Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm

[EN] Autonomous navigation is a complex problem that involves different tasks, such as location of the mobile robot in the scenario, robotic mapping, generating the trajectory, navigating from the initial point to the target point, detecting objects it may encounter in its path, etc. This paper presents a new optimal trajectory planning algorithm that allows the assessment of the energy efficiency of autonomous light vehicles. To the best of our knowledge, this is the first time in the literature that this is carried out by minimizing the travel time while considering the vehicle's dynamic behavior, its limitations, and with the capability of avoiding obstacles and constraining energy consumption. This enables the automotive industry to design environmentally sustainable strategies towards compliance with governmental greenhouse gas (GHG) emission regulations and for climate change mitigation and adaptation policies. The reduction in energy consumption also allows companies to stay competitive in the marketplace. The vehicle navigation control is efficiently implemented through a middleware of component-based software development (CBSD) based on a Robot Operating System (ROS) package. It boosts the reuse of software components and the development of systems from other existing systems. Therefore, it allows the avoidance of complex control software architectures to integrate the different hardware and software components. The global maps are created by scanning the environment with FARO 3D and 2D SICK laser sensors. The proposed algorithm presents a low computational cost and has been implemented as a new module of distributed architecture. It has been integrated into the ROS package to achieve real time autonomous navigation of the vehicle. The methodology has been successfully validated in real indoor experiments using a light vehicle under different scenarios entailing several obstacle locations and dynamic parameters.This work has been partially funded by FEDER-CICYT project with reference DPI2017-84201-R financed by Ministerio de Economia, Industria e Innovacion (Spain).Valera Fernández, Á.; Valero Chuliá, FJ.; Vallés Miquel, M.; Besa Gonzálvez, AJ.; Mata Amela, V.; Llopis-Albert, C. (2021). Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm. Sustainability. 13(3):1-23. https://doi.org/10.3390/su1303123312313

Novel Locomotion Methods in Magnetic Actuation and Pipe Inspection

There is much room for improvement in tube network inspections of jet aircraft. Often, these inspections are incomplete and inconsistent. In this paper, we develop a Modular Robotic Inspection System (MoRIS) for jet aircraft tube networks and a corresponding kinematic model. MoRIS consists of a Base Station for user control and communication, and robotic Vertebrae for accessing and inspecting the network. The presented and tested design of MoRIS can travel up to 9 feet in a tube network. The Vertebrae can navigate in all orientations, including smooth vertical tubes. The design is optimized for nominal 1.5 outside diameter tubes. We developed a model of the Locomotion Vertebra in a tube. We defined the model\u27s coordinate system and its generalized coordinates. We studied the configuration space of the robot, which includes all possible orientations of the Locomotion Vertebra. We derived the expression for the elastic potential energy of the Vertebra\u27s suspensions and minimized it to find the natural settling orientation of the robot. We further explore the effect of the tractive wheel\u27s velocity constraint on locomotion dynamics. Finally, we develop a general model for aircraft tube networks and for a taut tether. Stabilizing bipedal walkers is a engineering target throughout the research community. In this paper, we develop an impulsively actuated walking robot. Through the use of magnetic actuation, for the first time, pure impulsive actuation has been achieved in bipedal walkers. In studying this locomotion technique, we built the world\u27s smallest walker: Big Foot. A dynamical model was developed for Big Foot. A Heel Strike and a Constant Pulse Wave Actuation Schemes were selected for testing. The schemes were validated through simulations and experiments. We showed that there exists two regimes for impulsive actuation. There is a regime for impact-like actuation and a regime for longer duration impulsive actuation

Intelligent sensing for robot mapping and simultaneous human localization and activity recognition

