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

Challenges and Solutions for Autonomous Robotic Mobile Manipulation for Outdoor Sample Collection

In refinery, petrochemical, and chemical plants, process technicians collect uncontaminated samples to be analyzed in the quality control laboratory all time and all weather. This traditionally manual operation not only exposes the process technicians to hazardous chemicals, but also imposes an economical burden on the management. The recent development in mobile manipulation provides an opportunity to fully automate the operation of sample collection. This paper reviewed the various challenges in sample collection in terms of navigation of the mobile platform and manipulation of the robotic arm from four aspects, namely mobile robot positioning/attitude using global navigation satellite system (GNSS), vision-based navigation and visual servoing, robotic manipulation, mobile robot path planning and control. This paper further proposed solutions to these challenges and pointed the main direction of development in mobile manipulation

Doctor of Philosophy

dissertationThis dissertation solves the collision avoidance problem for single- and multi-robot systems where dynamic effects are significant. In many robotic systems (e.g., highly maneuverable and agile unmanned aerial vehicles) the dynamics cannot be ignored and collision avoidance schemes based on kinematic models can result in collisions or provide limited performance, especially at high operating speeds. Herein, real-time, model-based collision avoidance algorithms that explicitly consider the robots' dynamics and perform real-time input changes to alter the trajectory and steer the robot away from potential collisions are developed, implemented, and verified in simulations and physical experiments. Such algorithms are critical in applications where a high degree of autonomy and performance are needed, for example in robot-assisted first response where aerial and/or mobile ground robots are required to maneuver quickly through cluttered and dangerous environments in search of survivors. Firstly, the research extends reciprocal collision avoidance to robots with dynamics by unifying previous approaches to reciprocal collision avoidance under a single, generalized representation using control obstacles. In fact, it is shown how velocity obstacles, acceleration velocity obstacles, continuous control obstacles, and linear quadratic regulator (LQR)-obstacles are special instances of the generalized framework. Furthermore, an extension of control obstacles to general reciprocal collision avoidance for nonlinear, nonhomogeneous systems where the robots may have different state spaces and different nonlinear equations of motion from one another is described. Both simulations and physical experiments are provided for a combination of differential-drive, differential-drive with a trailer, and car-like robots to demonstrate that the approach is capable of letting a nonhomogeneous group of robots with nonlinear equations of motion safely avoid collisions at real-time computation rates. Secondly, the research develops a stochastic collision avoidance algorithm for a tele-operated unmanned aerial vehicle (UAV) that considers uncertainty in the robot's dynamics model and the obstacles' position as measured from sensors. The model-based automatic collision avoidance algorithm is implemented on a custom-designed quadcopter UAV system with on-board computation and the sensor data are processed using a split-and-merge segmentation algorithm and an approximate Minkowski difference. Flight tests are conducted to validate the algorithm's capabilities for providing tele-operated collision-free operation. Finally, a set of human subject studies are performed to quantitatively compare the performance between the model-based algorithm, the basic risk field algorithm (a variant on potential field), and full manual control. The results show that the model-based algorithm performs significantly better than manual control in both the number of collisions and the UAV's average speed, both of which are extremely vital, for example, for UAV-assisted search and rescue applications. Compared to the potential-field-based algorithm, the model-based algorithm allowed the pilot to operate the UAV with higher average speeds

