A New Tentacles-based Technique for Avoiding Obstacles during Visual Navigation

International audienceIn this paper, we design and validate a new tentacle-based approach, for avoiding obstacles during appearance-based navigation with a wheeled mobile robot. In the past, we have developed a framework for safe visual navigation. The robot follows a path represented as a set of key images, and during obstacle circumnavigation, the on-board camera is actuated to maintain scene visibility. In those works, the model used for obstacle avoidance was obtained using a potential vector field. Here, a more sophisticated and efficient method, that exploits the robot kinematic model, and predicts collision at look-ahead distances, is designed and integrated in that framework. Outdoor experiments comparing the two models show that the new approach presents many advantages. Higher speeds and precision can be attained, very cluttered scenarios involving large obstacles can be successfully dealt with, and the control inputs are smoother

Percepção do ambiente urbano e navegação usando visão robótica : concepção e implementação aplicado à veículo autônomo

Orientadores: Janito Vaqueiro Ferreira, Alessandro Corrêa VictorinoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: O desenvolvimento de veículos autônomos capazes de se locomover em ruas urbanas pode proporcionar importantes benefícios na redução de acidentes, no aumentando da qualidade de vida e também na redução de custos. Veículos inteligentes, por exemplo, frequentemente baseiam suas decisões em observações obtidas a partir de vários sensores tais como LIDAR, GPS e câmeras. Atualmente, sensores de câmera têm recebido grande atenção pelo motivo de que eles são de baixo custo, fáceis de utilizar e fornecem dados com rica informação. Ambientes urbanos representam um interessante mas também desafiador cenário neste contexto, onde o traçado das ruas podem ser muito complexos, a presença de objetos tais como árvores, bicicletas, veículos podem gerar observações parciais e também estas observações são muitas vezes ruidosas ou ainda perdidas devido a completas oclusões. Portanto, o processo de percepção por natureza precisa ser capaz de lidar com a incerteza no conhecimento do mundo em torno do veículo. Nesta tese, este problema de percepção é analisado para a condução nos ambientes urbanos associado com a capacidade de realizar um deslocamento seguro baseado no processo de tomada de decisão em navegação autônoma. Projeta-se um sistema de percepção que permita veículos robóticos a trafegar autonomamente nas ruas, sem a necessidade de adaptar a infraestrutura, sem o conhecimento prévio do ambiente e considerando a presença de objetos dinâmicos tais como veículos. Propõe-se um novo método baseado em aprendizado de máquina para extrair o contexto semântico usando um par de imagens estéreo, a qual é vinculada a uma grade de ocupação evidencial que modela as incertezas de um ambiente urbano desconhecido, aplicando a teoria de Dempster-Shafer. Para a tomada de decisão no planejamento do caminho, aplica-se a abordagem dos tentáculos virtuais para gerar possíveis caminhos a partir do centro de referencia do veículo e com base nisto, duas novas estratégias são propostas. Em primeiro, uma nova estratégia para escolher o caminho correto para melhor evitar obstáculos e seguir a tarefa local no contexto da navegação hibrida e, em segundo, um novo controle de malha fechada baseado na odometria visual e o tentáculo virtual é modelado para execução do seguimento de caminho. Finalmente, um completo sistema automotivo integrando os modelos de percepção, planejamento e controle são implementados e validados experimentalmente em condições reais usando um veículo autônomo experimental, onde os resultados mostram que a abordagem desenvolvida realiza com sucesso uma segura navegação local com base em sensores de câmeraAbstract: The development of autonomous vehicles capable of getting around on urban roads can provide important benefits in reducing accidents, in increasing life comfort and also in providing cost savings. Intelligent vehicles for example often base their decisions on observations obtained from various sensors such as LIDAR, GPS and Cameras. Actually, camera sensors have been receiving large attention due to they are cheap, easy to employ and provide rich data information. Inner-city environments represent an interesting but also very challenging scenario in this context, where the road layout may be very complex, the presence of objects such as trees, bicycles, cars might generate partial observations and also these observations are often noisy or even missing due to heavy occlusions. Thus, perception process by nature needs to be able to deal with uncertainties in the knowledge of the world around the car. While highway navigation and autonomous driving using a prior knowledge of the environment have been demonstrating successfully, understanding and navigating general inner-city scenarios with little prior knowledge remains an unsolved problem. In this thesis, this perception problem is analyzed for driving in the inner-city environments associated with the capacity to perform a safe displacement based on decision-making process in autonomous navigation. It is designed a perception system that allows robotic-cars to drive autonomously on roads, without the need to adapt the infrastructure, without requiring previous knowledge of the environment and considering the presence of dynamic objects such as cars. It is proposed a novel method based on machine learning to extract the semantic context using a pair of stereo images, which is merged in an evidential grid to model the uncertainties of an unknown urban environment, applying the Dempster-Shafer theory. To make decisions in path-planning, it is applied the virtual tentacle approach to generate possible paths starting from ego-referenced car and based on it, two news strategies are proposed. First one, a new strategy to select the correct path to better avoid obstacles and to follow the local task in the context of hybrid navigation, and second, a new closed loop control based on visual odometry and virtual tentacle is modeled to path-following execution. Finally, a complete automotive system integrating the perception, path-planning and control modules are implemented and experimentally validated in real situations using an experimental autonomous car, where the results show that the developed approach successfully performs a safe local navigation based on camera sensorsDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânic

