To deal with a shortage of computer scientists, industry is challenging the traditional view that students need a deep theoretical knowledge of computer systems. Some believe that time spent on assignments that do not reflect technical/development practices would be better used building practical skills. This is at odds with the pedagogical strategy of building mental models through students' implementation of tools and libraries that would otherwise be used as off-the-shelf components. Although project-based learning is a well-studied area, there is little to no literature that studies the link between these programming activities and the target cognitive processes. Researchers must understand the advantages and disadvantages of these types of projects in building the correct mental models and any transfer learning it enables. In this Birds of a Feather, we seek to create a working group of academics and professionals that are interested in an evidence-based understanding rather than reliance upon anecdotes. This group will look at the strategic implementation projects and how well they achieve their linked learning outcomes. This working group will discuss how to empirically define the value of these activities and gain a better understanding of how they affect a student's cognitive processes.

New trends in computer architecture include non-general purpose architectures, brain-inspired design, agile hardware development and environmentally responsible design. Computer Science students need to understand computer architecture to develop programs that can achieve high performance through a programmer's awareness of parallelism and latency. In a re-visit of curriculum guidelines, students could develop skills and competencies in less mature yet cutting-edge topics, including quantum computing. For example, should students understand and appreciate quantum processor design's components and characteristics? In a task to revise curricular guidelines, one faces the decision of which topics may be obsolete and should be dropped or modified. Equally important is deciding the set of topics that must be included. Such questions are hard to address, particularly for a curriculum intended to remain fresh under a 10-years horizon. For example, the danger of not properly promoting the discussion of quantum processor design in the new ACM/IEEE-CS/AAAI CS202X curricula is the long waiting cycle (15 years+) for any new revision. This BOF will foster the participants' interactions into a debate of whether new curricular guidelines should contain quantum processor design as part of the Architecture and Organization (AR) Knowledge Area.

Homeland safety and security will increasingly depend upon autonomous unmanned vehicles as a method of assessing and maintaining situational awareness. As autonomous team algorithms evolve toward requiring less human intervention, it may be that having an “operator-in-the-loop” becomes the ultimate goal in utilizing autonomous teams for surveillance. However studies have shown that trust plays a factor in how effectively an operator can work with autonomous teammates. In this work, we study mechanisms that look at autonomy as a system and not as the sum of individual actions. First, we conjecture that if the operator understands how the team autonomy is designed that the user would better trust that the system will contribute to the overall goal. Second, we focus on algorithm input criteria as being linked to operator perception and trust. We focus on adding a time-varying spatial projection of areas in the ROI that have been unseen for more than a set duration (STEC). Studies utilize a custom test bed that allows users to interact with a surveillance team to find a target in the region of interest. Results show that while algorithm training had an adverse effect, projecting salient team/surveillance state had a statistically significant impact on trust and did not negatively affect workload or performance. This result may point at a mechanism for improving trust through visualizing states as used in the autonomous algorithm.

Researchers have focused on frameworks and specialized languages that ease the reuse of previously developed software artifacts on new platforms by using domain concepts as a programming API rather than robot specific APIs. This approach relies upon the existence of a customized device driver program that maps the specific device API to the framework-based domain concepts. While this approach works well to isolate device semantics to a generalizable domain model, a customized driver is required to match each device to each framework or language. To that end, it may be worthwhile to explore the science of interfacing devices to frameworks. In this paper, we expand the previously presented RDIS to be more generalizable by redefining RDIS in terms of the robot device and framework domains. We introduce several high-level domain concepts that describe the invariant features of devices while providing parameterization for attributes that vary. We consider relevant threading models and a decomposition that insulates the application interface or framework connection from the robot-specific primitives. In addition to efficiencies that can be gained in providing declarative device to framework connectivity, new approaches in this space could be the basis for more user friendly development environments that use a discovery mechanism for presenting available control and access mechanisms.

In this half-day tutorial, we will introduce the ARTSI "Robotics Roadshow-in-a-Box (RRIB)", a single point resource for those getting started in robotics outreach. The RRIB is a kit which contains robots, software and prepared materials for providing robotics workshops for middle and high school students that focuses on showing computer scientists as problem solvers and not just programmers through activities with a larger context. The RRIB fills a need for materials that are accessible to those who may have limited knowledge of robotics or limited experience in middle school outreach, whether that is undergraduate students or faculty researchers who might have limited outreach experience or preparation time. Laptop Required.

