Application of Fractional Calculus in the Dynamical Analysis and Control of Mechanical Manipulators

Mathematics Subject Classification: 26A33, 93C83, 93C85, 68T40

Développement et expérimentation d'algorithmes de réorientation pour un robot sériel en chute libre

Ce mémoire présente 2 types de méthodes pour effectuer la réorientation d’un robot sériel en chute libre en utilisant les mouvements internes de celui-ci. Ces mouvements sont prescrits à partir d’algorithmes de planification de trajectoire basés sur le modèle dynamique du robot. La première méthode tente de réorienter le robot en appliquant une technique d’optimisation locale fonctionnant avec une fonction potentielle décrivant l’orientation du système, et la deuxième méthode applique des fonctions sinusoïdales aux articulations pour réorienter le robot. Pour tester les performances des méthodes en simulation, on tente de réorienter le robot pour une configuration initiale et finale identiques où toutes les membrures sont alignées mais avec le robot ayant complété une rotation de 180 degrés sur lui-même. Afin de comparer les résultats obtenus avec la réalité, un prototype de robot sériel plan flottant possédant trois membrures et deux liaisons rotoïdes est construit. Les expérimentations effectuées montrent que le prototype est capable d’atteindre les réorientations prescrites si peu de perturbations extérieures sont présentes et ce, même si le contrôle de l’orientation est effectué en boucle ouverte.

Vehículo autónomo polivalente para trabajos en invernadero.

Vehículo autónomo polivalente para trabajos en invernadero. El objeto de la invención es un vehículo que de forma autónoma puede moverse entre las líneas de cultivo en invernadero para realizar diferentes operaciones. Está dotado de un chasis diseñado para poder recibir diferentes implementos, así como un punto de enganche en la parte trasera para el acoplamiento de máquinas y equipos. Dispone de un sistema de transmisión hidrostático controlado electrónicamente que permite mover el equipo en un rango de velocidades continuo para adaptarla convenientemente a las exigencias del trabajo, además de suministrar energía a los implementos que la requieran. El movimiento del vehículo se consigue mediante el equilibrio dinámico de cada una de las orugas que componen el sistema de rodadura. Para ello dispone de un sistema sensorial compuesto por captadores de presión, codificadores incrementales y un radar. El sistema sensorial del vehículo se completa con sensores de ultrasonido distribuidos por todo el perímetro (detección de obstáculos), una brújula magnética (orientación del vehículo) y sensores de seguridad (prevenir colisiones). La información suministrada por el sistema sensorial permite la localización y navegación del vehículo en el entorno de trabajo. Toda la información es gestionada por un sistema empotrado, donde se ejecutan los programas que controlan el vehículo. El equipo también está dotado de una cámara multiespectral, adaptada para realizar un seguimiento continuo de determinados parámetros del cultivo, como pueden ser: detectar problemas fitosanitarios y de nutrición, controlar la evolución de la masa vegetal, localizar frutos, determinar la maduración de los frutos, etc.

Uma abordagem evolucionária para o planejamento online de rotas para múltiplos robôs móveis em ambiente multiagente

