Wavefront Propagation and Fuzzy Based Autonomous Navigation

Path planning and obstacle avoidance are the two major issues in any navigation system. Wavefront propagation algorithm, as a good path planner, can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper presents the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model of therobot environment. Waypoints in the path are incorporated into the obstacle avoidance. Features such as ageing of objects and smooth motion planning are implemented to enhance efficiency and also to cater for dynamic environments

Towards a Shared Control Navigation Function:Efficiency Based Command Modulation

This paper presents a novel shared control algorithm for robotized wheelchairs. The proposed algorithm is a new method to extend autonomous navigation techniques into the shared control domain. It reactively combines user’s and robot’s commands into a continuous function that approximates a classic Navigation Function (NF) by weighting input commands with NF constraints. Our approach overcomes the main drawbacks of NFs -calculus complexity and limitations on environment modeling- so it can be used in dynamic unstructured environments. It also benefits from NF properties: convergence to destination, smooth paths and safe navigation. Due to the user’s contribution to control, our function is not strictly a NF, so we call it a pseudo-navigation function (PNF) instead.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.The authors also would like to thank their home Institute, CEFET/RJ, the federal Brazilian research agencies CAPES (code 001) and CNPq, and the Rio de Janeiro research agency, FAPERJ, for supporting this work.info:eu-repo/semantics/publishedVersio

Sound Localization and Multi-Modal Steering for Autonomous Virtual Agents

With the increasing realism of interactive applications, there is a growing need for harnessing additional sensory modalities such as hearing. While the synthesis and propagation of sounds in virtual environments has been explored, there has been little work that addresses sound localization and its integration into behaviors for autonomous virtual agents. This paper develops a framework that enables autonomous virtual agents to localize sounds in dynamic virtual environments, subject to distortion effects due to attenuation, reflection and diffraction from obstacles, as well as interference between multiple audio signals. We additionally integrate hearing into standard predictive collision avoidance techniques and couple it with vision to allow agents to react to what they see and hear, while navigating in virtual environments

Behavioural strategy for indoor mobile robot navigation in dynamic environments

PhD ThesisDevelopment of behavioural strategies for indoor mobile navigation has become a challenging and practical issue in a cluttered indoor environment, such as a hospital or factory, where there are many static and moving objects, including humans and other robots, all of which trying to complete their own specific tasks; some objects may be moving in a similar direction to the robot, whereas others may be moving in the opposite direction. The key requirement for any mobile robot is to avoid colliding with any object which may prevent it from reaching its goal, or as a consequence bring harm to any individual within its workspace. This challenge is further complicated by unobserved objects suddenly appearing in the robots path, particularly when the robot crosses a corridor or an open doorway. Therefore the mobile robot must be able to anticipate such scenarios and manoeuvre quickly to avoid collisions. In this project, a hybrid control architecture has been designed to navigate within dynamic environments. The control system includes three levels namely: deliberative, intermediate and reactive, which work together to achieve short, fast and safe navigation. The deliberative level creates a short and safe path from the current position of the mobile robot to its goal using the wavefront algorithm, estimates the current location of the mobile robot, and extracts the region from which unobserved objects may appear. The intermediate level links the deliberative level and the reactive level, that includes several behaviours for implementing the global path in such a way to avoid any collision. In avoiding dynamic obstacles, the controller has to identify and extract obstacles from the sensor data, estimate their speeds, and then regular its speed and direction to minimize the collision risk and maximize the speed to the goal. The velocity obstacle approach (VO) is considered an easy and simple method for avoiding dynamic obstacles, whilst the collision cone principle is used to detect the collision situation between two circular-shaped objects. However the VO approach has two challenges when applied in indoor environments. The first challenge is extraction of collision cones of non-circular objects from sensor data, in which applying fitting circle methods generally produces large and inaccurate collision cones especially for line-shaped obstacle such as walls. The second challenge is that the mobile robot cannot sometimes move to its goal because all its velocities to the goal are located within collision cones. In this project, a method has been demonstrated to extract the colliii sion cones of circular and non-circular objects using a laser sensor, where the obstacle size and the collision time are considered to weigh the robot velocities. In addition the principle of the virtual obstacle was proposed to minimize the collision risk with unobserved moving obstacles. The simulation and experiments using the proposed control system on a Pioneer mobile robot showed that the mobile robot can successfully avoid static and dynamic obstacles. Furthermore the mobile robot was able to reach its target within an indoor environment without causing any collision or missing the target

Robot Trajectories Comparison: A Statistical Approach

The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM2 and WaveFront, using different environments, robots, and local planners

Vision-based Testbeds For Control System Applicaitons

In the field of control systems, testbeds are a pivotal step in the validation and improvement of new algorithms for different applications. They provide a safe, controlled environment typically having a significantly lower cost of failure than the final application. Vision systems provide nonintrusive methods of measurement that can be easily implemented for various setups and applications. This work presents methods for modeling, removing distortion, calibrating, and rectifying single and two camera systems, as well as, two very different applications of vision-based control system testbeds: deflection control of shape memory polymers and trajectory planning for mobile robots. First, a testbed for the modeling and control of shape memory polymers (SMP) is designed. Red-green-blue (RGB) thresholding is used to assist in the webcam-based, 3D reconstruction of points of interest. A PID based controller is designed and shown to work with SMP samples, while state space models were identified from step input responses. Models were used to develop a linear quadratic regulator that is shown to work in simulation. Also, a simple to use graphical interface is designed for fast and simple testing of a series of samples. Second a robot testbed is designed to test new trajectory planning algorithms. A templatebased predictive search algorithm is investigated to process the images obtained through a lowcost webcam vision system, which is used to monitor the testbed environment. Also a userfriendly graphical interface is developed such that the functionalities of the webcam, robots, and optimizations are automated. The testbeds are used to demonstrate a wavefront-enhanced, Bspline augmented virtual motion camouflage algorithm for single or multiple robots to navigate through an obstacle dense and changing environment, while considering inter-vehicle conflicts, iv obstacle avoidance, nonlinear dynamics, and different constraints. In addition, it is expected that this testbed can be used to test different vehicle motion planning and control algorithms

