Briana Lowe Wellman

Alice and robotics in introductory CS courses

Statistics for underrepresented minority groups and women continue to show low numbers in enrollment and rates of retention in academic computer science programs. A new approach to increase student interest in computer science in a first year program is introduced. Laboratory modules for an introductory programming course have been developed at the University of Alabama with the goal to increase student motivation and understanding of fundamental programming concepts. The course utilizes robots and Alice, a 3D graphical programming environment. The drag and drop interface of Alice allows students to program real robots using instructions that correspond to statements of programming languages such as Java, C++, and C#. Students gain programming experience that is transferable to upper level courses by engaging in a stimulating and less frustrating environment using Alice interfaced with robots.

Using rendezvous to overcome communication limitations in multirobot exploration

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.

Using simulation to predict multi-robot performance on coverage tasks

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.

Observation-based cooperation in mobile sensor networks: A bio-inspired approach for fault tolerant coverage

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.

Categorizing interference in real robot experiments

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.