scholarly journals ENABLING GRADUATE ENGINEERING STUDENTS WITH PROFICIENCY IN MOBILE ROBOTICS

2018 ◽  
Author(s):  
Adhiti Raman
Keyword(s):  
Student Performance ◽  
System Integration ◽  
Autonomous Vehicles ◽  
Engineering Students ◽  
Mobile Robotics ◽  
Autonomous Vehicle ◽  
Teaching Experiment ◽  
Robotic System ◽  
Complete Control ◽  
Hands On

In recent years, an aggressive expansion of research as well as commercialization efforts in autonomous vehicles can be witnessed. At the same time, many existing companies have expanded their portfolio to autonomous technologies as well (e.g. NVIDIA). This has created an already large need for autonomous-vehicle engineers who are not only proficient in single traditional engineering fields (e.g. mechanical) and old-school automotive studies, but who also have acquired the significantly different, interdisciplinary skillset for mobile robotics. Unlike students of computer science, mechanical engineering graduate students are hardly exposed to coding and robotic system integration in current traditional curricula. The new demands of the automotive industry require an automotive engineer who understands the science of autonomy as well as its impact on the design and implementation of autonomous vehicles, and is equipped with hands-on experience with the latest technology in the field.We describe a unique education program that draws content from traditional courses on mobile-robotics as well as incorporates experiential learning by hands-on training in software, specifically addressing the skill gap in traditional automotive engineering education. Geared towards engineering students with no previous training in robotic system integration, and with only basic undergraduate understanding of programming languages, the teaching experiment employed an active learning approach to introduce numerous concepts as a host of hands-on exercises on multiple robotic platforms. Beginning with simple tutorials on networked communication to demonstrate the power of ROS, the course built up to complete control system design on a student-built RC car that can avoid obstacles and navigate a racecourse by performing SLAM.A brief evaluation of the course exhibited good student performance in general with unique and creative approaches to the programming tasks in particular. Although employing different approaches, each student team was able to demonstrate comparable, efficient performance.

scholarly journals A Hands-On Laboratory for Intelligent Control Courses

2020 ◽  
Vol 10 (24) ◽  
pp. 9070
Author(s):  
Hugo Torres-Salinas ◽  
Juvenal Rodríguez-Reséndiz ◽  
Adyr A. Estévez-Bén ◽  
M. A. Cruz Pérez ◽  
P. Y. Sevilla-Camacho ◽  
...  
Keyword(s):  
Intelligent Control ◽  
Engineering Students ◽  
Industrial Area ◽  
Control Loop ◽  
Ability To Work ◽  
Control Engineering ◽  
Complete Control ◽  
Control Techniques ◽  
Learning Techniques ◽  
Hands On

This research focused on developing a methodology that facilitates the learning of control engineering students, specifically developing skills to design a complete control loop using fuzzy logic. The plant for this control loop is a direct current motor, one of the most common actuators used by educational and professional engineers. The research was carried out on a platform developed by a group of students. Although the learning techniques for the design and implementation of controllers are extensive, there has been a delay in teaching techniques that are relatively new compared to conventional control techniques. Then, the hands-on laboratory offers a tool for students to acquire the necessary skills in driver tuning. In addition to the study of complete systems, the ability to work in a team is developed, a fundamental skill in the professional industrial area. A qualitative and quantitative analysis of student learning was carried out, integrating a multidisciplinary project based on modern tools.

Empowering Graduate Engineering Students With Proficiency in Autonomy

2018 ◽  
Author(s):  
Adhiti T. Raman ◽  
Venkat N. Krovi ◽  
Matthias J. A. Schmid
Keyword(s):  
Systems Engineering ◽  
System Integration ◽  
Engineering Students ◽  
Mobile Robotics ◽  
Situational Awareness ◽  
Autonomous Systems ◽  
Performance Testing ◽  
Course Structure ◽  
Final Project ◽  
Localization And Mapping

A new class of distributed, autonomous systems is emerging, capable of exploiting multimodal distributed and networked spatial and temporal data (at significantly larger scales). A renaissance autonomy engineer requires proficiency in both traditional engineering concepts as well as a systems engineering skillset for implementing the ensuing complex systems. In this paper, we describe goals, development and first offering of a scaffolded course: “AuE 893 Autonomy: Science and Systems” to begin addressing this goal. Geared towards graduate engineering students, with limited prior exposure, the course complements the concepts from traditional courses (on mobile-robotics) with experiential hands-on system-integration efforts (building on the F1tenth.org kits). The staged course structure initially builds upon open-source Robotics Operating System (ROS) tutorials on simulated systems (Gazebo/RViz) with networked communication; Hardware-in-the-loop realization (with a Turtlebot platform) then aids the exploration (and reinforcement) of autonomy concepts. The course culminates in a final-project comprising performance testing with student-team integrated scaled Autonomous Remote Control cars (based on the F1tenth.org parts-list). All three student teams were successful in navigating around a closed racecourse at speeds of 10–15 miles per hour, using Simultaneous Localization and Mapping (SLAM) for situational awareness and obstacle-avoidance. We conclude with discussion of lessons-learnt and opportunities for future improvement.

scholarly journals Students Working Towards Robotic Chauffeurs: The SAE/GM Autodrive Challenge

