Traction awareness through haptic feedback for the teleoperation of UGVs*

Teleoperation of Unmanned Ground Vehicles (UGVs) is dependent on several factors as the human operator is physically detached from the UGV. This paper focuses on situations where a UGV designed for search and rescue loses traction, thus becoming unable to comply with the operator's commands. In such situations, the lack of Situation Awareness (SA) may lead to an incorrect and inefficient response to the current UGV state usually confusing and frustrating the human operator. The exclusive use of visual information to simultaneously perform the main task (e.g. search and rescue) and to be aware of possible impediments to UGV operation, such as loss of traction, becomes a very challenging task for a single human operator. We address the challenge of unburdening the visual channel by using other human senses to provide multimodal feedback in UGV teleoperation. To achieve this goal we present a teleoperation architecture comprising (1) a laser-based traction detector module, to discriminate between traction losses (stuck and sliding) and (2) a haptic interface to convey the detected traction state to the human operator through different types of tactile stimuli provided by three haptic devices (E-Vita, Traction Cylinder and Vibrotactile Glove). We also report the experimental results of a user study to evaluate to what extent this new feedback modality improves the user SA regarding the UGV traction state. Statistically significant results were found supporting the hypothesis that two of the haptic devices improved the comprehension of the traction state of the UGV when comparing to exclusively visual modality.info:eu-repo/semantics/acceptedVersio

A Framework for Analyzing and Discussing Level of Human Control Abstraction

It is often useful to understand the impact of an artificial teammate upon human workload in human-machine teams. Levels of Autonomy (LoA) differentiate systems based on control authority. Unfortunately, human workload is not necessarily correlated with LoA. An alternate classification framework, designated the Level of Human Control Abstraction (LHCA), is proposed. LHCA differentiates system states based on the control and monitoring tasks performed and the level of decisions made by humans. The framework defines five levels, designed to differentiate between system states based upon anticipated levels of human attention. This presentation will summarize the framework and demonstrate its application

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Methods for the improvement of power resource prediction and residual range estimation for offroad unmanned ground vehicles

Unmanned Ground Vehicles (UGVs) are becoming more widespread in their deployment. Advances in technology have improved not only their reliability but also their ability to perform complex tasks. UGVs are particularly attractive for operations that are considered unsuitable for human operatives. These include dangerous operations such as explosive ordnance disarmament, as well as situations where human access is limited including planetary exploration or search and rescue missions involving physically small spaces. As technology advances, UGVs are gaining increased capabilities and consummate increased complexity, allowing them to participate in increasingly wide range of scenarios. UGVs have limited power reserves that can restrict a UGV’s mission duration and also the range of capabilities that it can deploy. As UGVs tend towards increased capabilities and complexity, extra burden is placed on the already stretched power resources. Electric drives and an increasing array of processors, sensors and effectors, all need sufficient power to operate. Accurate prediction of mission power requirements is therefore of utmost importance, especially in safety critical scenarios where the UGV must complete an atomic task or risk the creation of an unsafe environment due to failure caused by depleted power. Live energy prediction for vehicles that traverse typical road surfaces is a wellresearched topic. However, this is not sufficient for modern UGVs as they are required to traverse a wide variety of terrains that may change considerably with prevailing environmental conditions. This thesis addresses the gap by presenting a novel approach to both off and on-line energy prediction that considers the effects of weather conditions on a wide variety of terrains. The prediction is based upon nonlinear polynomial regression using live sensor data to improve upon the accuracy provided by current methods. The new approach is evaluated and compared to existing algorithms using a custom ‘UGV mission power’ simulation tool. The tool allows the user to test the accuracy of various mission energy prediction algorithms over a specified mission routes that include a variety of terrains and prevailing weather conditions. A series of experiments that test and record the ‘real world’ power use of a typical small electric drive UGV are also performed. The tests are conducted for a variety of terrains and weather conditions and the empirical results are used to validate the results of the simulation tool. The new algorithm showed a significant improvement compared with current methods, which will allow for UGVs deployed in real world scenarios where they must contend with a variety of terrains and changeable weather conditions to make accurate energy use predictions. This enables more capabilities to be deployed with a known impact on remaining mission power requirement, more efficient mission durations through avoiding the need to maintain excessive estimated power reserves and increased safety through reduced risk of aborting atomic operations in safety critical scenarios. As supplementary contribution, this work created a power resource usage and prediction test bed UGV and resulting data-sets as well as a novel simulation tool for UGV mission energy prediction. The tool implements a UGV model with accurate power use characteristics, confirmed by an empirical test series. The tool can be used to test a wide variety of scenarios and power prediction algorithms and could be used for the development of further mission energy prediction technology or be used as a mission energy planning tool

