construct a map of the environment, as well as its known relative position, in accordance with its location, by using Simultaneous Localization And Mapping (SLAM) (Ghani et. al. 2014). Turtlebot uses the SLAM algorithm called GMapping. Using GMapping, the robot analyzes an existing map to find the best route to get to a destination(Schmidt et. al. 2012). If multiple routes exist there are existing algorithms to help the robot make a decision. In this paper, we document our findings of the many deficiencies in this method of “Robot-made” mapping, and then we propose a method that does not have these same deficiencies. We present a method where the floor plan could be converted to the map file format that
, manage, and improve operations. Skills Include: Systems Integration Planning RFP Development/Grant Writing Technical management including software development, system administration , telecommunications Professional Development Process Re-engineering Disaster Recovery End User Training ERP Design/Implementation IT Manage- ment Project Management Solution Engineering Systems SupportProf. Dennis O. Owen, Purdue University Dennis Owen is an Associate Professor of Computer and Information Technology at Purdue University. His primary teaching interests include application development and computer hardware. He is active in recruiting and curriculum transformation. c American Society for
IEEE.Lei Wang, Anhui Polytechnic University Lei Wang received the Ph.D. degree in mechanical and electronic engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2010. From November 2010 till date he works in Anhui Polytechnic University, Wuhu, China. He is an Associate Professor at Anhui Polytechnic University. His current research interests include engineering education, intelligent manufacturing system, job shop scheduling and mobile robot path planning. c American Society for Engineering Education, 2017 Multi-Lab-Driven Learning Method Used for Robotics ROS Study Chaomin Luo1, Jiawen Wang2, Wenbing Zhao3, and Lei Wang4
university-specific information reflecting the university’soverall vision and purpose ((Kibuuka, 2001), as cited in Creamer and Ghoston (2013)), and areoften developed through strategic planning in institutions. Thus, multiple research studies haveacknowledged mission statements to be important in describing institutions intent and goals(e.g.,Tierney, 1999; Young, 2001), and have argued that institutions need to be more strategic indeveloping statements which truly reflect their characteristics (e.g., Barnett (2003) in Kreber andMhina (2007)). In describing contradicting views on the significance of mission statementsKreber and Mhina (2007) cite Detomasi (1995) to describe how the latter suggest that missionstatements are “embarrassingly vague, and
such asusing library resources efficiently, ethics in research, scientific communication skills,information about applying to and planning for graduate education, funding sources forgraduate education, and industry careers. The students also participated in social events suchas a welcome picnic and a trip to a state park.Literature ReviewResearchers have found that educational benefits to students participating in undergraduateresearch experiences are improvements in communication and research skills, ability toperform teamwork, and motivation to pursue advanced degrees (Bauer & Bennett, 2003;Lopatto, 2004; 2007). Large gains in “clarification or confirmation of career/education paths”and personal/professional domains (such as “thinking
programs? Figure 2: IAB Survey. time-consuming and lacks guidance." • In [15] Sandersen says "The changes to the ABET-CAC assessment criteria are significant, and most programs are going to have to revise their assessment plans before their next visit."A majority of the literature reports on ABET accreditation conclude the beneficial results of theABET process justify the time and difficulty of conducting the process. All of the papers thatconclude thusly are by authors from departments that have successfully gone through theprocess.As noted in Section 2.1 of the paper, there are a large number of computer science degree pro-grams that are not ABET accredited. For
fluency, design fluency, cognitiveflexibility (the mental ability to think about multiple concepts), planning, response inhibition,handling novel situations, working memory, reasoning, problem solving, and abstract thinking(Alvarez, Emory and Emory 2006; Lezak, Howieson, and Loring, 2004; Monsell, 2003). Normanand Shallice (1980) outline five types of situations where routine activation of behavior wouldnot be sufficient for optimal performance: 1. Those that involve planning or decision making 2. Those that involve error correction or troubleshooting 3. Situations where responses are not well-rehearsed or contain novel sequences of actions 4. Dangerous or technically difficult situations 5. Situations that
continually improve and expand the activities for a wider age and experience range.Additionally, the author plans to extend several of these activities to cover more advancecomputer science topics. For example, with the “Network Topology and Problem Solving”activity, have multiple types of white hats each labeled to demonstrate that there are differenttypes of nodes within a network and discuss the role of each. Another example is expanding the“Sorting Algorithms with Paper Bags” to cover more complex sorting algorithms and morecomplex data structures. The “Linked List with Yarn and Paper Bags” could be easily beextended to cover not only doubly linked lists but circular linked lists as well. The author choseto focused early iterations of using these