Realization of Performance Advancements for WPI\u27s UGV - Prometheus

The objective of this project is to design and implement performance improvements for WPI\u27s intelligent ground vehicle, Prometheus, leading to a more competitive entry at the Intelligent Ground Vehicle Competition. Performance enhancements implemented by the project team include a new upper chassis design, a reconfigurable camera mount, extended Kalman filter-based localization with a GPS receiver and a compass module, a lane detection algorithm, and a modular software framework. As a result, Prometheus has improved autonomy, accessibility, robustness, reliability, and usability

Towards sensor-based manipulation of flexible objects

International audience— This paper presents the FLEXBOT project, a joint LIRMM-QUT effort to develop (in the near future) novel methodologies for robotic manipulation of flexible and deformable objects. To tackle this problem, and based on our past experiences, we propose to merge vision and force for manipulation control, and to rely on Model Predictive Control (MPC) and constrained optimization to program the object future shape. Index Terms— Control for object manipulation, learning from human demonstration, sensor fusion based on tactile, force and vision feedback. I. CONTEXT This abstract does not present experimental results, but aims at giving some preliminary hints on how flexible robot manipulation should be realized in the near future, particularly in the context of the FLEXBOT project, jointly submitted to the PHC FASIC Program 1 by LIRMM and QUT researchers. The objective of FLEXBOT is to solve one of the most challenging open problems in robotics. In fact, we aim at developing novel methodologies enabling robotic manipulation of flexible and deformable objects. The motivation comes from numerous applications, including the domestic, industrial, and medical examples 2 shown in Fig. 1. Many difficulties emerge when dealing with flexible manipulation. In the first place, the object deformation model (involving elasticity or plasticity) must be known, to derive the robot control inputs required for reconfiguring its shape. Ideally, this model should be derived online, while manipulating , with a simultaneous estimation and control approach, as commonly done in active perception and visual servoing. Hence perception, particularly from vision and force, will be indispensable. This leads to a second major difficulty: deformable object visual tracking. In fact, most current visual object tracking algorithms rely on rigidity, an assumption that is not valid here. A third challenge will consist in generating control inputs that comply with the shape the object is expected to have in the near future. In the next section, we provide a brief survey of the state of art on flexible object manipulation. We then conclude by proposing some novel methodologies for addressing the problem

System Integration and Intelligence Improvements for WPI’s UGV - Prometheus

This project focuses on realizing a series of operational improvements for WPI\u27s unmanned ground vehicle Prometheus with the end goal of a prize winning entry to the Intelligent Ground Vehicle Challenge. Operational improvements include a practical implementation of stereo vision on an NVIDIA GPU, a more reliable implementation of line detection, a better approach to mapping and path planning, and a modified system architecture realized by an easier to work with GPIO implementation. The end result of these improvements is better autonomy, accessibility, robustness, reliability, and usability for Prometheus

Sistemas de suporte à condução autónoma adequados a plataforma robótica 4-wheel skid-steer: percepção, movimento e simulação