Improving the novice's experience with programming may be key to retaining more students interested in Computer Science. Programming environments is an area that lacks comprehensive study. Some entry-level sequences prefer visual environments while others tout the benefits of command line environments. In either case, there is little more than anecdotal evidence to support either choice. We present a novel categorization of programming tools based on assistive features that predict the usefulness of programming environments for novices. This categorization is supported by a controlled study that measured the differences in engagement, efficiency, comprehension, and usability. We found that the extremes of assistive features are problematic regardless of prior programming experience.

Physical interference between robots happens when robots try to occupy the same region. In multi-robot experiments, interference between robots has been found to negatively impact performance. In addition, simulations do not always accurately predict real robot performance. So, we study the effects of unmodeled factors, such as interference, on the difference between real and simulated experiments. We believe that it is beneficial to understand the categorization of robot interference. Therefore, we examine different forms of interference in real robot experiments in order to set a basis for an interference model in simulation. We show that interference can be broken into categories which have discrete characteristics that affect robot performance differently in various environments.

Surveillance is an essential part of any security operation, with cameras playing an important role. Often these cameras are controlled by a single individual, but this can lead to inefficient surveillance that leaves areas uncovered and misses targets. We propose a constraint satisfaction problem (CSP) solution to surveilling an area that maximizes the area seen in a region of interest (ROI), minimizes the time individual cells in the ROI goes unseen, and prioritizes targets for tracking. Cells in the ROI are weighted based on the time since they were last seen and whether or not a target is predicted to be in the cell. A virtual environment with cameras and targets is surveilled based on various parameters affecting camera actions. Several metrics are used to measure the performance of each set of parameters, including cell time unseen, average linear uncovered length (ALUL), and time a target is seen with results compared to another surveillance algorithm. The CSP was able to view all cells while greatly increasing the target tracking ability of rarely viewed targets at the cost of slightly fewer viewing steps for the more often viewed targets.

In cooperative multirobot systems, communication can speed up completion, reduce redundancy, and prevent interference between robots. Typically, wireless point-to-point communication is used to coordinate robots. However, environmental interference, unpredictable network conditions, and distances between robots can affect the reliability of wireless communication. Therefore, approaches other than continuous message passing throughout exploration are useful. We consider the problem of coordinating a multirobot system to explore an unknown, large, open environment. An approach that uses sector search with rendezvous is presented. Robots explore an environment in sectors, or designated areas, and periodically meet to communicate map information of what they have explored. Our approach is compared to other communication paradigms in simulation. Results suggest that sector search with rendezvous is more efficient than having no communications. It further demonstrates advantages over scenarios in which robots communicate only with other robots in close proximity, and is comparable to a role-based approach with dynamic team hierarchies.

Simulations are typically used to model a problem and find a solution before real world testing. They speed up the validation process and allow researchers to modify their code accordingly. However, a problem occurs when simulation results are not consistent with real world results. Researchers have found inconsistencies due to odometry error and team size. However, no research has studied the effects specific to robot teams that affect the realism of multi-robot experiments. This paper shows how simulation results vary from experimental results when conducting multi-robot experiments. Simulation and real experiments are performed using different environments and cooperation paradigms. Results show that specific environmental features and cooperation paradigms significantly affect the usefulness of simulated results when predicting performance of real robot teams.

Designing a robot controller that can optimally manage limited resources in a deterministic, real-time manner can be challenging. Behavior-based architectures, which split autonomy into levels, are very popular but neither have real-time features that enforce timing constraints nor support determinism. Even though real-time features are not included, it seems like a natural fit to make each level in the behavior-based architecture its own task or process. The only that thing that it lacks are the timing features. This has already been implemented using Suns Java Real-Time System. It has also been shown that timing constraints effect performance. This brings us to the question; why not use the more traditional language of C or C++ to implement this behavior-based real-time architecture? Are we not taught that Java is useful but slow compared to C and C++? If so why not use C++ and the features of Open Robot Control Software (OROCOS) to implement the architecture. This paper answers the question of does it really matter what language is used in a behavior-based real-time architecture. We implemented the architecture using OROCOS/C++ running on UBUNTU. Then compared our implementation to two other implementations of the architecture: Java/Player on Fedora; and Suns Java Real-Time System (RTS) on Solaris. Results, from experiments on a robot, show that our OROCOS/C++ implementation performed similarly to the Java RTS implementation. Both the OROCOS and Java RTS implementations performed better than the Player/Java implementation. This suggests that Java is in fact feasible for a behavior-based real-time robot architecture but it needs to be run using Java RTS not the regular version.