O problema de planejamento de rotas de robôs móveis consiste em determinar a melhor rota para um robô, em um ambiente estático e/ou dinâmico, que seja capaz de deslocá-lo de um ponto inicial até e um ponto final, também em conhecido como estado objetivo. O presente trabalho emprega o uso de uma abordagem baseada em Algoritmos Genéticos para o planejamento de rotas de múltiplos robôs em um ambiente complexo composto por obstáculos fixos e obstáculos moveis. Através da implementação do modelo no software do NetLogo, uma ferramenta utilizada em simulações de aplicações multiagentes, possibilitou-se a modelagem de robôs e obstáculos presentes no ambiente como agentes interativos, viabilizando assim o desenvolvimento de processos de detecção e desvio de obstáculos. A abordagem empregada busca pela melhor rota para robôs e apresenta um modelo composto pelos operadores básicos de reprodução e mutação, acrescido de um novo operador duplo de refinamento capaz de aperfeiçoar as melhores soluções encontradas através da eliminação de movimentos inúteis. Além disso, o calculo da rota de cada robô adota um método de geração de subtrechos, ou seja, não calcula apenas uma unica rota que conecta os pontos inicial e final do cenário, mas sim várias pequenas subrotas que conectadas formam um caminho único capaz de levar o robô ao estado objetivo. Neste trabalho foram desenvolvidos dois cenários, para avaliação da sua escalabilidade: o primeiro consiste em um cenário simples composto apenas por um robô, um obstáculo movel e alguns obstáculos fixos; já o segundo, apresenta um cenário mais robusto, mais amplo, composto por múltiplos robôs e diversos obstáculos fixos e moveis. Ao final, testes de desempenho comparativos foram efetuados entre a abordagem baseada em Algoritmos Genéticos e o Algoritmo A*. Como critério de comparação foi utilizado o tamanho das rotas obtidas nas vinte simulações executadas em cada abordagem. A analise dos resultados foi especificada através do Teste t de Student.

A comparative study of navigation meshes

International audience

Triangular mesh parameterization with trimmed surfaces

Given a 2manifold triangular mesh \(M \subset {\mathbb {R}}^3\), with border, a parameterization of \(M\) is a FACE or trimmed surface \(F=\{S,L_0,\ldots, L_m\}\) -- \(F\) is a connected subset or region of a parametric surface \(S\), bounded by a set of LOOPs \(L_0,\ldots ,L_m\) such that each \(L_i \subset S\) is a closed 1manifold having no intersection with the other \(L_j\) LOOPs -- The parametric surface \(S\) is a statistical fit of the mesh \(M\) -- \(L_0\) is the outermost LOOP bounding \(F\) and \(L_i\) is the LOOP of the ith hole in \(F\) (if any) -- The problem of parameterizing triangular meshes is relevant for reverse engineering, tool path planning, feature detection, redesign, etc -- Stateofart mesh procedures parameterize a rectangular mesh \(M\) -- To improve such procedures, we report here the implementation of an algorithm which parameterizes meshes \(M\) presenting holes and concavities -- We synthesize a parametric surface \(S \subset {\mathbb {R}}^3\) which approximates a superset of the mesh \(M\) -- Then, we compute a set of LOOPs trimming \(S\), and therefore completing the FACE \(F=\ {S,L_0,\ldots ,L_m\}\) -- Our algorithm gives satisfactory results for \(M\) having low Gaussian curvature (i.e., \(M\) being quasi-developable or developable) -- This assumption is a reasonable one, since \(M\) is the product of manifold segmentation preprocessing -- Our algorithm computes: (1) a manifold learning mapping \(\phi : M \rightarrow U \subset {\mathbb {R}}^2\), (2) an inverse mapping \(S: W \subset {\mathbb {R}}^2 \rightarrow {\mathbb {R}}^3\), with \ (W\) being a rectangular grid containing and surpassing \(U\) -- To compute \(\phi\) we test IsoMap, Laplacian Eigenmaps and Hessian local linear embedding (best results with HLLE) -- For the back mapping (NURBS) \(S\) the crucial step is to find a control polyhedron \(P\), which is an extrapolation of \(M\) -- We calculate \(P\) by extrapolating radial basis functions that interpolate points inside \(\phi (M)\) -- We successfully test our implementation with several datasets presenting concavities, holes, and are extremely nondevelopable -- Ongoing work is being devoted to manifold segmentation which facilitates mesh parameterization