2018 ◽  
Vol 140 (03) ◽  
pp. S6-S11
Author(s):  
Diane L. Peters
Keyword(s):  
Autonomous Vehicles ◽  
Engineering Students ◽  
Real Life ◽  
Autonomous Driving ◽  
Interdisciplinary Teams ◽  
Academic Disciplines ◽  
General Motors ◽  
Hands On ◽  
The Us ◽  
Time Domain Response

This article focuses on efforts by automotive manufacturers and engineering students towards developing autonomous vehicles. The Society of Automotive Engineers (SAE) has defined different levels of autonomy (SAE J3016 standard), to describe how automated a vehicle is, which have also been adopted by the US Department of Transportation. The purpose of SAE and General Motors (GM) in designing and implementing hands-on engineering design and conducting technology-focused collegiate competition with an emphasis on autonomous driving and the associated technologies´ is to provide a professional development and educational experience for undergraduate and graduate students enrolled at selected universities. SAE and its sponsors are supporting the competition with training and mentoring. Students are also learning how to work in interdisciplinary teams, which has its own issues. Different academic disciplines approach problems differently, use different techniques, and sometimes even seem to speak a different language. Another important thing about the Challenge is that it lets them see how their courses impact real engineering problems. Students taking a controls course see plenty of Laplace transforms and all sorts of plots—root-locus plots, perhaps Nyquist plots or Bode plots, time domain response plots—but they may not always realize how this links up to real life.

Teaching of Multimode Heat Transfer Through Laboratory Experiments

2010 ◽  
Author(s):  
Peter Rodgers ◽  
Shrinivas Bojanampati ◽  
Valerie Eveloy ◽  
Afshin Goharzadeh ◽  
Arman Molki
Keyword(s):  
Heat Transfer ◽  
Student Performance ◽  
Mechanical Engineering ◽  
Engineering Students ◽  
Teaching Strategy ◽  
Real Life ◽  
Vital Role ◽  
Test Facility ◽  
Test Configuration ◽  
Hands On

Hands-on laboratory skills play a vital role in providing mechanical engineering students with a sound understanding of the scientific fundamentals and their application in solving real-life engineering problems. This paper describes a hands-on laboratory thermofluid project which is taught as part of a one-semester, junior-level mechanical engineering course titled Core Measurements Laboratory. The experiment focuses on characterization of heat transfer from a cartridge-heated, isothermal cylinder inside a circular enclosure, by conduction, natural convection and radiation. The project consists in the design and fabrication of the test facility, data acquisition and comparison of experimental results with analytical predictions, with a formal report submitted on completion. The project is undertaken by a team of four students over a five-week period. Emphasis is placed on highlighting potential discrepancies between measurement and analytical predictions, which are inherent in the test configuration considered, reflecting realistic engineering situations. Sample measurement and analysis results are reported. The teaching strategy employed to integrate fundamental theories with hands-on experiences is described. The effectiveness of the laboratory project in enhancing student learning of heat transfer, engineering analysis of discrepancies between predictions and measurements, and project management skills was demonstrated by monitoring student performance improvements over the duration of the project.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

scholarly journals Modelling of the system “driver - automation - autonomous vehicle - road”

2020 ◽  
Vol 10 (1) ◽  
pp. 175-182 ◽  
Author(s):  
Grzegorz Koralewski
Keyword(s):  
Autonomous Vehicles ◽  
Combustion Engine ◽  
Autonomous Vehicle ◽  
Application Software ◽  
Motion Simulation ◽  
Gear Shift ◽  
Shift Control ◽  
Driving Torque ◽  
Physical Quantities ◽  
Motion Quality

AbstractThe work presents a simulation model of a “driver–automation–autonomous vehicles–road” system which is the basis for synthesis of automatic gear shift control system. The mathematical description makes use of physical quantities which characterise driving torque transformation from the combustion engine to the car driven wheels. The basic components of the model are algorithms for the driver’s action logic in controlling motion velocity, logic of gear shift control functioning regarding direction and moment of switching, for determining right-hand side of differential equations and for motion quality indicators. The model is realised in a form of an application software package, comprising sub-programmes for input data, for computerised motion simulation of cars with mechanical and hydro-mechanical – automatically controlled – transmission systems and for models of characteristic car routes.

scholarly journals An Open-Source Scale Model Platform for Teaching Autonomous Vehicle Technologies

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3850
Author(s):  
Bastien Vincke ◽  
Sergio Rodriguez Rodriguez Florez ◽  
Pascal Aubert
Keyword(s):  
Open Source ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Constrained Systems ◽  
Scale Model ◽  
Technical Students ◽  
Vehicle Technologies ◽  
Model Platform ◽  
High Level ◽  
Time And Energy

Emerging technologies in the context of Autonomous Vehicles (AV) have drastically evolved the industry’s qualification requirements. AVs incorporate complex perception and control systems. Teaching the associated skills that are necessary for the analysis of such systems becomes a very difficult process and existing solutions do not facilitate learning. In this study, our efforts are devoted to proposingan open-source scale model vehicle platform that is designed for teaching the fundamental concepts of autonomous vehicles technologies that are adapted to undergraduate and technical students. The proposed platform is as realistic as possible in order to present and address all of the fundamental concepts that are associated with AV. It includes all on-board components of a stand-alone system, including low and high level functions. Such functionalities are detailed and a proof of concept prototype is presented. A set of experiments is carried out, and the results obtained using this prototype validate the usability of the model for the analysis of time- and energy-constrained systems, as well as distributed embedded perception systems.

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