Trajectory/temporal planning of a wheeled mobile robot

In order for a mobile robot to complete its task it must be able to plan and follow a trajectory. Depending on the environment, it may also be necessary to follow a given velocity profile. This is known as temporal planning. Temporal planning can be used to minimize time of motion and to avoid moving obstacles. For example, assuming the mobile robot is an intelligent wheelchair, it must follow a prescribed path (sidewalk, hospital corridor) while following a strict speed limit (slowing down for pedestrians, cars). Computing a realistic velocity profile for a mobile robot is a challenging task due to a large number of kinematic and dynamic constraints that are involved. Unlike prior works which performed temporal planning in a 2-dimensional environment, this thesis presents a new temporal planning algorithm in a 3-dimensional environment. This algorithm is implemented on a wheeled mobile robot that is to be used in a healthcare setting. The path planning stage is accomplished by using cubic spline functions. A rudimentary trajectory is created by assigning an arbitrary time to each segment of the path. This trajectory is made feasible by applying a number of constraints and using a linear scaling technique. When a velocity profile is provided, a non-linear time scaling technique is used to fit the robot’s center linear velocity to the specified velocity. A method for avoiding moving obstacles is also implemented. Both simulation and experimental results for the wheeled mobile robot are presented. These results show good agreement with each other. For both simulation and experimentation, six different examples of paths in the Engineering Building of the University of Saskatchewan, were used. Experiments were performed using the PowerBot mobile robot in the robotics lab at the University of Saskatchewan

Autonomous navigation of a wheeled mobile robot in farm settings

This research is mainly about autonomously navigation of an agricultural wheeled mobile robot in an unstructured outdoor setting. This project has four distinct phases defined as: (i) Navigation and control of a wheeled mobile robot for a point-to-point motion. (ii) Navigation and control of a wheeled mobile robot in following a given path (path following problem). (iii) Navigation and control of a mobile robot, keeping a constant proximity distance with the given paths or plant rows (proximity-following). (iv) Navigation of the mobile robot in rut following in farm fields. A rut is a long deep track formed by the repeated passage of wheeled vehicles in soft terrains such as mud, sand, and snow. To develop reliable navigation approaches to fulfill each part of this project, three main steps are accomplished: literature review, modeling and computer simulation of wheeled mobile robots, and actual experimental tests in outdoor settings. First, point-to-point motion planning of a mobile robot is studied; a fuzzy-logic based (FLB) approach is proposed for real-time autonomous path planning of the robot in unstructured environment. Simulation and experimental evaluations shows that FLB approach is able to cope with different dynamic and unforeseen situations by tuning a safety margin. Comparison of FLB results with vector field histogram (VFH) and preference-based fuzzy (PBF) approaches, reveals that FLB approach produces shorter and smoother paths toward the goal in almost all of the test cases examined. Then, a novel human-inspired method (HIM) is introduced. HIM is inspired by human behavior in navigation from one point to a specified goal point. A human-like reasoning ability about the situations to reach a predefined goal point while avoiding any static, moving and unforeseen obstacles are given to the robot by HIM. Comparison of HIM results with FLB suggests that HIM is more efficient and effective than FLB. Afterward, navigation strategies are built up for path following, rut following, and proximity-following control of a wheeled mobile robot in outdoor (farm) settings and off-road terrains. The proposed system is composed of different modules which are: sensor data analysis, obstacle detection, obstacle avoidance, goal seeking, and path tracking. The capabilities of the proposed navigation strategies are evaluated in variety of field experiments; the results show that the proposed approach is able to detect and follow rows of bushes robustly. This action is used for spraying plant rows in farm field. Finally, obstacle detection and obstacle avoidance modules are developed in navigation system. These modules enables the robot to detect holes or ground depressions (negative obstacles), that are inherent parts of farm settings, and also over ground level obstacles (positive obstacles) in real-time at a safe distance from the robot. Experimental tests are carried out on two mobile robots (PowerBot and Grizzly) in outdoor and real farm fields. Grizzly utilizes a 3D-laser range-finder to detect objects and perceive the environment, and a RTK-DGPS unit for localization. PowerBot uses sonar sensors and a laser range-finder for obstacle detection. The experiments demonstrate the capability of the proposed technique in successfully detecting and avoiding different types of obstacles both positive and negative in variety of scenarios

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles

Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE

Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving)