Scalability of a robotic inspection and repair system

Shift2Rail and In2Smart are two initiatives that will be part of the development of the necessary technologies to complete the Single European Railway Area (SERA). The target of this proposal is to accelerate the integration of new and advanced technologies into innovative rail product solutions. Shift2Rail has a robust framework to meet ambitious objectives. The most important is to double the capacity of the European rail system and increase its reliability and service quality by 50% while having life-cycle costs. In2Smart, as a project directed mainly of Network Rail, is measured in Technology Readiness Levels (TRL). These levels will indicate the maturity of technology for the application into the industry. The intention of this project is to reach a homogeneous TRL 3/4 demonstrator of a system capable to secure proper maintenance of rails, which is a Robotic Inspection and Repair System (RIRS). This research is focused on the scalability of the RIRS, taking into consideration the creation of a representative demonstrator that will authenticate the concept, the validation and verification of that demonstrator and finally the simulation of a scale-up system that will be more robust and will upgrade the TRL. This document contains the development of the control diagrams and schematics for the future incorporation of this control to a higher TRL prototype. The initial demonstrator consists of an autonomous railway vehicle equipped with a robotic arm that will scan the rails searching for faults and simulate a repairing process with a 3D printed polymer. The V&V of the physical demonstrator was a result of tests in the laboratory and the display of the demonstrator in several conferences and events.Manufacturin

Study of Mobile Robot Operations Related to Lunar Exploration

Mobile robots extend the reach of exploration in environments unsuitable, or unreachable, by humans. Far-reaching environments, such as the south lunar pole, exhibit lighting conditions that are challenging for optical imagery required for mobile robot navigation. Terrain conditions also impact the operation of mobile robots; distinguishing terrain types prior to physical contact can improve hazard avoidance. This thesis presents the conclusions of a trade-off that uses the results from two studies related to operating mobile robots at the lunar south pole. The lunar south pole presents engineering design challenges for both tele-operation and lidar-based autonomous navigation in the context of a near-term, low-cost, short-duration lunar prospecting mission. The conclusion is that direct-drive tele-operation may result in improved science data return. The first study is on demonstrating lidar reflectance intensity, and near-infrared spectroscopy, can improve terrain classification over optical imagery alone. Two classification techniques, Naive Bayes and multi-class SVM, were compared for classification errors. Eight terrain types, including aggregate, loose sand and compacted sand, are classified using wavelet-transformed optical images, and statistical values of lidar reflectance intensity. The addition of lidar reflectance intensity was shown to reduce classification errors for both classifiers. Four types of aggregate material are classified using statistical values of spectral reflectance. The addition of spectral reflectance was shown to reduce classification errors for both classifiers. The second study is on human performance in tele-operating a mobile robot over time-delay and in lighting conditions analogous to the south lunar pole. Round-trip time delay between operator and mobile robot leads to an increase in time to turn the mobile robot around obstacles or corners as operators tend to implement a `wait and see\u27 approach. A study on completion time for a cornering task through varying corridor widths shows that time-delayed performance fits a previously established cornering law, and that varying lighting conditions did not adversely affect human performance. The results of the cornering law are interpreted to quantify the additional time required to negotiate a corner under differing conditions, and this increase in time can be interpreted to be predictive when operating a mobile robot through a driving circuit

