environments. Individually, RL and FMP algorithms each have their own limitations. FMP is not able to produce time-optimal paths and existing RL solutions are not able to produce collision-free paths in dense environments. Therefore, we first tried improving the performance of recent RL approaches by introducing a new reward function that not only eliminates the requirement of a pre supervised learning (SL) step but also decreases the chance of collision in crowded environments. That improved things, but there were still a lot of failure cases. So, we developed a hybrid approach to leverage the simpler FMP approach in stuck, simple and high-risk cases, and continue using RL for normal cases in which FMP can't produce optimal path. Also, we extend GA3C-CADRL algorithm to 3D environment. Simulation results show that the proposed algorithm outperforms both deep RL and FMP algorithms and produces up to 50% more successful scenarios than deep RL and up to 75% less extra time to reach goal than FMP.Comment: IEEE Robotics and Automation Letters (2020

A New Approach to Evaluation of the Material Cutting Using the Artificial Neural Networks

Article deals with the process of accurate measurements and evaluation, i.e. the monitoring of the technological process of the material cutting using the Abrasive Water Jet and process control while using the artificial neural network, and a suggestion of process steps, particularly by a thorough application of mathematical modelling principles. An important mission of this article is to document, in an easy and comprehensible way, a tool intended for the creation of good control properties for multiple axes of the material cutting head support (using the AWJ, Plasma, acetylene burner, etc.). A desired objective is to achieve the best possible or optimal quality of the surface of the cut material, while using the properties of modern cutting technologies. To achieve those objectives, one of several known options was applied, in particular the advantages of the artificial neural networks and their beneficial properties. A simple task will be used to practically document the behaviour of an artificial neural network and a selected model execution

Intelligent Navigation for a Solar Powered Unmanned Underwater Vehicle

In this paper, an intelligent navigation system for an unmanned underwater vehicle powered by renewable energy and designed for shadow water inspection in missions of a long duration is proposed. The system is composed of an underwater vehicle, which tows a surface vehicle. The surface vehicle is a small boat with photovoltaic panels, a methanol fuel cell and communication equipment, which provides energy and communication to the underwater vehicle. The underwater vehicle has sensors to monitor the underwater environment such as sidescan sonar and a video camera in a flexible configuration and sensors to measure the physical and chemical parameters of water quality on predefined paths for long distances. The underwater vehicle implements a biologically inspired neural architecture for autonomous intelligent navigation. Navigation is carried out by integrating a kinematic adaptive neuro‐controller for trajectory tracking and an obstacle avoidance adaptive neuro‐  controller. The autonomous underwater vehicle is capable of operating during long periods of observation and monitoring. This autonomous vehicle is a good tool for observing large areas of sea, since it operates for long periods of time due to the contribution of renewable energy. It correlates all sensor data for time and geodetic position. This vehicle has been used for monitoring the Mar Menor lagoon.Supported by the Coastal Monitoring System for the Mar Menor (CMS‐  463.01.08_CLUSTER) project founded by the Regional Government of Murcia, by the SICUVA project (Control and Navigation System for AUV Oceanographic Monitoring Missions. REF: 15357/PI/10) founded by the Seneca Foundation of Regional Government of Murcia and by the DIVISAMOS project (Design of an Autonomous Underwater Vehicle for Inspections and oceanographic mission‐UPCT: DPI‐ 2009‐14744‐C03‐02) founded by the Spanish Ministry of Science and Innovation from Spain

Study of FSRU-LNGC System Based on a Quantitative Multi-cluster Risk Informed Model

PresentationThe offshore LNG terminal, referred to as LNG floating storage unit or floating storage and re- gasification unit (FSRU), performs well on both building process and operation process. The LNG FSRU is a cost-effective and time efficient solution for LNG transferring in the offshore area, and it brings minimal impacts to the surrounding environment as well. This paper proposed a systematic method to integrate chemical process safety with maritime safety analysis. The evaluation network was adopted to process a comparison study between two possible locations for LNG offshore FSRU. This research divided the whole process into three parts, beginning with the LNG Carrier navigating in the inbound channel, the berthing operation and ending with the completion of LNG transferring operation. The preferred location is determined by simultaneously evaluating navigation safety, berthing safety and LNG transferring safety objectives based on the quantitative multi-cluster network multi-attribute decision analysis (QMNMDA) method. The maritime safety analysis, including navigational process and berthing process, was simulated by LNG ship simulator and analyzed by statistical tools; evaluation scale for maritime safety analysis were determined by analyzing data from ninety experts. The chemical process safety simulation was employed to LNG transferring events such as connection hose rupture, flange failure by the consequence simulation tool. Two scenarios, i.e., worst case scenario and maximum credible scenario, were taken into consideration by inputting different data of evaluating parameters. The QMNMDA method transformed the evaluation criteria to one comparable unit, safety utility value, to evaluate the different alternatives. Based on the final value of the simulation, the preferred location can be determined, and the mitigation measures were presented accordingly

Diseño del software de control de un UUV para monitorización oceanográfica usando un modelo de componentes y framework con despliegue flexible