Involving humans to monitor complex areas using a network of cameras is expensive and prone to errors and fatigue. Hence arises the need for unmanned, cooperative camera systems that can identify targets with intelligent camera motions. Acquiring the targets requires panning of the cameras in a manner that minimizes the time and the contiguous space that goes unmonitored. Average Linear Uncovered Length (ALUL) quantifies the effect of a specific unmonitored area by computing the average length of all lines drawn through the area. This paper considers ALUL as a metric that indicates the target acquisition ability of a particular system configuration. When used in conjunction with temporal constraints, we can intelligently automate camera coverage to improve target acquisition and tracking performance of the system. The results from the experiments confirm that the coverage in constrained environments when the existing camera configuration cannot view large portions of the region of our interest improves when ALUL is considered.

We consider the problem of dispersing nodes of a mobile sensor network to cover an unknown environment. Approaches that use observation to infer state and intent to disperse robot nodes are presented. Nodes use their observations to decide on their next actions. With simulated and physical node experiments, we compare observation-based approaches to no communications, direct communications, and potential field approaches. Experimental results show that using observation to infer state and intent to disperse nodes performs better than non-communicative and potential field based cooperation and in some cases direct communications.

In this paper, we propose an extension to existing mobile robot development environments that explicitly declares the frequency requirements for client controller threads. This extension enables better use of robot resources by running modules only as fast as needed. Developers are forced to consider the frequency requirements and their impacts, which should result in better code. Physical experiments employed a K-team Koala robot. Preliminary results showing the effect of explicitly defining frequency are also presented.

The eROSI (educational, research-oriented, sensing, inexpensive) robot is a robotic platform designed by the Center for Distributed Robotics at the University of Minnesota. It was designed for two purposes; an educational platform and a distributed robotics platform. These two purposes require very different and oftentimes contradictory abilities. As an educational platform, it must be easy to use and program for. It must also be non-intimidating to students who may not have much experience with robotics. As a research platform, it must be powerful enough to run useful behaviors, and expandable with new sensors. These two different purposes have driven the design of the eROSI robot through three generations of development. The first two generations have been tested and evaluated in terms of its two intended purposes. The feedback generated was then used to modify the design of the robot to better accommodate its intended purposes. The eROSI robot is still an ongoing project and aims to create a better platform for both research and education.

Many researchers study the search of unknown areas with teams of cooperating robots. Robot cooperation often relies upon shared representations built from shared information. When robots share information, they inadvertently share the error associated with that information. In this research, we realistically assume that all position-based data has some amount of localization error. We model the propagation of this error in cooperative search. We conjecture that propagation of error differs based on whether search targets are shared or locally discovered. Search errors due to inaccurate position estimates are quantified and compared in both approaches via simulations.

Multirobot coordination techniques range from sharing state and intent information to explicit coordination strategies. The choice of cooperation strategy can affect task performance and resource utilization. This paper investigates the use of local search queues, rather than globally shared search queues. This work shows this change improves search in terms of interference and communications loads, two key factors in multirobot search, without significantly impacting time-to-search and coverage metrics.

Women and minorities are underrepresented in the IT field at the high school, university, and industry levels. Efforts to address this imbalance are often too late to solve underlying problems such as perceived ineptitude and actual inexperience. By designing and hosting a program for these underrepresented students in the middle grades, the Center for Distributed Robotics at the University of Minnesota hopes to establish a successful annual robotics day camp which would inspire both women and minorities to pursue careers in technology. Detailed accounts of the goals and methodology are provided. Initial survey results reveal a very positive response from the campers as well as strengths and weaknesses which would be useful in designing or refining similar camps

This article assesses the performance of a virtual pheromone-based sectored dispersion algorithm which is implemented on a low-cost miniature mobile robotic platform using sonar sensing. The approach is a distributed one and the robotic platform and sensor array are sufficiently inexpensive that they can be employed in undergraduate research and/or for coursework in a small college environment

The Scout is a miniature robot capable of performing surveillance and reconnaissance applications. However, the small size of the Scout limits the on-board processing capability and thus requires that the primary sensing modality (vision) be processed remotely. This creates a dependence upon a video channel which can become a severe bottleneck when attempting to use large numbers of robots. This paper discusses the newest generation of the Scout, the COTS 3.0, which attempts to reduce the effect of the communication bottleneck by using more local sensing capabilities to achieve a degree of autonomous functionality. Several experiments showing the durability of the new COTS Scout and the autonomous capability are presented.