The development of fire detection robot

Bu tez çalışmasının amacı; özellikle endüstriyel alanlarda, erken yangın algılamada kullanılacak bir yangın algılama robotu tasarlamak ve imal etmektir. Bu robot; önceden belirlenen sanal güzergâh üzerinde engel algılama fonksiyonuyla ve yeniden programlanabilir hareket ünitesiyle devriye gezebilecek ve yangın kaynağını tespit edebilmek için ortam taraması yapabilecek şekilde tasarlanmış ve imal edilmiştir. Sistem; hareket planlama ünitesine tanımlanan programlar ile değişken devriye güzergâhlarını takip edebilme yeteneğine sahiptir. Robotun tasarım ve uygulama süreçleri şu şekildedir; mekanik sistemin tasarımı ve geliştirilmesi, elektronik sistemin tasarımı ve geliştirilmesi ve gerekli yazılımların hazırlanmasıdır. Mekanik sistemin tasarım ve geliştirilme sürecinde; taslak çizimleri, ölçülendirmeler ve üç boyutlu modelleme için bilgisayar destekli tasarım ve katı modelleme programları kullanılmıştır. Robotun taşıyıcı gövdesi; ucuz, sağlam ve kolay işlenebilir malzemeler olan ahşap ve sert plastik köpük kullanılarak imal edilmiştir. Robot sürüş sisteminde diferansiyel metot kullanılmıştır. Yarı otomatik robot dört adet fırçalı doğru akım motoru ile çalışmaktadır. Elektronik sistemin tasarımı ve geliştirilmesi sürecinde; hazır kart almak yerine ihtiyaca uygun elektronik veri kazanım ve kontrol devreleri tasarlanıp üretilmiştir. Bu devrelerin şematik diyagramı ve baskı devresi Proteus elektronik tasarım programı kullanılarak hazırlanmıştır. Bu devreler; motor hareketlerini kontrol etmekte ve dizüstü bilgisayar ile algılama üniteleri arasında bir köprü kurmakta kullanılmıştır. Yazılımların hazırlanma sürecinde; engel algılamada ve güzergâh takibinde kullanılacak akıllı yazılımlar geliştirilmiştir. Ayrıca daha güvenilir yangın algılama sağlamak için; çoklu sensör algılama ve değerlendirme algoritması geliştirilmiştir. Bu tezin sonucunda; özellikle endüstriyel alanlarda kullanılabilecek, çeşitli fonksiyonlara sahip bir yangın algılama robotu tasarlanıp imal edilmiştir. Yapılan testlerle; sistemin en fazla 100 cm mesafedeki yangını, robot 0,5 m/s hızla ilerlerken tespit edebildiği sonucuna varılmıştır.The aim of this thesis is to design and manufacture a fire detection robot that especially operates in industrial areas for fire inspection and early detection. Robot is designed and implemented to track prescribed paths with obstacle avoidance function through obstacle avoidance and motion planning units and to scan the environment in order to detect fire source using fire detection unit. Robot is able to track patrolling routes using virtual lines that defined to the motion planning unit. The design and implementation processes of the robot are as follow; the design and the development of mechanical, electronic systems and software. The design and the development of mechanical system; for the sketch drawings, dimensioning and solid state modeling of the robot, computer aided design and solid modelling computer programs were used. The carrier board of the robot is produced using wooden material and rigid plastic foam which are cheap, strong enough and easy to manufacture. Differential steering method is selected for semi-autonomous robot driving system and it is powered by four brushed DC (direct current) motors. The design and the development of electronic system; electronic circuits were designed and produced, instead of buying a commercial card. Both schematic diagrams and circuits of the data acquisition and control circuits are designed using Proteus electronic design program. These circuits are used to control the motion of the motors and establish a data flow between the laptop and the other peripheral sensing components. Software development; intelligent algorithms for obstacle avoidance and path tracking have been developed. A sensor data fusion algorithm for the sensors was also developed to get more reliable fire detection information. In conclusion; a fire inspection and detection robot with various functions to especially can be used in industrial areas was designed and manufactured. The functions of the robot were tested. It can be concluded that system is able to detect the fire source maximum 100 cm distance away while robot is moving with 0.5 m/s forward speed

Learning to visually predict terrain properties for planetary rovers

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 174-180).For future planetary exploration missions, improvements in autonomous rover mobility have the potential to increase scientific data return by providing safe access to geologically interesting sites that lie in rugged terrain, far from landing areas. This thesis presents an algorithmic framework designed to improve rover-based terrain sensing, a critical component of any autonomous mobility system operating in rough terrain. Specifically, this thesis addresses the problem of predicting the mechanical properties of distant terrain. A self-supervised learning framework is proposed that enables a robotic system to learn predictions of mechanical properties of distant terrain, based on measurements of mechanical properties of similar terrain that has been previously traversed. The proposed framework relies on three distinct algorithms. A mechanical terrain characterization algorithm is proposed that computes upper and lower bounds on the net traction force available at a patch of terrain, via a constrained optimization framework. Both model-based and sensor-based constraints are employed. A terrain classification method is proposed that exploits features from proprioceptive sensor data, and employs either a supervised support vector machine (SVM) or unsupervised k-means classifier to assign class labels to terrain patches that the rover has traversed. A second terrain classification method is proposed that exploits features from exteroceptive sensor data (e.g. color and texture), and is automatically trained in a self-supervised manner, based on the outputs of the proprioceptive terrain classifier.(cont.) The algorithm includes a method for distinguishing novel terrain from previously observed terrain. The outputs of these three algorithms are merged to yield a map of the surrounding terrain that is annotated with the expected achievable net traction force. Such a map would be useful for path planning purposes. The algorithms proposed in this thesis have been experimentally validated in an outdoor, Mars-analog environment. The proprioceptive terrain classifier demonstrated 92% accuracy in labeling three distinct terrain classes. The exteroceptive terrain classifier that relies on self-supervised training was shown to be approximately as accurate as a similar, human-supervised classifier, with both achieving 94% correct classification rates on identical data sets. The algorithm for detection of novel terrain demonstrated 89% accuracy in detecting novel terrain in this same environment. In laboratory tests, the mechanical terrain characterization algorithm predicted the lower bound of the net available traction force with an average margin of 21% of the wheel load.by Christopher A. Brooks.Ph.D