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2011.Thesis (Ph. D.) -- Bilkent University, 2011.Includes bibliographical references leaves 147-163.We consider three different problems in two different sensing domains, namely ultrasonic sensing and inertial sensing. Since the applications considered in each domain are inherently different, this thesis is composed of two main parts. The approach common to the two parts is that raw data acquired from simple sensors is processed intelligently to extract useful information about the environment. In the first part, we employ active snake contours and Kohonen’s selforganizing feature maps (SOMs) for representing and evaluating discrete point maps of indoor environments efficiently and compactly. We develop a generic error criterion for comparing two different sets of points based on the Euclidean distance measure. The point sets can be chosen as (i) two different sets of map points acquired with different mapping techniques or different sensing modalities, (ii) two sets of fitted curve points to maps extracted by different mapping techniques or sensing modalities, or (iii) a set of extracted map points and a set of fitted curve points. The error criterion makes it possible to compare the accuracy of maps obtained with different techniques among themselves, as well as with an absolute reference. We optimize the parameters of active snake contours and SOMs using uniform sampling of the parameter space and particle swarm optimization. A demonstrative example from ultrasonic mapping is given based on experimental data and compared with a very accurate laser map, considered an absolute reference. Both techniques can fill the erroneous gaps in discrete point maps. Snake curve fitting results in more accurate maps than SOMs because it is more robust to outliers. The two methods and the error criterion are sufficiently general that they can also be applied to discrete point maps acquired with other mapping techniques and other sensing modalities. In the second part, we use body-worn inertial/magnetic sensor units for recognition of daily and sports activities, as well as for human localization in GPSdenied environments. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. The error characteristics of the sensors are modeled using the Allan variance technique, and the parameters of lowand high-frequency error components are estimated. Then, we provide a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. We compute a large number of features extracted from the sensor data, and reduce these features using both Principal Components Analysis (PCA) and sequential forward feature selection (SFFS). We consider eight different pattern recognition techniques and provide a comparison in terms of the correct classification rates, computational costs, and their training and storage requirements. Results with sensors mounted on various locations on the body are also provided. The results indicate that if the system is trained by the data of an individual person, it is possible to obtain over 99% correct classification rates with a simple quadratic classifier such as the Bayesian decision method. However, if the training data of that person are not available beforehand, one has to resort to more complex classifiers with an expected correct classification rate of about 85%. We also consider the human localization problem using body-worn inertial/ magnetic sensors. Inertial sensors are characterized by drift error caused by the integration of their rate output to get position information. Because of this drift, the position and orientation data obtained from inertial sensor signals are reliable over only short periods of time. Therefore, position updates from externally referenced sensors are essential. However, if the map of the environment is known, the activity context of the user provides information about position. In particular, the switches in the activity context correspond to discrete locations on the map. By performing activity recognition simultaneously with localization, one can detect the activity context switches and use the corresponding position information as position updates in the localization filter. The localization filter also involves a smoother, which combines the two estimates obtained by running the zero-velocity update (ZUPT) algorithm both forward and backward in time. We performed experiments with eight subjects in an indoor and an outdoor environment involving “walking,” “turning,” and “standing” activities. Using the error criterion in the first part of the thesis, we show that the position errors can be decreased by about 85% on the average. We also present the results of a 3-D experiment performed in a realistic indoor environment and demonstrate that it is possible to achieve over 90% error reduction in position by performing activity recognition simultaneously with localization.Altun, KeremPh.D

Coordination schemes for distributed boundary coverage with a swarm of miniature robots:synthesis, analysis and experimental validation

We provide a comparison of a series of original coordination mechanisms for the distributed boundary coverage problem with a swarm of miniature robots. Our analysis is based on real robot experimentation and models at different levels of abstraction. Distributed boundary coverage is an instance of the distributed coverage problem and has applications such as inspection of structures, de-mining, cleaning, and painting. Coverage is a particularly good example for the benefits of a multi-robot approach due to the potential for parallel task execution and additional robustness out of redundancy. The constraints imposed by a potential application, the autonomous inspection of a jet turbine engine, were our motivation for the algorithms considered in this thesis. Thus, there is particular emphasis on how algorithms perform under the influence of sensor and actuator noise, limited computational and communication capabilities, as well as on the policies about how to cope with such problems. The algorithms developed in this dissertation can be classified into reactive and deliberative algorithms, as well as non-collaborative and collaborative algorithms. The performance of these algorithms ranges from very low to very high, corresponding to highly redundant coverage to near-optimal partitioning of the environments, respectively. At the same time, requirements and assumptions on the robotic platform and the environment (from no communication to global communication, and from no localization to global localization) are incrementally raised. All the algorithms are robust to sensor and actuator noise and gracefully decay to the performance of a randomized algorithm as a function of an increased noise level and/or additional hardware constraints. Although the deliberative algorithms are fully deterministic, the actual performance is probabilistic due to inevitable sensor and actuator noise. For this reason, probabilistic models are used for predicting time to complete coverage and take into account sensor and actuator noise calibrated by using real hardware. For reactive systems with limited memory, the performance is captured using a compact representation based on rate equations that track the expected number of robots in a certain state. As the number of states explode for the deliberative algorithms that require a substantial use of memory, this approach becomes less tractable with the amount of deliberation performed, and we use Discrete Event System (DES) simulation in these cases. Our contribution to the domain of multi-robot systems is three-fold. First, we provide a methodology for system identification and optimal control of a robot swarm using probabilistic models. Second, we develop a series of algorithms for distributed coverage by a team of miniature robots that gracefully decay from a near-optimal performance to the performance of a randomized approach under the influence of sensor and actuator noise. Third, we design an implement a miniature inspection platform based on the miniature robot Alice with ZigBee ready communication capabilities and color vision on a foot-print smaller than 2 × 2 × 3 cm3

Sensing and Control for Robust Grasping with Simple Hardware

Robots can move, see, and navigate in the real world outside carefully structured factories, but they cannot yet grasp and manipulate objects without human intervention. Two key barriers are the complexity of current approaches, which require complicated hardware or precise perception to function effectively, and the challenge of understanding system performance in a tractable manner given the wide range of factors that impact successful grasping. This thesis presents sensors and simple control algorithms that relax the requirements on robot hardware, and a framework to understand the capabilities and limitations of grasping systems.Engineering and Applied Science