and independent study courses wereexcluded. In spring 2016, there was a total of 1111 students in the sampling frame. A sample sizeof 10 % of this population was considered to have sufficient statistical power to derive theresults.The stratified random sampling method was used to select the participants with the strata basedon course level (e.g. 100, 200, 300 & 400-level courses). The sample was randomly selectedacross the four strata so as to be proportional to the number of students enrolled in each stratum(course level). Table 1 shows the percentage of students selected from each course level resultingin the sample size of 111. Table 1. Participant Sampling Plan Course Number of
describes a mobile robot course in which students program basicbehaviors, e.g. wall following or obstacle avoidance. Berry also discusses possibilities forincluding higher level AI concepts and computer vision in future iterations of the course.Team teaching a coordinated deep learning course with a robotics course would requiresignificant planning and student cooperation between courses, but would likely have many of theadvantages described in the previous paragraph. Additional benefits to faculty could includeenhanced (multidisciplinary) collaboration and junior faculty mentorship 12 .A new generation of hardware like NVIDIA’s Jetson mobile deep learning card, combined withtransfer learning, could ease the burden of incorporating deep learning
architecture, he developed the first algorithm that allowed rendering arbitrary three-dimensional polygonal shapes for haptic interfaces (force-feedback human-computer interfaces). He holds 6 patents. c American Society for Engineering Education, 2017 Measuring revealed student scheduling preferences using constrained discrete choice modelsAbstractFor constrained student resources with large student populations it is often necessary toimplement some form of reservation or scheduling system. Examples of scheduled-accessresources can include one-on-one tutoring, machine shops or labs, and computer-based testingfacilities. For planning and resource scheduling purposes it is important to be
reducing the complexity for end users.In the next phase of this project, we plan to conduct a usability study with current practitioners todetermine whether the tablet interface is useful to a practitioner during a therapy session. Basedon this feedback, we will modify the tablet interface, and add more behaviors to the NAO and tothe app to broaden its applicability to more patients.References[1] SoftBank Robotics, "Discover Nao, the little humanoid robot from Aldebaran | Aldebaran," [Online]. Available: https://www.ald.softbankrobotics.com/en/cool-robots/nao. [Accessed 5 July 2016].[2] SoftBank Robotics, "Choregraphe User Guide," [Online]. Available: http://doc.aldebaran.com/2-1/software/choregraphe/index.html. [Accessed 5 July 2016].[3
MIST Space Vehicle Mission Planning Laboratory at the University of Maryland Eastern Shore. In 2010, he joined Eastern Michigan University as an Associate Dean in the College of Technology and currently is a Professor in the School of Engineer- ing Technology. He has an extensive experience in curriculum and laboratory design and development. Dr. Eydgahi has served as a member of the Board of Directors for Tau Alpha Pi, as a member of Advi- sory and Editorial boards for many International Journals in Engineering and Technology, as a member of review panel for NASA and Department of Education, as a regional and chapter chairman of IEEE, SME, and ASEE, and as a session chair and as a member of scientific and
identifier or expression not found”, “UndeclaredVariables”, and “Pointer error”. The first two errors can be summarized as “students writingcodes without a plan” [16]. The third error is the gap between understanding pointers andtheir types. It is observed that some students tried to cast types of understanding the implica-tions. Their programs could pass compilation but had run-time failures. Compilation Error Percentage of total errors Expected identifier or expression not found 23.5% Undeclared Variables 20.8% Pointer error 17.2% Not used Variables
ofinstructional contents is planned in our future work. References 1. Almatrafi, O., Khondkar, I., & Aditya, J. (2015). An Empirical Study of Face-to-Face and Distance Learning Sections of a Core Telecommunication course. American Sociaty of Engineering Education. Seatle, WA. 2. Li, P., Jones, J. M., & Augustus, K. K. (2011). Incorporating Virtual Lab Automation Systems in IT Education. American Society for Engineering Education. 3. May, D., Terkowsky, C., & Ortelt, T. R. (2016). Using and Evaluating Remote Labs in Transnational Online Courses for Mechanical Engineering Students. American Society for Engineering Education. 4. Saliah-Hassane, H., Saad, M., Ofosu, W. K., djibo, k., Mayaki, H. A., & Amadou
Environment (XSEDE) Conference in Atlanta,Georgia. The 2015 cohort participated in the student program at XSEDE15 Conference, in St.Louis, Missouri, in the 2015 NC/SC REU Site Mini-Symposium in Charleston, South Carolina,and presented their research projects to incoming freshmen to encourage them to consider addinga research experience to their academic plans. These opportunities took place as part of theVisREU Experience, rather than after completion of the program—another unique feature of the2014/2015 VisREU Experience.Survey Research Instrument The A La Carte Student Survey Toolkit [27] is used to collect and report evaluation datafrom the VisREU Site. Survey instrument scales correspond to recommended indicators found tobe common among