Ship performance modelling for least-CO2 emission routes

Decarbonization of maritime transport requires immediate action. In the short term, ship weather routing can provide greenhouse gas emission reductions, even for existing ships and without retrofitting them. Weather routing is based on making optimal use of both envi- ronmental information and knowledge about vessel seakeeping and performance. Combining them at a state-of-the-art level and making use of path planning in realistic conditions can be challenging. To address these topics in an open-source framework, this thesis led to the development of a new module called bateau , and to its combination with the ship routing model VISIR. bateau includes both hull geometry and propulsion modelling for various vessel types. It has two objectives: to predict the sustained speed in a seaway and to estimate the CO2 emission rate during the voyage. Various semi-empirical approaches were used in bateau to predict the ship hydro- and aerodynamical resistance in both head and oblique seas. Assuming that the ship sails at a constant engine load, the involuntary speed loss due to waves was estimated. This thesis also attempted to clarify the role played by the actual representation of the sea state. In particular, the influence of the wave steepness parameter was assessed. For dealing with ships with a greater superstructure, the wind added resistance was also estimated. Numerical experiments via bateau were conducted for both a medium and a large-size container ships, a bulk-carrier, and a tanker. The simulations of optimal routes were carried out for a feeder containership during voyages in the North Indian Ocean and in the South China Sea. Least-CO2 routes were compared to the least-distance ones, assessing the relative CO2 savings. Analysis fields from the Copernicus Marine Service were used in the numerical experiments.

Studio e implementazione di algoritmi per il tracciamento ottimo di satellite LEO da stazioni di terra di tipo radioamatoriale

Questo elaborato di tesi ha l’obbiettivo di studiare le limitazioni delle stazioni di terra nel tracciamento di satelliti in orbita LEO, investigare possibili soluzioni ed implementare queste soluzioni all’interno della Ground Station AMGS di Forlì per verificarne l’efficacia. A questo scopo, dopo un’attenta revisione della letteratura sono stati identificati due promettenti algoritmi descritti nei paper: “Trajectory optimisation to minimise antenna pointing error” di P. S. Crawford , R. J. H. Brush e “An optimal antenna motion generation using shortest path planning” di Moon-Jin Jeon , Dong-Soo Kwon. Questi algoritmi sono stati implementi in Python 3, al fine di inglobarli all’interno del software di tracking al momento in uso nella GS di Forlì, ovvero AMGS Orbit Predictor. All’interno di questo elaborato sono anche riportati i risultati dei test conseguiti e una valutazione dettagliata di questi ultimi.

Planning autonomous vehicles in the absence of speed lanes using an elastic strip

Planning of autonomous vehicles in the absence of speed lanes is a less-researched problem. However, it is an important step toward extending the possibility of autonomous vehicles to countries where speed lanes are not followed. The advantages of having nonlane-oriented traffic include larger traffic bandwidth and more overtaking, which are features that are highlighted when vehicles vary in terms of speed and size. In the most general case, the road would be filled with a complex grid of static obstacles and vehicles of varying speeds. The optimal travel plan consists of a set of maneuvers that enables a vehicle to avoid obstacles and to overtake vehicles in an optimal manner and, in turn, enable other vehicles to overtake. The desired characteristics of this planning scenario include near completeness and near optimality in real time with an unstructured environment, with vehicles essentially displaying a high degree of cooperation and enabling every possible(safe) overtaking procedure to be completed as soon as possible. Challenges addressed in this paper include a (fast) method for initial path generation using an elastic strip, (re-)defining the notion of completeness specific to the problem, and inducing the notion of cooperation in the elastic strip. Using this approach, vehicular behaviors of overtaking, cooperation, vehicle following,obstacle avoidance, etc., are demonstrated.

Manoeuvre Based Mission Control System for an Autonomous Surface Vehicle

In this work the mission control and supervision system developed for the ROAZ Autonomous Surface Vehicle is presented. Complexity in mission requirements coupled with flexibility lead to the design of a modular hierarchical mission control system based on hybrid systems control. Monitoring and supervision control for a vehicle such as ROAZ mission is not an easy task using tools with low complexity and yet powerful enough. A set of tools were developed to perform both on board mission control and remote planning and supervision. “ROAZ- Mission Control” was developed to be used in support to bathymetric and security missions performed in river and at seas.