[EN] Nowadays, there are many electronic products that incorporate elements and features coming from the research in the field of mobile robotics. For instance, the well-known vacuum cleaning robot Roomba by iRobot, which belongs to the field of service robotics, one of the most active within the sector. There are also numerous autonomous robotic systems in industrial warehouses and plants. It is the case of Autonomous Guided Vehicles (AGVs), which are able to drive completely autonomously in very structured environments. Apart from industry and consumer electronics, within the automotive field there are some devices that give intelligence to the vehicle, derived in most cases from advances in mobile robotics. In fact, more and more often vehicles incorporate Advanced Driver Assistance Systems (ADAS), such as navigation control with automatic speed regulation, lane change and overtaking assistant, automatic parking or collision warning, among other features. However, despite all the advances there are some problems that remain unresolved and can be improved. Collisions and rollovers stand out among the most common accidents of vehicles with manual or autonomous driving. In fact, it is almost impossible to guarantee driving without accidents in unstructured environments where vehicles share the space with other moving agents, such as other vehicles and pedestrians. That is why searching for techniques to improve safety in intelligent vehicles, either autonomous or manual-assisted driving, is still a trending topic within the robotics community. This thesis focuses on the design of tools and techniques for planning and control of intelligent vehicles in order to improve safety and comfort. The dissertation is divided into two parts, the first one on autonomous driving and the second one on manual-assisted driving. The main link between them is the use of clothoids as mathematical formulation for both trajectory generation and collision detection. Among the problems solved the following stand out: obstacle avoidance, rollover avoidance and advanced driver assistance to avoid collisions with pedestrians.[ES] En la actualidad se comercializan infinidad de productos de electrónica de consumo que incorporan elementos y características procedentes de avances en el sector de la robótica móvil. Por ejemplo, el conocido robot aspirador Roomba de la empresa iRobot, el cual pertenece al campo de la robótica de servicio, uno de los más activos en el sector. También hay numerosos sistemas robóticos autónomos en almacenes y plantas industriales. Es el caso de los vehículos autoguiados (AGVs), capaces de conducir de forma totalmente autónoma en entornos muy estructurados. Además de en la industria y en electrónica de consumo, dentro del campo de la automoción también existen dispositivos que dotan de cierta inteligencia al vehículo, derivados la mayoría de las veces de avances en robótica móvil. De hecho, cada vez con mayor frecuencia los vehículos incorporan sistemas avanzados de asistencia al conductor (ADAS por sus siglas en inglés), tales como control de navegación con regulación automática de velocidad, asistente de cambio de carril y adelantamiento, aparcamiento automático o aviso de colisión, entre otras prestaciones. No obstante, pese a todos los avances siguen existiendo problemas sin resolver y que pueden mejorarse. La colisión y el vuelco destacan entre los accidentes más comunes en vehículos con conducción tanto manual como autónoma. De hecho, la dificultad de conducir en entornos desestructurados compartiendo el espacio con otros agentes móviles, tales como coches o personas, hace casi imposible garantizar la conducción sin accidentes. Es por ello que la búsqueda de técnicas para mejorar la seguridad en vehículos inteligentes, ya sean de conducción autónoma o manual asistida, es un tema que siempre está en auge en la comunidad robótica. La presente tesis se centra en el diseño de herramientas y técnicas de planificación y control de vehículos inteligentes, para la mejora de la seguridad y el confort. La disertación se ha dividido en dos partes, la primera sobre conducción autónoma y la segunda sobre conducción manual asistida. El principal nexo de unión es el uso de clotoides como elemento de generación de trayectorias y detección de colisiones. Entre los problemas que se resuelven destacan la evitación de obstáculos, la evitación de vuelcos y la asistencia avanzada al conductor para evitar colisiones con peatones.[CA] En l'actualitat es comercialitzen infinitat de productes d'electrònica de consum que incorporen elements i característiques procedents d'avanços en el sector de la robòtica mòbil. Per exemple, el conegut robot aspirador Roomba de l'empresa iRobot, el qual pertany al camp de la robòtica de servici, un dels més actius en el sector. També hi ha nombrosos sistemes robòtics autònoms en magatzems i plantes industrials. És el cas dels vehicles autoguiats (AGVs), els quals són capaços de conduir de forma totalment autònoma en entorns molt estructurats. A més de en la indústria i en l'electrònica de consum, dins el camp de l'automoció també existeixen dispositius que doten al vehicle de certa intel·ligència, la majoria de les vegades derivats d'avanços en robòtica mòbil. De fet, cada vegada amb més freqüència els vehicles incorporen sistemes avançats d'assistència al conductor (ADAS per les sigles en anglés), com ara control de navegació amb regulació automàtica de velocitat, assistent de canvi de carril i avançament, aparcament automàtic o avís de col·lisió, entre altres prestacions. No obstant això, malgrat tots els avanços segueixen existint problemes sense resoldre i que poden millorar-se. La col·lisió i la bolcada destaquen entre els accidents més comuns en vehicles amb conducció tant manual com autònoma. De fet, la dificultat de conduir en entorns desestructurats compartint l'espai amb altres agents mòbils, tals com cotxes o persones, fa quasi impossible garantitzar la conducció sense accidents. És per això que la recerca de tècniques per millorar la seguretat en vehicles intel·ligents, ja siguen de conducció autònoma o manual assistida, és un tema que sempre està en auge a la comunitat robòtica. La present tesi es centra en el disseny d'eines i tècniques de planificació i control de vehicles intel·ligents, per a la millora de la seguretat i el confort. La dissertació s'ha dividit en dues parts, la primera sobre conducció autònoma i la segona sobre conducció manual assistida. El principal nexe d'unió és l'ús de clotoides com a element de generació de trajectòries i detecció de col·lisions. Entre els problemes que es resolen destaquen l'evitació d'obstacles, l'evitació de bolcades i l'assistència avançada al conductor per evitar col·lisions amb vianants.Girbés Juan, V. (2016). Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/65072TESI