Sensoriamento remoto para culturas agrícolas baseado em um quadricóptero de baixo custo

Este artículo presenta una propuesta para recolectar información de cultivos agrícolas mediante un cuadricóptero de bajo costo, llamado AR Drone 2.0. Para lograr el objetivo se diseña un sistema de teledetección que enmarca desafíos identificados en la presente investigación, tales como, la adquisición de fotografías aéreas de todo un cultivo y la navegación del AR Drone en zonas no planas. El proyecto se encuentra en una fase temprana de desarrollo. La primera etapa indaga la plataforma y las herramientas  hardware y software para construir el prototipo propuesto; la segunda, describe los experimentos de desempeño de los sensores de estabilidad y altura del AR Drone, con el fin de diseñar una estrategia de control de altura en cultivos no planos.  Además, se evalúan algoritmos de planificación de ruta basados en la ruta más corta mediante grafos (Dijkstra, A* y propagación de frente de onda) usando un cuadricóptero simulado. La implementación de los algoritmos de la ruta más corta es el comienzo de la cobertura total de un cultivo. Las observaciones del comportamiento del cuadricóptero en el simulador Gazebo y las pruebas reales, demuestran la viabilidad de ejecutar el proyecto, usando el AR Drone como plataforma de un sistema de teledetección para agricultura de precisión.This paper presents a proposal for information gathering from crops by means of a low-cost quadcopter known as the AR Drone 2.0. To achieve this, we designed a system for remote sensing that addresses challenges identified in the present research, such as acquisition of aerial photographs of an entire crop and AR Drone navigation on non-planar areas arises. The project is currently at an early stage of development. The first stage describes platform and hardware/software tools used to build the proposed prototype. Second stage characterizes performance experiments of sensors stability and altitude in AR Drone, in order to design an altitude strategy control over non-flat crops. In addition, path planning algorithms based on shortest route by graphs (Dijkstra, A* and wavefront propagation) are evaluated with simulated quadcopter. The implementation of the shortest path algorithms is the beginning to full coverage of a crop. Observations of quadcopter behavior in Gazebo simulator and real tests demonstrate viability to execute the project by using AR Drone like platform of a remote sensing system to precision agriculture.Este artigo apresenta uma proposta para a coleta de informações sobre as culturas agrícolas utilizando um quadricóptero de baixo custo, chamado AR Drone 2.0. Para atingir o objetivo proposto foi desenhado um sistema de sensoriamento remoto que determina desafios, tais como a aquisição de fotografias aéreas de toda a colheita e a navegação do AR Drone em áreas não planas. O projeto está atualmente na sua fase de desenvolvimento. A primeira fase examina a plataforma e as ferramentas de hardware e de software necessárias para construir o protótipo proposto; a segunda fase descreve os experimentos de desempenho da estabilidade e da altura do AR Drone, a fim de conceber uma estratégia para o controle de altura em colheitas não planas; aliás, são avaliados algoritmos de planificação de rota com base na rota mais curta mediante grafos (Dijkstra, A *, e propagação da frente de onda) usando um quadricóptero simulado. A implementação dos algoritmos da rota mais curta é o início da cobertura total de uma colheita. Tanto as observações do comportamento do quadricóptero no simulador Gazebo, como os testes reais, demonstram a viabilidade de implementar o projeto usando o AR Drone como uma plataforma para um sistema de sensoriamento remoto para a agricultura de precisão

Characterization of Quad-Copter Positioning Systems and the Effect of Pose Uncertainties on Field Probe Measurements

When measuring the Radar Cross Section (RCS) of a test object, many uncertainties must be accounted for, such as the non-homogeneous nature of the medium between the radar test equipment and the platform under test. There are a variety of other error sources, including clutter and Radio Frequency Interference (RFI), motivating the development of techniques to measure and model the uncertainties in RCS measurements. The following research, in unison with prior and current efforts, intends to reduce the impact of these uncertainties by utilizing a unique two-way field probe in the form of a geodesic sphere encompassing a commercial quad-copter aircraft. The probe is used to measure the incident fields in the target volume in an effort to quantify one of the key sources of uncertainty in an RCS measurement, distortions in the incident wave. In order to do this, the geodesic sphere must be fully understood. This research determined the uncertainty of the probe by creating a calibrated data set of the probe’s RCS, extracting the calibrated RCS based on the measurement flight path, comparing the measured with the calibrated data, and determining the deviation in the difference. The accuracy of the comparison, and therefore the measurement, depends on the accuracy of the flight path. An uncertainty in the probe’s position and orientation during flight translates into a field measurement uncertainty. These uncertainties were determined for the Parrot Bebop quad-copter, a differential GPS, and a Vicon™ system. Each uncertainty was fed into the measurement model and their measurement uncertainties were determined. Field measurement accuracies of \u3c 2° in phase and \u3c 0.05V/m in magnitude were demonstrated