Los vehículos submarinos no tripulados (Unmanned Underwater Vehicles, UUVs) se diseñan para misiones de monitorización, inspección e intervención. En estudios oceanográficos y de monitorización ambiental son cada vez más demandados por las innumerables ventajas que presentan con respecto a las tecnologías tradicionales. Estos vehículos son desarrollados para superar los retos científicos y los problemas de ingeniería que aparecen en el entorno no estructurado y hostil del fondo marino en el que operan. Su desarrollo no solo conlleva las mismas dificultades que el resto de los robots de servicio (heterogeneidad en el hardware, incertidumbre de los sistemas de medida, complejidad del software, etc.), sino que además se les unen las propias del dominio de aplicación, la robótica submarina: condiciones de iluminación, incertidumbre en cuanto a posición y velocidad, restricciones energéticas, etc. Este artículo describe el UUV AEGIR, un vehículo utilizado como banco de pruebas para la implementación de estrategias de control y misiones oceanográficas. También describe el desarrollo de una cadena de herramientas que sigue un enfoque dirigido por modelos, utilizada en el diseño del software de control del vehículo, así como un framework basado en componentes que proporciona el soporte de ejecución de la aplicación y permite su despliegue flexible en nodos, procesos e hilos y pre-verificación del comportamiento concurrente. Su diseño ha permitido desarrollar, comprobar y añadir los componentes que proporcionan el comportamiento necesario para que el UUV AEGIR pudiera completar con éxito distintos tipos de misiones oceanográficas.Este trabajo ha sido parcialmente financiado por el proyecto financiado por la CICYT del Gobierno Español DIVISAMOS (ref. DPI2009-14744-C03-02) y ViSelTR (ref. TIN2012-39279), así como por el proyecto financiado por la Fundación Séneca de la Región de Murcia MISSION-SICUVA (ref. 15374/PI/10) y el proyecto “Coastal Monitoring System for the Mar Menor Coastal Lagoon (PEPLAN 463.02-08 CLUSTER de la Región de Murcia. Francisco Sánchez Ledesma agradece la financiación recibida por parte del programa de becas FPU del MEC (beca AP2009-5083). Por último, los autores quieren agradecer también a la Armada Española la cesión del vehículo UUV y su posterior ayuda en su reconstrucción

Facility Siting Study of LNG-FSRU System Based on Quantitative Multi-Hierarchy Framework MADA

This research proposed to establish a quantitative assessment framework for a site selection study of liquefied natural gas (LNG) receiving terminal by considering both chemical process safety and marine transportation safety. The offshore LNG terminal, referred as LNG floating storage unit (FSU) or floating storage and re-gasification unit (FSRU), performs well on both building and operation processes. The LNG FSRU system is a cost-effective and time efficient solution for LNG transferring in the offshore area, and it brings minimal impacts to the surrounding environment as well. This paper proposed an evaluation framework for LNG FSRU system site selection. The evaluation framework was adopted to process a comparison study between two possible locations for LNG offshore FSU/FSRU. This research divided the whole process into three, beginning with the LNG Carrier navigating in the inbound channel, through the berthing operation and ending with the completion of LNG transferring operation. The preferred location is determined by simultaneously evaluating navigation safety, berthing safety and LNG transferring safety objectives based on the quantitative multi-hierarchy framework multi-attribute decision analysis (QMFMADA) method. The maritime safety analysis, including navigational process and berthing process, was simulated by LNG ship simulator DMU V-Dragon 3000A and analyzed by statistical software such as R and JMP. The chemical process safety simulation was employed to LNG transferring events such as connection hose rupture, flange failure by the consequence simulation tool Safeti. Two scenarios, i.e., worst case scenario and maximum credible scenario, were taken into consideration by inputting different data of evaluating parameters. The QMFMADA method transformed the evaluation criteria to one comparable unit, risk utility value, to evaluate the different alternatives. Based on the final value of the simulation, the preferred location can be determined and the mitigation measures were presented accordingly

A Generalized Neural Network Approach to Mobile Robot Navigation and Obstacle Avoidance

In this thesis, we tackle the problem of extending neural network navigation algorithms for various types of mobile robots and 2-dimensional range sensors. We propose a general method to interpret the data from various types of 2-dimensional range sensors and a neural network algorithm to perform the navigation task. Our approach can yield a global navigation algorithm which can be applied to various types of range sensors and mobile robot platforms. Moreover, this method allows the neural networks to be trained using only one type of 2-dimensional range sensor, which contributes positively to reducing the time required for training the networks. Experimental results carried out in simulation environments demonstrate the effectiveness of our approach in mobile robot navigation for different kinds of robots and sensors. Therefore, the successful implementation of our method provides a solution to apply mobile robot navigation algorithms to various robot platforms

Analysis and Development of Computational Intelligence based Navigational Controllers for Multiple Mobile Robots

Navigational path planning problems of the mobile robots have received considerable attention over the past few decades. The navigation problem of mobile robots are consisting of following three aspects i.e. locomotion, path planning and map building. Based on these three aspects path planning algorithm for a mobile robot is formulated, which is capable of finding an optimal collision free path from the start point to the target point in a given environment. The main objective of the dissertation is to investigate the advanced methodologies for both single and multiple mobile robots navigation in highly cluttered environments using computational intelligence approach. Firstly, three different standalone computational intelligence approaches based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Cuckoo Search (CS) algorithm and Invasive Weed Optimization (IWO) are presented to address the problem of path planning in unknown environments. Next two different hybrid approaches are developed using CS-ANFIS and IWO-ANFIS to solve the mobile robot navigation problems. The performance of each intelligent navigational controller is demonstrated through simulation results using MATLAB. Experimental results are conducted in the laboratory, using real mobile robots to validate the versatility and effectiveness of the proposed navigation techniques. Comparison studies show, that there are good agreement between them. During the analysis of results, it is noticed that CS-ANFIS and IWO-ANFIS hybrid navigational controllers perform better compared to other discussed navigational controllers. The results obtained from the proposed navigation techniques are validated by comparison with the results from other intelligent techniques such as Fuzzy logic, Neural Network, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and other hybrid algorithms. By investigating the results, finally it is concluded that the proposed navigational methodologies are efficient and robust in the sense, that they can be effectively implemented to solve the path optimization problems of mobile robot in any complex environment