Multi-vehicle planning using RRT-connect

The problem of planning multiple vehicles deals with the design of an effective algorithm that can cause multiple autonomous vehicles on the road to communicate and generate a collaborative optimal travel plan. Our modelling of the problem considers vehicles to vary greatly in terms of both size and speed, which makes it suboptimal to have a faster vehicle follow a slower vehicle or for vehicles to drive with predefined speed lanes. It is essential to have a fast planning algorithm whilst still being probabilistically complete. The Rapidly Exploring Random Trees (RRT) algorithm developed and reported on here uses a problem specific coordination axis, a local optimization algorithm, priority based coordination, and a module for deciding travel speeds. Vehicles are assumed to remain in their current relative position laterally on the road unless otherwise instructed. Experimental results presented here show regular driving behaviours, namely vehicle following, overtaking, and complex obstacle avoidance. The ability to showcase complex behaviours in the absence of speed lanes is characteristic of the solution developed.

Multi-level planning for semi-autonomous vehicles in traffic scenarios based on separation maximization

The planning of semi-autonomous vehicles in traffic scenarios is a relatively new problem that contributes towards the goal of making road travel by vehicles free of human drivers. An algorithm needs to ensure optimal real time planning of multiple vehicles (moving in either direction along a road), in the presence of a complex obstacle network. Unlike other approaches, here we assume that speed lanes are not present and that different lanes do not need to be maintained for inbound and outbound traffic. Our basic hypothesis is to carry forward the planning task to ensure that a sufficient distance is maintained by each vehicle from all other vehicles, obstacles and road boundaries. We present here a 4-layer planning algorithm that consists of road selection (for selecting the individual roads of traversal to reach the goal), pathway selection (a strategy to avoid and/or overtake obstacles, road diversions and other blockages), pathway distribution (to select the position of a vehicle at every instance of time in a pathway), and trajectory generation (for generating a curve, smooth enough, to allow for the maximum possible speed). Cooperation between vehicles is handled separately at the different levels, the aim being to maximize the separation between vehicles. Simulated results exhibit behaviours of smooth, efficient and safe driving of vehicles in multiple scenarios; along with typical vehicle behaviours including following and overtaking.

Planning autonomous vehicles in the absence of speed lanes using lateral potentials

Chaotic traffic, prevalent in many countries, is marked by a large number of vehicles driving with different speeds without following any predefined speed lanes. Such traffic rules out using any planning algorithm for these vehicles which is based upon the maintenance of speed lanes and lane changes. The absence of speed lanes may imply more bandwidth and easier overtaking in cases where vehicles vary considerably in both their size and speed. Inspired by the performance of artificial potential fields in the planning of mobile robots, we propose here lateral potentials as measures to enable vehicles to decide about their lateral positions on the road. Each vehicle is subjected to a potential from obstacles and vehicles in front, road boundaries, obstacles and vehicles to the side and higher speed vehicles to the rear. All these potentials are lateral and only govern steering the vehicle. A speed control mechanism is also used for longitudinal control of vehicle. The proposed system is shown to perform well for obstacle avoidance, vehicle following and overtaking behaviors.

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.

Evaluation of mission planning strategies of a robotic aerial vehicle for the AIRC international competition

The International Aerial Robotics Competition (IARC) is an important event where teams from universities design flying autonomous vehicles to overcome the last challenges in the field. The goal of the Seventh Mission proposed by the IARC is to guide several mobile ground robots to a target area. The scenario is complex and not determinist due to the random behavior of the ground robots movement. The UAV must select efficient strategies to complete the mission. The goal of this work has been evaluating different alternative mission planning strategies of a UAV for this competition. The Mission Planner component is in charge of taking the UAV decisions. Different strategies have been developed and evaluated for the component, achieving a better performance Mission Planner and valuable knowledge about the mission. For this purpose, it was necessary to develop a simulator to evaluate the different strategies. The simulator was built as an improvement of an existing previous version.