Objekt-Manipulation und Steuerung der Greifkraft durch Verwendung von Taktilen Sensoren

This dissertation describes a new type of tactile sensor and an improved version of the dynamic tactile sensing approach that can provide a regularly updated and accurate estimate of minimum applied forces for use in the control of gripper manipulation. The pre-slip sensing algorithm is proposed and implemented into two-finger robot gripper. An algorithm that can discriminate between types of contact surface and recognize objects at the contact stage is also proposed. A technique for recognizing objects using tactile sensor arrays, and a method based on the quadric surface parameter for classifying grasped objects is described. Tactile arrays can recognize surface types on contact, making it possible for a tactile system to recognize translation, rotation, and scaling of an object independently.Diese Dissertation beschreibt eine neue Art von taktilen Sensoren und einen verbesserten Ansatz zur dynamischen Erfassung von taktilen daten, der in regelmäßigen Zeitabständen eine genaue Bewertung der minimalen Greifkraft liefert, die zur Steuerung des Greifers nötig ist. Ein Berechnungsverfahren zur Voraussage des Schlupfs, das in einen Zwei-Finger-Greifarm eines Roboters eingebaut wurde, wird vorgestellt. Auch ein Algorithmus zur Unterscheidung von verschiedenen Oberflächenarten und zur Erkennung von Objektformen bei der Berührung wird vorgestellt. Ein Verfahren zur Objekterkennung mit Hilfe einer Matrix aus taktilen Sensoren und eine Methode zur Klassifikation ergriffener Objekte, basierend auf den Daten einer rechteckigen Oberfläche, werden beschrieben. Mit Hilfe dieser Matrix können unter schiedliche Arten von Oberflächen bei Berührung erkannt werden, was es für das Tastsystem möglich macht, Verschiebung, Drehung und Größe eines Objektes unabhängig voneinander zu erkennen

Modelling and Navigation of Autonomous Vehicles on Roundabouts

A path following controller was proposed that allows autonomous vehicles to safely navigate roundabouts. The controller consisted of a vector field algorithm that generated velocity commands to direct a vehicle. These velocity commands were fulfilled by an actuator controller that converts the velocity commands into wheel torques and steering angles that physically move a vehicle. This conversion is accomplished using an online optimization process that relies on an internal vehicle model to solve for necessary wheel torques and steering angles. To test the controller’s performance, a 16 degree of freedom vehicle dynamic model was developed with consideration for vehicle turn physics. Firstly, tire force data was gathered by performing driving maneuvers on a test track using a vehicle fitted with tire measurement equipment. The generated tire force data was used to compare various combined slip tire force models for their accuracy. The most accurate model was added to the high-fidelity vehicle model. Next, suspension kinematic data was generated using a simple testing procedure. The vehicle was equipped with the tire measurement equipment and the vehicle was raised a lowered with a hydraulic jack. Using displacement and orientation data from this test, a novel reduced order suspension kinematic model that reproduces the observed motion profile was developed. Application of the path following controller to the high-fidelity model resulted in close following of a roundabout path with small deviations

A Novel Propeller Design for Micro-Swimming robot

The applications of a micro-swimming robot such as minimally invasive surgery, liquid pipeline robot etc. are widespread in recent years. The potential application fields are so inspiring, and it is becoming more and more achievable with the development of microbiology and Micro-Electro-Mechanical Systems (MEMS). The aim of this study is to improve the performance of micro-swimming robot through redesign the structure. To achieve the aim, this study reviewed all of the modelling methods of low Reynolds number flow including Resistive-force Theory (RFT), Slender Body Theory (SBT), and Immersed Boundary Method (IBM) etc. The swimming model with these methods has been analysed. Various aspects e.g. hydrodynamic interaction, design, development, optimisation and numerical methods from the previous researches have been studied. Based on the previous design of helix propeller for micro-swimmer, this study has proposed a novel propeller design for a micro-swimming robot which can improve the velocity with simplified propulsion structure. This design has adapted the coaxial symmetric double helix to improve the performance of propulsion and to increase stability. The central lines of two helical tails overlap completely to form a double helix structure, and its tail radial force is balanced with the same direction and can produce a stable axial motion. The verification of this design is conducted using two case studies. The first one is a pipe inspection robot which is in mm scale and swims in high viscosity flow that satisfies the low Reynolds number flow condition. Both simulation and experiment analysis are conducted for this case study. A cross-development method is adopted for the simulation analysis and prototype development. The experiment conditions are set up based on the simulation conditions. The conclusion from the analysis of simulation results gives suggestions to improve design and fabrication for the prototype. Some five revisions of simulation and four revisions of the prototype have been completed. The second case study is the human blood vessel robot. For the limitations of fabrication technology, only simulation is conducted, and the result is compared with previous researches. The results show that the proposed propeller design can improve velocity performance significantly. The main outcomes of this study are the design of a micro-swimming robot with higher velocity performance and the validation from both simulation and experiment

Navigation of Unmanned Aerial Vehicles in GPS-denied Environments

Ph.DDOCTOR OF PHILOSOPH