As competições de robótica móvel desempenham papel preponderante na difusão da ciência e da engenharia ao público em geral. E também um espaço dedicado ao ensaio e comparação de diferentes estratégias e abordagens aos diversos desafios da robótica móvel. Uma das vertentes que tem reunido maior interesse nos promotores deste género de iniciativas e entre o público em geral são as competições de condução autónoma. Tipicamente as Competi¸c˜oes de Condução Autónoma (CCA) tentam reproduzir um ambiente semelhante a uma estrutura rodoviária tradicional, no qual sistemas autónomos deverão dar resposta a um conjunto variado de desafios que vão desde a deteção da faixa de rodagem `a interação com distintos elementos que compõem uma estrutura rodoviária típica, do planeamento trajetórias à localização. O objectivo desta dissertação de mestrado visa documentar o processo de desenho e concepção de uma plataforma robótica móvel do tipo 4-wheel skid-steer para realização de tarefas de condução autónoma em ambiente estruturado numa pista que pretende replicar uma via de circulação automóvel dotada de sinalética básica e alguns obstáculos. Paralelamente, a dissertação pretende também fazer uma análise qualitativa entre o processo de simulação e a sua transposição para uma plataforma robótica física. inferir sobre a diferenças de performance e de comportamento.Mobile robotics competitions play an important role in the diffusion of science and engineering to the general public. It is also a space dedicated to test and compare different strategies and approaches to several challenges of mobile robotics. One of the aspects that has attracted more the interest of promoters for this kind of initiatives and general public is the autonomous driving competitions. Typically, Autonomous Driving Competitions (CCAs) attempt to replicate an environment similar to a traditional road structure, in which autonomous systems should respond to a wide variety of challenges ranging from lane detection to interaction with distinct elements that exist in a typical road structure, from planning trajectories to location. The aim of this master’s thesis is to document the process of designing and endow a 4-wheel skid-steer mobile robotic platform to carry out autonomous driving tasks in a structured environment on a track that intends to replicate a motorized roadway including signs and obstacles. In parallel, the dissertation also intends to make a qualitative analysis between the simulation process and the transposition of the developed algorithm to a physical robotic platform, analysing the differences in performance and behavior

Control of Magnetic Continuum Robots for Endoscopy

The present thesis discusses the problem of magnetic actuation and control applied to millimetre-scale robots for endoluminal procedures. Magnetic actuation, given its remote manipulation capabilities, has the potential to overcome several limitations of current endoluminal procedures, such as the relatively large size, high sti�ness and limited dexterity of existing tools. The application of functional forces remotely facilitates the development of softer and more dexterous endoscopes, which can navigate with reduced discomfort for the patient. However, the solutions presented in literature are not always able to guarantee smooth navigation in complex and convoluted anatomical structures. This thesis aims at improving the navigational capabilities of magnetic endoluminal robots, towards achieving full autonomy. This is realized by introducing novel design, sensing and control approaches for magnetically actuated soft endoscopes and catheters. First, the application of accurate closed-loop control to a 1 Internal Permanent Magnet (IPM) endoscope was analysed. The proposed approach can guarantee better navigation capabilities, thanks to the manipulation of every mechanical Degree of Freedom (DOF) - 5 DOFs. Speci�cally, it was demonstrated that gravity can be balanced with su�cient accuracy to guarantee tip levitation. In this way contact is minimized and obstacle avoidance improved. Consequently, the overall navigation capabilities of the endoscope were enhanced for given application. To improve exploration of convoluted anatomical pathways, the design of magnetic endoscopes with multiple magnetic elements along their length was introduced. This approach to endoluminal device design can ideally allow manipulation along the full length; facilitating full shape manipulation, as compared to tip-only control. To facilitate the control of multiple magneto-mechanical DOFs along the catheters' length, a magnetic actuation method was developed based on the collaborative robotic manipulation of 2 External Permanent Magnets (EPMs). This method, compared to the state-of-the-art, facilitates large workspace and applied �eld, while guaranteeing dexterous actuation. Using this approach, it was demonstrated that it is possible to actuate up to 8 independent magnetic DOFs. In the present thesis, two di�erent applications are discussed and evaluated, namely: colonoscopy and navigational bronchoscopy. In the former, a single-IPM endoscopic approach is utilized. In this case, the anatomy is large enough to permit equipping the endoscope with a camera; allowing navigation by direct vision. Navigational bronchoscopy, on-the-other-hand, is performed in very narrow peripheral lumina, and navigation is informed via pre-operative imaging. The presented work demonstrates how the design of the magnetic catheters, informed by a pre-operative Computed Tomography (CT) scan, can mitigate the need for intra-operative imaging and, consequently, reduce radiation exposure for patients and healthcare workers. Speci�cally, an optimization routine to design the catheters is presented, with the aim of achieving follow-the-leader navigation without supervision. In both scenarios, analysis of how magnetic endoluminal devices can improve the current practice and revolutionize the future of medical diagnostics and treatment is presented and discussed

Study of Cooperative Control System for Multiple Mobile Robots Using Particle Swarm Optimization

The idea of using multiple mobile robots for tracking targets in an unknown environment can be realized with Particle Swarm Optimization proposed by Kennedy and Eberhart in 1995. The actual implementation of an efficient algorithm like Particle Swarm Optimization (PSO) is required when robots need to avoid the randomly placed obstacles in unknown environment and reach the target point. However, ordinary methods of obstacle avoidance have not proven good results in route planning. PSO is a self-adaptive population-based method in which behavior of the swarm is iteratively generated from the combination of social and cognitive behaviors and is an effective technique for collective robotic search problem. When PSO is used for exploration, this algorithm enables robots to travel on trajectories that lead to total swarm convergence on some target