Humanoid manipulation and locomotion with real-time footstep optimization

Cette thèse porte sur la réalisation des tâches avec la locomotion sur des robots humanoïdes. Grâce à leurs nombreux degrés de liberté, ces robots possèdent un très haut niveau de redondance. D’autre part, les humanoïdes sont sous-actionnés dans le sens où la position et l’orientation ne sont pas directement contrôlées par un moteur. Ces deux aspects, le plus souvent étudiés séparément dans la littérature, sont envisagés ici dans un même cadre. En outre, la génération d’un mouvement complexe impliquant à la fois des tâches de manipulation et de locomotion, étudiée habituellement sous l’angle de la planification de mouvement, est abordée ici dans sa composante réactivité temps réel. En divisant le processus d’optimisation en deux étapes, un contrôleur basé sur la notion de pile de tâches permet l’adaptation temps réel des empreintes de pas planifiées dans la première étape. Un module de perception est également conçu pour créer une boucle fermée de perception-décision-action. Cette architecture combinant planification et réactivité est validée sur le robot HRP-2. Deux classes d’expériences sont menées. Dans un cas, le robot doit saisir un objet éloigné, posé sur une table ou sur le sol. Dans l’autre, le robot doit franchir un obstacle. Dans les deux cas, les condition d’exécution sont mises à jour en temps réel pour faire face à la dynamique de l’environnement : changement de position de l’objet à saisir ou de l’obstacle à franchir. ABSTRACT : This thesis focuses on realization of tasks with locomotion on humanoid robots. Thanks to their numerous degrees of freedom, humanoid robots possess a very high level of redundancy. On the other hand, humanoids are underactuated in the sense that the position and orientation of the base are not directly controlled by any motor. These two aspects, usually studied separately in manipulation and locomotion research, are unified in a same framework in this thesis and are resolved as one unique problem. Moreover, the generation of a complex movement involving both tasks and footsteps is also improved becomes reactive. By dividing the optimization process into appropriate stages and by feeding directly the intermediate result to a task-based controller, footsteps can be calculated and adapted in real-time to deal with changes in the environment. A perception module is also developed to build a closed perception-decision-action loop. This architecture combining motion planning and reactivity validated on the HRP-2 robot. Two classes of experiments are carried out. In one case the robot has to grasp an object far away at different height level. In the other, the robot has to step over an object on the floor. In both cases, the execution conditions are updated in real-time to deal with the dynamics of the environment: changes in position of the target to be caught or of the obstacle to be stepped over